Optimization and approximation

CERN Document Server

Pedregal, Pablo

2017-01-01

This book provides a basic, initial resource, introducing science and engineering students to the field of optimization. It covers three main areas: mathematical programming, calculus of variations and optimal control, highlighting the ideas and concepts and offering insights into the importance of optimality conditions in each area. It also systematically presents affordable approximation methods. Exercises at various levels have been included to support the learning process.

Sequential optimization of matrix chain multiplication relative to different cost functions

KAUST Repository

Chikalov, Igor; Hussain, Shahid; Moshkov, Mikhail

2011-01-01

In this paper, we present a methodology to optimize matrix chain multiplication sequentially relative to different cost functions such as total number of scalar multiplications, communication overhead in a multiprocessor environment, etc. For n matrices our optimization procedure requires O(n 3) arithmetic operations per one cost function. This work is done in the framework of a dynamic programming extension that allows sequential optimization relative to different criteria. © 2011 Springer-Verlag Berlin Heidelberg.

Optimal Sequential Resource Sharing and Exchange in Multi-Agent Systems

OpenAIRE

Xiao, Yuanzhang

2014-01-01

Central to the design of many engineering systems and social networks is to solve the underlying resource sharing and exchange problems, in which multiple decentralized agents make sequential decisions over time to optimize some long-term performance metrics. It is challenging for the decentralized agents to make optimal sequential decisions because of the complicated coupling among the agents and across time. In this dissertation, we mainly focus on three important classes of multi-agent seq...

A Bayesian Optimal Design for Sequential Accelerated Degradation Testing

Directory of Open Access Journals (Sweden)

Xiaoyang Li

2017-07-01

Full Text Available When optimizing an accelerated degradation testing (ADT plan, the initial values of unknown model parameters must be pre-specified. However, it is usually difficult to obtain the exact values, since many uncertainties are embedded in these parameters. Bayesian ADT optimal design was presented to address this problem by using prior distributions to capture these uncertainties. Nevertheless, when the difference between a prior distribution and actual situation is large, the existing Bayesian optimal design might cause some over-testing or under-testing issues. For example, the implemented ADT following the optimal ADT plan consumes too much testing resources or few accelerated degradation data are obtained during the ADT. To overcome these obstacles, a Bayesian sequential step-down-stress ADT design is proposed in this article. During the sequential ADT, the test under the highest stress level is firstly conducted based on the initial prior information to quickly generate degradation data. Then, the data collected under higher stress levels are employed to construct the prior distributions for the test design under lower stress levels by using the Bayesian inference. In the process of optimization, the inverse Gaussian (IG process is assumed to describe the degradation paths, and the Bayesian D-optimality is selected as the optimal objective. A case study on an electrical connector’s ADT plan is provided to illustrate the application of the proposed Bayesian sequential ADT design method. Compared with the results from a typical static Bayesian ADT plan, the proposed design could guarantee more stable and precise estimations of different reliability measures.

Shearlets and Optimally Sparse Approximations

DEFF Research Database (Denmark)

Kutyniok, Gitta; Lemvig, Jakob; Lim, Wang-Q

2012-01-01

Multivariate functions are typically governed by anisotropic features such as edges in images or shock fronts in solutions of transport-dominated equations. One major goal both for the purpose of compression as well as for an efficient analysis is the provision of optimally sparse approximations...... optimally sparse approximations of this model class in 2D as well as 3D. Even more, in contrast to all other directional representation systems, a theory for compactly supported shearlet frames was derived which moreover also satisfy this optimality benchmark. This chapter shall serve as an introduction...... to and a survey about sparse approximations of cartoon-like images by band-limited and also compactly supported shearlet frames as well as a reference for the state-of-the-art of this research field....

Dynamic programming approach to optimization of approximate decision rules

KAUST Repository

Amin, Talha

2013-02-01

This paper is devoted to the study of an extension of dynamic programming approach which allows sequential optimization of approximate decision rules relative to the length and coverage. We introduce an uncertainty measure R(T) which is the number of unordered pairs of rows with different decisions in the decision table T. For a nonnegative real number β, we consider β-decision rules that localize rows in subtables of T with uncertainty at most β. Our algorithm constructs a directed acyclic graph Δβ(T) which nodes are subtables of the decision table T given by systems of equations of the kind "attribute = value". This algorithm finishes the partitioning of a subtable when its uncertainty is at most β. The graph Δβ(T) allows us to describe the whole set of so-called irredundant β-decision rules. We can describe all irredundant β-decision rules with minimum length, and after that among these rules describe all rules with maximum coverage. We can also change the order of optimization. The consideration of irredundant rules only does not change the results of optimization. This paper contains also results of experiments with decision tables from UCI Machine Learning Repository. © 2012 Elsevier Inc. All rights reserved.

Approximate Reanalysis in Topology Optimization

DEFF Research Database (Denmark)

Amir, Oded; Bendsøe, Martin P.; Sigmund, Ole

2009-01-01

In the nested approach to structural optimization, most of the computational effort is invested in the solution of the finite element analysis equations. In this study, the integration of an approximate reanalysis procedure into the framework of topology optimization of continuum structures...

Approximative solutions of stochastic optimization problem

Czech Academy of Sciences Publication Activity Database

Lachout, Petr

2010-01-01

Roč. 46, č. 3 (2010), s. 513-523 ISSN 0023-5954 R&D Projects: GA ČR GA201/08/0539 Institutional research plan: CEZ:AV0Z10750506 Keywords : Stochastic optimization problem * sensitivity * approximative solution Subject RIV: BA - General Mathematics Impact factor: 0.461, year: 2010 http://library.utia.cas.cz/separaty/2010/SI/lachout-approximative solutions of stochastic optimization problem.pdf

Classifiers based on optimal decision rules

KAUST Repository

Amin, Talha

2013-11-25

Based on dynamic programming approach we design algorithms for sequential optimization of exact and approximate decision rules relative to the length and coverage [3, 4]. In this paper, we use optimal rules to construct classifiers, and study two questions: (i) which rules are better from the point of view of classification-exact or approximate; and (ii) which order of optimization gives better results of classifier work: length, length+coverage, coverage, or coverage+length. Experimental results show that, on average, classifiers based on exact rules are better than classifiers based on approximate rules, and sequential optimization (length+coverage or coverage+length) is better than the ordinary optimization (length or coverage).

Classifiers based on optimal decision rules

KAUST Repository

Amin, Talha M.; Chikalov, Igor; Moshkov, Mikhail; Zielosko, Beata

2013-01-01

Based on dynamic programming approach we design algorithms for sequential optimization of exact and approximate decision rules relative to the length and coverage [3, 4]. In this paper, we use optimal rules to construct classifiers, and study two questions: (i) which rules are better from the point of view of classification-exact or approximate; and (ii) which order of optimization gives better results of classifier work: length, length+coverage, coverage, or coverage+length. Experimental results show that, on average, classifiers based on exact rules are better than classifiers based on approximate rules, and sequential optimization (length+coverage or coverage+length) is better than the ordinary optimization (length or coverage).

Constrained Optimization via Stochastic approximation with a simultaneous perturbation gradient approximation

DEFF Research Database (Denmark)

Sadegh, Payman

1997-01-01

This paper deals with a projection algorithm for stochastic approximation using simultaneous perturbation gradient approximation for optimization under inequality constraints where no direct gradient of the loss function is available and the inequality constraints are given as explicit functions...... of the optimization parameters. It is shown that, under application of the projection algorithm, the parameter iterate converges almost surely to a Kuhn-Tucker point, The procedure is illustrated by a numerical example, (C) 1997 Elsevier Science Ltd....

Development of New Lipid-Based Paclitaxel Nanoparticles Using Sequential Simplex Optimization

Science.gov (United States)

Dong, Xiaowei; Mattingly, Cynthia A.; Tseng, Michael; Cho, Moo; Adams, Val R.; Mumper, Russell J.

2008-01-01

The objective of these studies was to develop Cremophor-free lipid-based paclitaxel (PX) nanoparticle formulations prepared from warm microemulsion precursors. To identify and optimize new nanoparticles, experimental design was performed combining Taguchi array and sequential simplex optimization. The combination of Taguchi array and sequential simplex optimization efficiently directed the design of paclitaxel nanoparticles. Two optimized paclitaxel nanoparticles (NPs) were obtained: G78 NPs composed of glyceryl tridodecanoate (GT) and polyoxyethylene 20-stearyl ether (Brij 78), and BTM NPs composed of Miglyol 812, Brij 78 and D-alpha-tocopheryl polyethylene glycol 1000 succinate (TPGS). Both nanoparticles successfully entrapped paclitaxel at a final concentration of 150 μg/ml (over 6% drug loading) with particle sizes less than 200 nm and over 85% of entrapment efficiency. These novel paclitaxel nanoparticles were stable at 4°C over three months and in PBS at 37°C over 102 hours as measured by physical stability. Release of paclitaxel was slow and sustained without initial burst release. Cytotoxicity studies in MDA-MB-231 cancer cells showed that both nanoparticles have similar anticancer activities compared to Taxol®. Interestingly, PX BTM nanocapsules could be lyophilized without cryoprotectants. The lyophilized powder comprised only of PX BTM NPs in water could be rapidly rehydrated with complete retention of original physicochemical properties, in-vitro release properties, and cytotoxicity profile. Sequential Simplex Optimization has been utilized to identify promising new lipid-based paclitaxel nanoparticles having useful attributes. PMID:19111929

Optimal Sequential Diagnostic Strategy Generation Considering Test Placement Cost for Multimode Systems

Directory of Open Access Journals (Sweden)

Shigang Zhang

2015-10-01

Full Text Available Sequential fault diagnosis is an approach that realizes fault isolation by executing the optimal test step by step. The strategy used, i.e., the sequential diagnostic strategy, has great influence on diagnostic accuracy and cost. Optimal sequential diagnostic strategy generation is an important step in the process of diagnosis system construction, which has been studied extensively in the literature. However, previous algorithms either are designed for single mode systems or do not consider test placement cost. They are not suitable to solve the sequential diagnostic strategy generation problem considering test placement cost for multimode systems. Therefore, this problem is studied in this paper. A formulation is presented. Two algorithms are proposed, one of which is realized by system transformation and the other is newly designed. Extensive simulations are carried out to test the effectiveness of the algorithms. A real-world system is also presented. All the results show that both of them have the ability to solve the diagnostic strategy generation problem, and they have different characteristics.

Optimal Sequential Diagnostic Strategy Generation Considering Test Placement Cost for Multimode Systems

Science.gov (United States)

Zhang, Shigang; Song, Lijun; Zhang, Wei; Hu, Zheng; Yang, Yongmin

2015-01-01

Sequential fault diagnosis is an approach that realizes fault isolation by executing the optimal test step by step. The strategy used, i.e., the sequential diagnostic strategy, has great influence on diagnostic accuracy and cost. Optimal sequential diagnostic strategy generation is an important step in the process of diagnosis system construction, which has been studied extensively in the literature. However, previous algorithms either are designed for single mode systems or do not consider test placement cost. They are not suitable to solve the sequential diagnostic strategy generation problem considering test placement cost for multimode systems. Therefore, this problem is studied in this paper. A formulation is presented. Two algorithms are proposed, one of which is realized by system transformation and the other is newly designed. Extensive simulations are carried out to test the effectiveness of the algorithms. A real-world system is also presented. All the results show that both of them have the ability to solve the diagnostic strategy generation problem, and they have different characteristics. PMID:26457709

Fast regularizing sequential subspace optimization in Banach spaces

International Nuclear Information System (INIS)

Schöpfer, F; Schuster, T

2009-01-01

We are concerned with fast computations of regularized solutions of linear operator equations in Banach spaces in case only noisy data are available. To this end we modify recently developed sequential subspace optimization methods in such a way that the therein employed Bregman projections onto hyperplanes are replaced by Bregman projections onto stripes whose width is in the order of the noise level

astroABC : An Approximate Bayesian Computation Sequential Monte Carlo sampler for cosmological parameter estimation

Energy Technology Data Exchange (ETDEWEB)

Jennings, E.; Madigan, M.

2017-04-01

Given the complexity of modern cosmological parameter inference where we arefaced with non-Gaussian data and noise, correlated systematics and multi-probecorrelated data sets, the Approximate Bayesian Computation (ABC) method is apromising alternative to traditional Markov Chain Monte Carlo approaches in thecase where the Likelihood is intractable or unknown. The ABC method is called"Likelihood free" as it avoids explicit evaluation of the Likelihood by using aforward model simulation of the data which can include systematics. Weintroduce astroABC, an open source ABC Sequential Monte Carlo (SMC) sampler forparameter estimation. A key challenge in astrophysics is the efficient use oflarge multi-probe datasets to constrain high dimensional, possibly correlatedparameter spaces. With this in mind astroABC allows for massive parallelizationusing MPI, a framework that handles spawning of jobs across multiple nodes. Akey new feature of astroABC is the ability to create MPI groups with differentcommunicators, one for the sampler and several others for the forward modelsimulation, which speeds up sampling time considerably. For smaller jobs thePython multiprocessing option is also available. Other key features include: aSequential Monte Carlo sampler, a method for iteratively adapting tolerancelevels, local covariance estimate using scikit-learn's KDTree, modules forspecifying optimal covariance matrix for a component-wise or multivariatenormal perturbation kernel, output and restart files are backed up everyiteration, user defined metric and simulation methods, a module for specifyingheterogeneous parameter priors including non-standard prior PDFs, a module forspecifying a constant, linear, log or exponential tolerance level,well-documented examples and sample scripts. This code is hosted online athttps://github.com/EliseJ/astroABC

Optimal random perturbations for stochastic approximation using a simultaneous perturbation gradient approximation

DEFF Research Database (Denmark)

Sadegh, Payman; Spall, J. C.

1998-01-01

simultaneous perturbation approximation to the gradient based on loss function measurements. SPSA is based on picking a simultaneous perturbation (random) vector in a Monte Carlo fashion as part of generating the approximation to the gradient. This paper derives the optimal distribution for the Monte Carlo...

Sequential Optimization of Paths in Directed Graphs Relative to Different Cost Functions

KAUST Repository

Abubeker, Jewahir Ali

2011-05-14

This paper is devoted to the consideration of an algorithm for sequential optimization of paths in directed graphs relative to di_erent cost functions. The considered algorithm is based on an extension of dynamic programming which allows to represent the initial set of paths and the set of optimal paths after each application of optimization procedure in the form of a directed acyclic graph.

A Sequential Convex Semidefinite Programming Algorithm for Multiple-Load Free Material Optimization

Czech Academy of Sciences Publication Activity Database

Stingl, M.; Kočvara, Michal; Leugering, G.

2009-01-01

Roč. 20, č. 1 (2009), s. 130-155 ISSN 1052-6234 R&D Projects: GA AV ČR IAA1075402 Grant - others:commision EU(XE) EU-FP6-30717 Institutional research plan: CEZ:AV0Z10750506 Keywords : structural optimization * material optimization * semidefinite programming * sequential convex programming Subject RIV: BA - General Mathematics Impact factor: 1.429, year: 2009

Sequential Optimization of Paths in Directed Graphs Relative to Different Cost Functions

KAUST Repository

Abubeker, Jewahir Ali; Chikalov, Igor; Hussain, Shahid; Moshkov, Mikhail

2011-01-01

This paper is devoted to the consideration of an algorithm for sequential optimization of paths in directed graphs relative to di_erent cost functions. The considered algorithm is based on an extension of dynamic programming which allows

Nonlinear analysis approximation theory, optimization and applications

CERN Document Server

2014-01-01

Many of our daily-life problems can be written in the form of an optimization problem. Therefore, solution methods are needed to solve such problems. Due to the complexity of the problems, it is not always easy to find the exact solution. However, approximate solutions can be found. The theory of the best approximation is applicable in a variety of problems arising in nonlinear functional analysis and optimization. This book highlights interesting aspects of nonlinear analysis and optimization together with many applications in the areas of physical and social sciences including engineering. It is immensely helpful for young graduates and researchers who are pursuing research in this field, as it provides abundant research resources for researchers and post-doctoral fellows. This will be a valuable addition to the library of anyone who works in the field of applied mathematics, economics and engineering.

OPTIMIZATION OF AGGREGATION AND SEQUENTIAL-PARALLEL EXECUTION MODES OF INTERSECTING OPERATION SETS

Directory of Open Access Journals (Sweden)

G. М. Levin

2016-01-01

Full Text Available A mathematical model and a method for the problem of optimization of aggregation and of sequential- parallel execution modes of intersecting operation sets are proposed. The proposed method is based on the two-level decomposition scheme. At the top level the variant of aggregation for groups of operations is selected, and at the lower level the execution modes of operations are optimized for a fixed version of aggregation.

Galerkin approximations of nonlinear optimal control problems in Hilbert spaces

Directory of Open Access Journals (Sweden)

Mickael D. Chekroun

2017-07-01

Full Text Available Nonlinear optimal control problems in Hilbert spaces are considered for which we derive approximation theorems for Galerkin approximations. Approximation theorems are available in the literature. The originality of our approach relies on the identification of a set of natural assumptions that allows us to deal with a broad class of nonlinear evolution equations and cost functionals for which we derive convergence of the value functions associated with the optimal control problem of the Galerkin approximations. This convergence result holds for a broad class of nonlinear control strategies as well. In particular, we show that the framework applies to the optimal control of semilinear heat equations posed on a general compact manifold without boundary. The framework is then shown to apply to geoengineering and mitigation of greenhouse gas emissions formulated here in terms of optimal control of energy balance climate models posed on the sphere $\\mathbb{S}^2$.

Fifth International Conference on "Approximation and Optimization in the Caribbean"

CERN Document Server

Approximation, Optimization and Mathematical Economic

2001-01-01

The articles in this proceedings volume reflect the current trends in the theory of approximation, optimization and mathematical economics, and include numerous applications. The book will be of interest to researchers and graduate students involved in functional analysis, approximation theory, mathematical programming and optimization, game theory, mathematical finance and economics.

A Sequential Optimization Sampling Method for Metamodels with Radial Basis Functions

Science.gov (United States)

Pan, Guang; Ye, Pengcheng; Yang, Zhidong

2014-01-01

Metamodels have been widely used in engineering design to facilitate analysis and optimization of complex systems that involve computationally expensive simulation programs. The accuracy of metamodels is strongly affected by the sampling methods. In this paper, a new sequential optimization sampling method is proposed. Based on the new sampling method, metamodels can be constructed repeatedly through the addition of sampling points, namely, extrema points of metamodels and minimum points of density function. Afterwards, the more accurate metamodels would be constructed by the procedure above. The validity and effectiveness of proposed sampling method are examined by studying typical numerical examples. PMID:25133206

Local Approximation and Hierarchical Methods for Stochastic Optimization

Science.gov (United States)

Cheng, Bolong

In this thesis, we present local and hierarchical approximation methods for two classes of stochastic optimization problems: optimal learning and Markov decision processes. For the optimal learning problem class, we introduce a locally linear model with radial basis function for estimating the posterior mean of the unknown objective function. The method uses a compact representation of the function which avoids storing the entire history, as is typically required by nonparametric methods. We derive a knowledge gradient policy with the locally parametric model, which maximizes the expected value of information. We show the policy is asymptotically optimal in theory, and experimental works suggests that the method can reliably find the optimal solution on a range of test functions. For the Markov decision processes problem class, we are motivated by an application where we want to co-optimize a battery for multiple revenue, in particular energy arbitrage and frequency regulation. The nature of this problem requires the battery to make charging and discharging decisions at different time scales while accounting for the stochastic information such as load demand, electricity prices, and regulation signals. Computing the exact optimal policy becomes intractable due to the large state space and the number of time steps. We propose two methods to circumvent the computation bottleneck. First, we propose a nested MDP model that structure the co-optimization problem into smaller sub-problems with reduced state space. This new model allows us to understand how the battery behaves down to the two-second dynamics (that of the frequency regulation market). Second, we introduce a low-rank value function approximation for backward dynamic programming. This new method only requires computing the exact value function for a small subset of the state space and approximate the entire value function via low-rank matrix completion. We test these methods on historical price data from the

The optimal XFEM approximation for fracture analysis

International Nuclear Information System (INIS)

Jiang Shouyan; Du Chengbin; Ying Zongquan

2010-01-01

The extended finite element method (XFEM) provides an effective tool for analyzing fracture mechanics problems. A XFEM approximation consists of standard finite elements which are used in the major part of the domain and enriched elements in the enriched sub-domain for capturing special solution properties such as discontinuities and singularities. However, two issues in the standard XFEM should specially be concerned: efficient numerical integration methods and an appropriate construction of the blending elements. In the paper, an optimal XFEM approximation is proposed to overcome the disadvantage mentioned above in the standard XFEM. The modified enrichment functions are presented that can reproduced exactly everywhere in the domain. The corresponding FORTRAN program is developed for fracture analysis. A classic problem of fracture mechanics is used to benchmark the program. The results indicate that the optimal XFEM can alleviate the errors and improve numerical precision.

Diversity comparison of Pareto front approximations in many-objective optimization.

Science.gov (United States)

Li, Miqing; Yang, Shengxiang; Liu, Xiaohui

2014-12-01

Diversity assessment of Pareto front approximations is an important issue in the stochastic multiobjective optimization community. Most of the diversity indicators in the literature were designed to work for any number of objectives of Pareto front approximations in principle, but in practice many of these indicators are infeasible or not workable when the number of objectives is large. In this paper, we propose a diversity comparison indicator (DCI) to assess the diversity of Pareto front approximations in many-objective optimization. DCI evaluates relative quality of different Pareto front approximations rather than provides an absolute measure of distribution for a single approximation. In DCI, all the concerned approximations are put into a grid environment so that there are some hyperboxes containing one or more solutions. The proposed indicator only considers the contribution of different approximations to nonempty hyperboxes. Therefore, the computational cost does not increase exponentially with the number of objectives. In fact, the implementation of DCI is of quadratic time complexity, which is fully independent of the number of divisions used in grid. Systematic experiments are conducted using three groups of artificial Pareto front approximations and seven groups of real Pareto front approximations with different numbers of objectives to verify the effectiveness of DCI. Moreover, a comparison with two diversity indicators used widely in many-objective optimization is made analytically and empirically. Finally, a parametric investigation reveals interesting insights of the division number in grid and also offers some suggested settings to the users with different preferences.

Optimization, formulation, and characterization of multiflavonoids-loaded flavanosome by bulk or sequential technique.

Science.gov (United States)

Karthivashan, Govindarajan; Masarudin, Mas Jaffri; Kura, Aminu Umar; Abas, Faridah; Fakurazi, Sharida

2016-01-01

This study involves adaptation of bulk or sequential technique to load multiple flavonoids in a single phytosome, which can be termed as "flavonosome". Three widely established and therapeutically valuable flavonoids, such as quercetin (Q), kaempferol (K), and apigenin (A), were quantified in the ethyl acetate fraction of Moringa oleifera leaves extract and were commercially obtained and incorporated in a single flavonosome (QKA-phosphatidylcholine) through four different methods of synthesis - bulk (M1) and serialized (M2) co-sonication and bulk (M3) and sequential (M4) co-loading. The study also established an optimal formulation method based on screening the synthesized flavonosomes with respect to their size, charge, polydispersity index, morphology, drug-carrier interaction, antioxidant potential through in vitro 1,1-diphenyl-2-picrylhydrazyl kinetics, and cytotoxicity evaluation against human hepatoma cell line (HepaRG). Furthermore, entrapment and loading efficiency of flavonoids in the optimal flavonosome have been identified. Among the four synthesis methods, sequential loading technique has been optimized as the best method for the synthesis of QKA-phosphatidylcholine flavonosome, which revealed an average diameter of 375.93±33.61 nm, with a zeta potential of -39.07±3.55 mV, and the entrapment efficiency was >98% for all the flavonoids, whereas the drug-loading capacity of Q, K, and A was 31.63%±0.17%, 34.51%±2.07%, and 31.79%±0.01%, respectively. The in vitro 1,1-diphenyl-2-picrylhydrazyl kinetics of the flavonoids indirectly depicts the release kinetic behavior of the flavonoids from the carrier. The QKA-loaded flavonosome had no indication of toxicity toward human hepatoma cell line as shown by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide result, wherein even at the higher concentration of 200 µg/mL, the flavonosomes exert >85% of cell viability. These results suggest that sequential loading technique may be a promising

Optimal Piecewise-Linear Approximation of the Quadratic Chaotic Dynamics

Directory of Open Access Journals (Sweden)

J. Petrzela

2012-04-01

Full Text Available This paper shows the influence of piecewise-linear approximation on the global dynamics associated with autonomous third-order dynamical systems with the quadratic vector fields. The novel method for optimal nonlinear function approximation preserving the system behavior is proposed and experimentally verified. This approach is based on the calculation of the state attractor metric dimension inside a stochastic optimization routine. The approximated systems are compared to the original by means of the numerical integration. Real electronic circuits representing individual dynamical systems are derived using classical as well as integrator-based synthesis and verified by time-domain analysis in Orcad Pspice simulator. The universality of the proposed method is briefly discussed, especially from the viewpoint of the higher-order dynamical systems. Future topics and perspectives are also provided

Computation of Stackelberg Equilibria of Finite Sequential Games

DEFF Research Database (Denmark)

Bosanski, Branislav; Branzei, Simina; Hansen, Kristoffer Arnsfelt

2015-01-01

The Stackelberg equilibrium is a solution concept that describes optimal strategies to commit to: Player~1 (the leader) first commits to a strategy that is publicly announced, then Player~2 (the follower) plays a best response to the leader's choice. We study Stackelberg equilibria in finite...... sequential (i.e., extensive-form) games and provide new exact algorithms, approximate algorithms, and hardness results for finding equilibria for several classes of such two-player games....

Aspects of approximate optimisation: overcoming the curse of dimensionality and design of experiments

NARCIS (Netherlands)

Trichon, Sophie; Bonte, M.H.A.; Ponthot, Jean-Philippe; van den Boogaard, Antonius H.

2007-01-01

Coupling optimisation algorithms to Finite Element Methods (FEM) is a very promising way to achieve optimal metal forming processes. However, many optimisation algorithms exist and it is not clear which of these algorithms to use. This paper investigates the sensitivity of a Sequential Approximate

Optimal approximation of linear systems by artificial immune response

Institute of Scientific and Technical Information of China (English)

无

2006-01-01

This paper puts forward a novel artificial immune response algorithm for optimal approximation of linear systems. A quaternion model of artificial immune response is proposed for engineering computing. The model abstracts four elements, namely, antigen, antibody, reaction rules among antibodies, and driving algorithm describing how the rules are applied to antibodies, to simulate the process of immune response. Some reaction rules including clonal selection rules, immunological memory rules and immune regulation rules are introduced. Using the theorem of Markov chain, it is proofed that the new model is convergent. The experimental study on the optimal approximation of a stable linear system and an unstable one show that the approximate models searched by the new model have better performance indices than those obtained by some existing algorithms including the differential evolution algorithm and the multi-agent genetic algorithm.

Truss topology optimization with discrete design variables by outer approximation

DEFF Research Database (Denmark)

Stolpe, Mathias

2015-01-01

Several variants of an outer approximation method are proposed to solve truss topology optimization problems with discrete design variables to proven global optimality. The objective is to minimize the volume of the structure while satisfying constraints on the global stiffness of the structure...... for classical outer approximation approaches applied to optimal design problems. A set of two- and three-dimensional benchmark problems are solved and the numerical results suggest that the proposed approaches are competitive with other special-purpose global optimization methods for the considered class...... under the applied loads. We extend the natural problem formulation by adding redundant force variables and force equilibrium constraints. This guarantees that the designs suggested by the relaxed master problems are capable of carrying the applied loads, a property which is generally not satisfied...

On the effect of response transformations in sequential parameter optimization.

Science.gov (United States)

Wagner, Tobias; Wessing, Simon

2012-01-01

Parameter tuning of evolutionary algorithms (EAs) is attracting more and more interest. In particular, the sequential parameter optimization (SPO) framework for the model-assisted tuning of stochastic optimizers has resulted in established parameter tuning algorithms. In this paper, we enhance the SPO framework by introducing transformation steps before the response aggregation and before the actual modeling. Based on design-of-experiments techniques, we empirically analyze the effect of integrating different transformations. We show that in particular, a rank transformation of the responses provides significant improvements. A deeper analysis of the resulting models and additional experiments with adaptive procedures indicates that the rank and the Box-Cox transformation are able to improve the properties of the resultant distributions with respect to symmetry and normality of the residuals. Moreover, model-based effect plots document a higher discriminatory power obtained by the rank transformation.

Portfolio Optimization under Local-Stochastic Volatility: Coefficient Taylor Series Approximations & Implied Sharpe Ratio

OpenAIRE

Lorig, Matthew; Sircar, Ronnie

2015-01-01

We study the finite horizon Merton portfolio optimization problem in a general local-stochastic volatility setting. Using model coefficient expansion techniques, we derive approximations for the both the value function and the optimal investment strategy. We also analyze the `implied Sharpe ratio' and derive a series approximation for this quantity. The zeroth-order approximation of the value function and optimal investment strategy correspond to those obtained by Merton (1969) when the risky...

Spacecraft attitude control using neuro-fuzzy approximation of the optimal controllers

Science.gov (United States)

Kim, Sung-Woo; Park, Sang-Young; Park, Chandeok

2016-01-01

In this study, a neuro-fuzzy controller (NFC) was developed for spacecraft attitude control to mitigate large computational load of the state-dependent Riccati equation (SDRE) controller. The NFC was developed by training a neuro-fuzzy network to approximate the SDRE controller. The stability of the NFC was numerically verified using a Lyapunov-based method, and the performance of the controller was analyzed in terms of approximation ability, steady-state error, cost, and execution time. The simulations and test results indicate that the developed NFC efficiently approximates the SDRE controller, with asymptotic stability in a bounded region of angular velocity encompassing the operational range of rapid-attitude maneuvers. In addition, it was shown that an approximated optimal feedback controller can be designed successfully through neuro-fuzzy approximation of the optimal open-loop controller.

Discrete-Time Stable Generalized Self-Learning Optimal Control With Approximation Errors.

Science.gov (United States)

Wei, Qinglai; Li, Benkai; Song, Ruizhuo

2018-04-01

In this paper, a generalized policy iteration (GPI) algorithm with approximation errors is developed for solving infinite horizon optimal control problems for nonlinear systems. The developed stable GPI algorithm provides a general structure of discrete-time iterative adaptive dynamic programming algorithms, by which most of the discrete-time reinforcement learning algorithms can be described using the GPI structure. It is for the first time that approximation errors are explicitly considered in the GPI algorithm. The properties of the stable GPI algorithm with approximation errors are analyzed. The admissibility of the approximate iterative control law can be guaranteed if the approximation errors satisfy the admissibility criteria. The convergence of the developed algorithm is established, which shows that the iterative value function is convergent to a finite neighborhood of the optimal performance index function, if the approximate errors satisfy the convergence criterion. Finally, numerical examples and comparisons are presented.

Efficient Approximation of Optimal Control for Markov Games

DEFF Research Database (Denmark)

Fearnley, John; Rabe, Markus; Schewe, Sven

2011-01-01

We study the time-bounded reachability problem for continuous-time Markov decision processes (CTMDPs) and games (CTMGs). Existing techniques for this problem use discretisation techniques to break time into discrete intervals, and optimal control is approximated for each interval separately...

Approximating the Pareto set of multiobjective linear programs via robust optimization

NARCIS (Netherlands)

Gorissen, B.L.; den Hertog, D.

2012-01-01

We consider problems with multiple linear objectives and linear constraints and use adjustable robust optimization and polynomial optimization as tools to approximate the Pareto set with polynomials of arbitrarily large degree. The main difference with existing techniques is that we optimize a

Markdown Optimization via Approximate Dynamic Programming

Directory of Open Access Journals (Sweden)

Cos?gun

2013-02-01

Full Text Available We consider the markdown optimization problem faced by the leading apparel retail chain. Because of substitution among products the markdown policy of one product affects the sales of other products. Therefore, markdown policies for product groups having a significant crossprice elasticity among each other should be jointly determined. Since the state space of the problem is very huge, we use Approximate Dynamic Programming. Finally, we provide insights on the behavior of how each product price affects the markdown policy.

Exploring the sequential lineup advantage using WITNESS.

Science.gov (United States)

Goodsell, Charles A; Gronlund, Scott D; Carlson, Curt A

2010-12-01

Advocates claim that the sequential lineup is an improvement over simultaneous lineup procedures, but no formal (quantitatively specified) explanation exists for why it is better. The computational model WITNESS (Clark, Appl Cogn Psychol 17:629-654, 2003) was used to develop theoretical explanations for the sequential lineup advantage. In its current form, WITNESS produced a sequential advantage only by pairing conservative sequential choosing with liberal simultaneous choosing. However, this combination failed to approximate four extant experiments that exhibited large sequential advantages. Two of these experiments became the focus of our efforts because the data were uncontaminated by likely suspect position effects. Decision-based and memory-based modifications to WITNESS approximated the data and produced a sequential advantage. The next step is to evaluate the proposed explanations and modify public policy recommendations accordingly.

Annealing evolutionary stochastic approximation Monte Carlo for global optimization

KAUST Repository

Liang, Faming

2010-04-08

In this paper, we propose a new algorithm, the so-called annealing evolutionary stochastic approximation Monte Carlo (AESAMC) algorithm as a general optimization technique, and study its convergence. AESAMC possesses a self-adjusting mechanism, whose target distribution can be adapted at each iteration according to the current samples. Thus, AESAMC falls into the class of adaptive Monte Carlo methods. This mechanism also makes AESAMC less trapped by local energy minima than nonadaptive MCMC algorithms. Under mild conditions, we show that AESAMC can converge weakly toward a neighboring set of global minima in the space of energy. AESAMC is tested on multiple optimization problems. The numerical results indicate that AESAMC can potentially outperform simulated annealing, the genetic algorithm, annealing stochastic approximation Monte Carlo, and some other metaheuristics in function optimization. © 2010 Springer Science+Business Media, LLC.

Optimal causal inference: estimating stored information and approximating causal architecture.

Science.gov (United States)

Still, Susanne; Crutchfield, James P; Ellison, Christopher J

2010-09-01

We introduce an approach to inferring the causal architecture of stochastic dynamical systems that extends rate-distortion theory to use causal shielding--a natural principle of learning. We study two distinct cases of causal inference: optimal causal filtering and optimal causal estimation. Filtering corresponds to the ideal case in which the probability distribution of measurement sequences is known, giving a principled method to approximate a system's causal structure at a desired level of representation. We show that in the limit in which a model-complexity constraint is relaxed, filtering finds the exact causal architecture of a stochastic dynamical system, known as the causal-state partition. From this, one can estimate the amount of historical information the process stores. More generally, causal filtering finds a graded model-complexity hierarchy of approximations to the causal architecture. Abrupt changes in the hierarchy, as a function of approximation, capture distinct scales of structural organization. For nonideal cases with finite data, we show how the correct number of the underlying causal states can be found by optimal causal estimation. A previously derived model-complexity control term allows us to correct for the effect of statistical fluctuations in probability estimates and thereby avoid overfitting.

Approximate optimal tracking control for near-surface AUVs with wave disturbances

Science.gov (United States)

Yang, Qing; Su, Hao; Tang, Gongyou

2016-10-01

This paper considers the optimal trajectory tracking control problem for near-surface autonomous underwater vehicles (AUVs) in the presence of wave disturbances. An approximate optimal tracking control (AOTC) approach is proposed. Firstly, a six-degrees-of-freedom (six-DOF) AUV model with its body-fixed coordinate system is decoupled and simplified and then a nonlinear control model of AUVs in the vertical plane is given. Also, an exosystem model of wave disturbances is constructed based on Hirom approximation formula. Secondly, the time-parameterized desired trajectory which is tracked by the AUV's system is represented by the exosystem. Then, the coupled two-point boundary value (TPBV) problem of optimal tracking control for AUVs is derived from the theory of quadratic optimal control. By using a recently developed successive approximation approach to construct sequences, the coupled TPBV problem is transformed into a problem of solving two decoupled linear differential sequences of state vectors and adjoint vectors. By iteratively solving the two equation sequences, the AOTC law is obtained, which consists of a nonlinear optimal feedback item, an expected output tracking item, a feedforward disturbances rejection item, and a nonlinear compensatory term. Furthermore, a wave disturbances observer model is designed in order to solve the physically realizable problem. Simulation is carried out by using the Remote Environmental Unit (REMUS) AUV model to demonstrate the effectiveness of the proposed algorithm.

Annealing evolutionary stochastic approximation Monte Carlo for global optimization

KAUST Repository

Liang, Faming

2010-01-01

outperform simulated annealing, the genetic algorithm, annealing stochastic approximation Monte Carlo, and some other metaheuristics in function optimization. © 2010 Springer Science+Business Media, LLC.

Optimization, formulation, and characterization of multiflavonoids-loaded flavanosome by bulk or sequential technique

Directory of Open Access Journals (Sweden)

Karthivashan G

2016-07-01

Full Text Available Govindarajan Karthivashan,1 Mas Jaffri Masarudin,2 Aminu Umar Kura,1 Faridah Abas,3,4 Sharida Fakurazi1,5 1Laboratory of Vaccines and Immunotherapeutics, Institute of Bioscience, 2Department of Cell and Molecular Biology, Faculty of Biotechnology and Biomolecular Sciences, 3Department of Food Science, Faculty of Food Science and Technology, 4Laboratory of Natural Products, Institute of Bioscience, 5Department of Human Anatomy, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia Abstract: This study involves adaptation of bulk or sequential technique to load multiple flavonoids in a single phytosome, which can be termed as “flavonosome”. Three widely established and therapeutically valuable flavonoids, such as quercetin (Q, kaempferol (K, and apigenin (A, were quantified in the ethyl acetate fraction of Moringa oleifera leaves extract and were commercially obtained and incorporated in a single flavonosome (QKA–phosphatidylcholine through four different methods of synthesis – bulk (M1 and serialized (M2 co-sonication and bulk (M3 and sequential (M4 co-loading. The study also established an optimal formulation method based on screening the synthesized flavonosomes with respect to their size, charge, polydispersity index, morphology, drug–carrier interaction, antioxidant potential through in vitro 1,1-diphenyl-2-picrylhydrazyl kinetics, and cytotoxicity evaluation against human hepatoma cell line (HepaRG. Furthermore, entrapment and loading efficiency of flavonoids in the optimal flavonosome have been identified. Among the four synthesis methods, sequential loading technique has been optimized as the best method for the synthesis of QKA–phosphatidylcholine flavonosome, which revealed an average diameter of 375.93±33.61 nm, with a zeta potential of -39.07±3.55 mV, and the entrapment efficiency was >98% for all the flavonoids, whereas the drug-loading capacity of Q, K, and A was 31.63%±0

Optimal base-stock policy for the inventory system with periodic review, backorders and sequential lead times

DEFF Research Database (Denmark)

Johansen, Søren Glud; Thorstenson, Anders

2008-01-01

We extend well-known formulae for the optimal base stock of the inventory system with continuous review and constant lead time to the case with periodic review and stochastic, sequential lead times. Our extension uses the notion of the 'extended lead time'. The derived performance measures...

Optimal Control via Reinforcement Learning with Symbolic Policy Approximation

NARCIS (Netherlands)

Kubalìk, Jiřì; Alibekov, Eduard; Babuska, R.; Dochain, Denis; Henrion, Didier; Peaucelle, Dimitri

2017-01-01

Model-based reinforcement learning (RL) algorithms can be used to derive optimal control laws for nonlinear dynamic systems. With continuous-valued state and input variables, RL algorithms have to rely on function approximators to represent the value function and policy mappings. This paper

Sparse linear models: Variational approximate inference and Bayesian experimental design

International Nuclear Information System (INIS)

Seeger, Matthias W

2009-01-01

A wide range of problems such as signal reconstruction, denoising, source separation, feature selection, and graphical model search are addressed today by posterior maximization for linear models with sparsity-favouring prior distributions. The Bayesian posterior contains useful information far beyond its mode, which can be used to drive methods for sampling optimization (active learning), feature relevance ranking, or hyperparameter estimation, if only this representation of uncertainty can be approximated in a tractable manner. In this paper, we review recent results for variational sparse inference, and show that they share underlying computational primitives. We discuss how sampling optimization can be implemented as sequential Bayesian experimental design. While there has been tremendous recent activity to develop sparse estimation, little attendance has been given to sparse approximate inference. In this paper, we argue that many problems in practice, such as compressive sensing for real-world image reconstruction, are served much better by proper uncertainty approximations than by ever more aggressive sparse estimation algorithms. Moreover, since some variational inference methods have been given strong convex optimization characterizations recently, theoretical analysis may become possible, promising new insights into nonlinear experimental design.

Sparse linear models: Variational approximate inference and Bayesian experimental design

Energy Technology Data Exchange (ETDEWEB)

Seeger, Matthias W [Saarland University and Max Planck Institute for Informatics, Campus E1.4, 66123 Saarbruecken (Germany)

2009-12-01

A wide range of problems such as signal reconstruction, denoising, source separation, feature selection, and graphical model search are addressed today by posterior maximization for linear models with sparsity-favouring prior distributions. The Bayesian posterior contains useful information far beyond its mode, which can be used to drive methods for sampling optimization (active learning), feature relevance ranking, or hyperparameter estimation, if only this representation of uncertainty can be approximated in a tractable manner. In this paper, we review recent results for variational sparse inference, and show that they share underlying computational primitives. We discuss how sampling optimization can be implemented as sequential Bayesian experimental design. While there has been tremendous recent activity to develop sparse estimation, little attendance has been given to sparse approximate inference. In this paper, we argue that many problems in practice, such as compressive sensing for real-world image reconstruction, are served much better by proper uncertainty approximations than by ever more aggressive sparse estimation algorithms. Moreover, since some variational inference methods have been given strong convex optimization characterizations recently, theoretical analysis may become possible, promising new insights into nonlinear experimental design.

Sequential Optimization of Global Sequence Alignments Relative to Different Cost Functions

KAUST Repository

Odat, Enas M.

2011-05-01

The purpose of this dissertation is to present a methodology to model global sequence alignment problem as directed acyclic graph which helps to extract all possible optimal alignments. Moreover, a mechanism to sequentially optimize sequence alignment problem relative to different cost functions is suggested. Sequence alignment is mostly important in computational biology. It is used to find evolutionary relationships between biological sequences. There are many algo- rithms that have been developed to solve this problem. The most famous algorithms are Needleman-Wunsch and Smith-Waterman that are based on dynamic program- ming. In dynamic programming, problem is divided into a set of overlapping sub- problems and then the solution of each subproblem is found. Finally, the solutions to these subproblems are combined into a final solution. In this thesis it has been proved that for two sequences of length m and n over a fixed alphabet, the suggested optimization procedure requires O(mn) arithmetic operations per cost function on a single processor machine. The algorithm has been simulated using C#.Net programming language and a number of experiments have been done to verify the proved statements. The results of these experiments show that the number of optimal alignments is reduced after each step of optimization. Furthermore, it has been verified that as the sequence length increased linearly then the number of optimal alignments increased exponentially which also depends on the cost function that is used. Finally, the number of executed operations increases polynomially as the sequence length increase linearly.

Note: Optimal base-stock policy for the inventory system with periodic review, backorders and sequential lead times

DEFF Research Database (Denmark)

Johansen, Søren Glud; Thorstenson, Anders

We show that well-known textbook formulae for determining the optimal base stock of the inventory system with continuous review and constant lead time can easily be extended to the case with periodic review and stochastic, sequential lead times. The provided performance measures and conditions...

An Approximate Method for Solving Optimal Control Problems for Discrete Systems Based on Local Approximation of an Attainability Set

Directory of Open Access Journals (Sweden)

V. A. Baturin

2017-03-01

Full Text Available An optimal control problem for discrete systems is considered. A method of successive improvements along with its modernization based on the expansion of the main structures of the core algorithm about the parameter is suggested. The idea of the method is based on local approximation of attainability set, which is described by the zeros of the Bellman function in the special problem of optimal control. The essence of the problem is as follows: from the end point of the phase is required to find a path that minimizes functional deviations of the norm from the initial state. If the initial point belongs to the attainability set of the original controlled system, the value of the Bellman function equal to zero, otherwise the value of the Bellman function is greater than zero. For this special task Bellman equation is considered. The support approximation and Bellman equation are selected. The Bellman function is approximated by quadratic terms. Along the allowable trajectory, this approximation gives nothing, because Bellman function and its expansion coefficients are zero. We used a special trick: an additional variable is introduced, which characterizes the degree of deviation of the system from the initial state, thus it is obtained expanded original chain. For the new variable initial nonzero conditions is selected, thus obtained trajectory is lying outside attainability set and relevant Bellman function is greater than zero, which allows it to hold a non-trivial approximation. As a result of these procedures algorithms of successive improvements is designed. Conditions for relaxation algorithms and conditions for the necessary conditions of optimality are also obtained.

An intrinsic robust rank-one-approximation approach for currencyportfolio optimization

Directory of Open Access Journals (Sweden)

Hongxuan Huang

2018-03-01

Full Text Available A currency portfolio is a special kind of wealth whose value fluctuates with foreignexchange rates over time, which possesses 3Vs (volume, variety and velocity properties of big datain the currency market. In this paper, an intrinsic robust rank one approximation (ROA approachis proposed to maximize the value of currency portfolios over time. The main results of the paperinclude four parts: Firstly, under the assumptions about the currency market, the currency portfoliooptimization problem is formulated as the basic model, in which there are two types of variablesdescribing currency amounts in portfolios and the amount of each currency exchanged into another,respectively. Secondly, the rank one approximation problem and its variants are also formulated toapproximate a foreign exchange rate matrix, whose performance is measured by the Frobenius normor the 2-norm of a residual matrix. The intrinsic robustness of the rank one approximation is provedtogether with summarizing properties of the basic ROA problem and designing a modified powermethod to search for the virtual exchange rates hidden in a foreign exchange rate matrix. Thirdly,a technique for decision variables reduction is presented to attack the currency portfolio optimization.The reduced formulation is referred to as the ROA model, which keeps only variables describingcurrency amounts in portfolios. The optimal solution to the ROA model also induces a feasible solutionto the basic model of the currency portfolio problem by integrating forex operations from the ROAmodel with practical forex rates. Finally, numerical examples are presented to verify the feasibility ande ciency of the intrinsic robust rank one approximation approach. They also indicate that there existsan objective measure for evaluating and optimizing currency portfolios over time, which is related tothe virtual standard currency and independent of any real currency selected specially for measurement.

Improved Genetic Algorithm with Two-Level Approximation for Truss Optimization by Using Discrete Shape Variables

Directory of Open Access Journals (Sweden)

Shen-yan Chen

2015-01-01

Full Text Available This paper presents an Improved Genetic Algorithm with Two-Level Approximation (IGATA to minimize truss weight by simultaneously optimizing size, shape, and topology variables. On the basis of a previously presented truss sizing/topology optimization method based on two-level approximation and genetic algorithm (GA, a new method for adding shape variables is presented, in which the nodal positions are corresponding to a set of coordinate lists. A uniform optimization model including size/shape/topology variables is established. First, a first-level approximate problem is constructed to transform the original implicit problem to an explicit problem. To solve this explicit problem which involves size/shape/topology variables, GA is used to optimize individuals which include discrete topology variables and shape variables. When calculating the fitness value of each member in the current generation, a second-level approximation method is used to optimize the continuous size variables. With the introduction of shape variables, the original optimization algorithm was improved in individual coding strategy as well as GA execution techniques. Meanwhile, the update strategy of the first-level approximation problem was also improved. The results of numerical examples show that the proposed method is effective in dealing with the three kinds of design variables simultaneously, and the required computational cost for structural analysis is quite small.

DiceKriging, DiceOptim: Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization

Directory of Open Access Journals (Sweden)

Olivier Roustant

2012-10-01

Full Text Available We present two recently released R packages, DiceKriging and DiceOptim, for the approximation and the optimization of expensive-to-evaluate deterministic functions. Following a self-contained mini tutorial on Kriging-based approximation and optimization, the functionalities of both packages are detailed and demonstrated in two distinct sections. In particular, the versatility of DiceKriging with respect to trend and noise specifications, covariance parameter estimation, as well as conditional and unconditional simulations are illustrated on the basis of several reproducible numerical experiments. We then put to the fore the implementation of sequential and parallel optimization strategies relying on the expected improvement criterion on the occasion of DiceOptim’s presentation. An appendix is dedicated to complementary mathematical and computational details.

Applying the minimax principle to sequential mastery testing

NARCIS (Netherlands)

Vos, Hendrik J.

2002-01-01

The purpose of this paper is to derive optimal rules for sequential mastery tests. In a sequential mastery test, the decision is to classify a subject as a master, a nonmaster, or to continue sampling and administering another random item. The framework of minimax sequential decision theory (minimum

Quantum approximate optimization algorithm for MaxCut: A fermionic view

Science.gov (United States)

Wang, Zhihui; Hadfield, Stuart; Jiang, Zhang; Rieffel, Eleanor G.

2018-02-01

Farhi et al. recently proposed a class of quantum algorithms, the quantum approximate optimization algorithm (QAOA), for approximately solving combinatorial optimization problems (E. Farhi et al., arXiv:1411.4028; arXiv:1412.6062; arXiv:1602.07674). A level-p QAOA circuit consists of p steps; in each step a classical Hamiltonian, derived from the cost function, is applied followed by a mixing Hamiltonian. The 2 p times for which these two Hamiltonians are applied are the parameters of the algorithm, which are to be optimized classically for the best performance. As p increases, parameter optimization becomes inefficient due to the curse of dimensionality. The success of the QAOA approach will depend, in part, on finding effective parameter-setting strategies. Here we analytically and numerically study parameter setting for the QAOA applied to MaxCut. For the level-1 QAOA, we derive an analytical expression for a general graph. In principle, expressions for higher p could be derived, but the number of terms quickly becomes prohibitive. For a special case of MaxCut, the "ring of disagrees," or the one-dimensional antiferromagnetic ring, we provide an analysis for an arbitrarily high level. Using a fermionic representation, the evolution of the system under the QAOA translates into quantum control of an ensemble of independent spins. This treatment enables us to obtain analytical expressions for the performance of the QAOA for any p . It also greatly simplifies the numerical search for the optimal values of the parameters. By exploring symmetries, we identify a lower-dimensional submanifold of interest; the search effort can be accordingly reduced. This analysis also explains an observed symmetry in the optimal parameter values. Further, we numerically investigate the parameter landscape and show that it is a simple one in the sense of having no local optima.

Reliability-based trajectory optimization using nonintrusive polynomial chaos for Mars entry mission

Science.gov (United States)

Huang, Yuechen; Li, Haiyang

2018-06-01

This paper presents the reliability-based sequential optimization (RBSO) method to settle the trajectory optimization problem with parametric uncertainties in entry dynamics for Mars entry mission. First, the deterministic entry trajectory optimization model is reviewed, and then the reliability-based optimization model is formulated. In addition, the modified sequential optimization method, in which the nonintrusive polynomial chaos expansion (PCE) method and the most probable point (MPP) searching method are employed, is proposed to solve the reliability-based optimization problem efficiently. The nonintrusive PCE method contributes to the transformation between the stochastic optimization (SO) and the deterministic optimization (DO) and to the approximation of trajectory solution efficiently. The MPP method, which is used for assessing the reliability of constraints satisfaction only up to the necessary level, is employed to further improve the computational efficiency. The cycle including SO, reliability assessment and constraints update is repeated in the RBSO until the reliability requirements of constraints satisfaction are satisfied. Finally, the RBSO is compared with the traditional DO and the traditional sequential optimization based on Monte Carlo (MC) simulation in a specific Mars entry mission to demonstrate the effectiveness and the efficiency of the proposed method.

Sequential Optimization of Paths in Directed Graphs Relative to Different Cost Functions

KAUST Repository

Mahayni, Malek A.

2011-07-01

Finding optimal paths in directed graphs is a wide area of research that has received much of attention in theoretical computer science due to its importance in many applications (e.g., computer networks and road maps). Many algorithms have been developed to solve the optimal paths problem with different kinds of graphs. An algorithm that solves the problem of paths’ optimization in directed graphs relative to different cost functions is described in [1]. It follows an approach extended from the dynamic programming approach as it solves the problem sequentially and works on directed graphs with positive weights and no loop edges. The aim of this thesis is to implement and evaluate that algorithm to find the optimal paths in directed graphs relative to two different cost functions ( , ). A possible interpretation of a directed graph is a network of roads so the weights for the function represent the length of roads, whereas the weights for the function represent a constraint of the width or weight of a vehicle. The optimization aim for those two functions is to minimize the cost relative to the function and maximize the constraint value associated with the function. This thesis also includes finding and proving the relation between the two different cost functions ( , ). When given a value of one function, we can find the best possible value for the other function. This relation is proven theoretically and also implemented and experimented using Matlab®[2].

Optimization strategies for complex engineering applications

Energy Technology Data Exchange (ETDEWEB)

Eldred, M.S.

1998-02-01

LDRD research activities have focused on increasing the robustness and efficiency of optimization studies for computationally complex engineering problems. Engineering applications can be characterized by extreme computational expense, lack of gradient information, discrete parameters, non-converging simulations, and nonsmooth, multimodal, and discontinuous response variations. Guided by these challenges, the LDRD research activities have developed application-specific techniques, fundamental optimization algorithms, multilevel hybrid and sequential approximate optimization strategies, parallel processing approaches, and automatic differentiation and adjoint augmentation methods. This report surveys these activities and summarizes the key findings and recommendations.

An Error Estimate for Symplectic Euler Approximation of Optimal Control Problems

KAUST Repository

Karlsson, Jesper; Larsson, Stig; Sandberg, Mattias; Szepessy, Anders; Tempone, Raul

2015-01-01

This work focuses on numerical solutions of optimal control problems. A time discretization error representation is derived for the approximation of the associated value function. It concerns symplectic Euler solutions of the Hamiltonian system connected with the optimal control problem. The error representation has a leading-order term consisting of an error density that is computable from symplectic Euler solutions. Under an assumption of the pathwise convergence of the approximate dual function as the maximum time step goes to zero, we prove that the remainder is of higher order than the leading-error density part in the error representation. With the error representation, it is possible to perform adaptive time stepping. We apply an adaptive algorithm originally developed for ordinary differential equations. The performance is illustrated by numerical tests.

A Randomized Exchange Algorithm for Computing Optimal Approximate Designs of Experiments

KAUST Repository

Harman, Radoslav; Filová , Lenka; Richtarik, Peter

2018-01-01

We propose a class of subspace ascent methods for computing optimal approximate designs that covers both existing as well as new and more efficient algorithms. Within this class of methods, we construct a simple, randomized exchange algorithm (REX). Numerical comparisons suggest that the performance of REX is comparable or superior to the performance of state-of-the-art methods across a broad range of problem structures and sizes. We focus on the most commonly used criterion of D-optimality that also has applications beyond experimental design, such as the construction of the minimum volume ellipsoid containing a given set of data-points. For D-optimality, we prove that the proposed algorithm converges to the optimum. We also provide formulas for the optimal exchange of weights in the case of the criterion of A-optimality. These formulas enable one to use REX for computing A-optimal and I-optimal designs.

A Randomized Exchange Algorithm for Computing Optimal Approximate Designs of Experiments

KAUST Repository

Harman, Radoslav

2018-01-17

We propose a class of subspace ascent methods for computing optimal approximate designs that covers both existing as well as new and more efficient algorithms. Within this class of methods, we construct a simple, randomized exchange algorithm (REX). Numerical comparisons suggest that the performance of REX is comparable or superior to the performance of state-of-the-art methods across a broad range of problem structures and sizes. We focus on the most commonly used criterion of D-optimality that also has applications beyond experimental design, such as the construction of the minimum volume ellipsoid containing a given set of data-points. For D-optimality, we prove that the proposed algorithm converges to the optimum. We also provide formulas for the optimal exchange of weights in the case of the criterion of A-optimality. These formulas enable one to use REX for computing A-optimal and I-optimal designs.

Optimal decision making on the basis of evidence represented in spike trains.

Science.gov (United States)

Zhang, Jiaxiang; Bogacz, Rafal

2010-05-01

Experimental data indicate that perceptual decision making involves integration of sensory evidence in certain cortical areas. Theoretical studies have proposed that the computation in neural decision circuits approximates statistically optimal decision procedures (e.g., sequential probability ratio test) that maximize the reward rate in sequential choice tasks. However, these previous studies assumed that the sensory evidence was represented by continuous values from gaussian distributions with the same variance across alternatives. In this article, we make a more realistic assumption that sensory evidence is represented in spike trains described by the Poisson processes, which naturally satisfy the mean-variance relationship observed in sensory neurons. We show that for such a representation, the neural circuits involving cortical integrators and basal ganglia can approximate the optimal decision procedures for two and multiple alternative choice tasks.

Legendre-tau approximation for functional differential equations. II - The linear quadratic optimal control problem

Science.gov (United States)

Ito, Kazufumi; Teglas, Russell

1987-01-01

The numerical scheme based on the Legendre-tau approximation is proposed to approximate the feedback solution to the linear quadratic optimal control problem for hereditary differential systems. The convergence property is established using Trotter ideas. The method yields very good approximations at low orders and provides an approximation technique for computing closed-loop eigenvalues of the feedback system. A comparison with existing methods (based on averaging and spline approximations) is made.

Approximability of optimization problems through adiabatic quantum computation

CERN Document Server

Cruz-Santos, William

2014-01-01

The adiabatic quantum computation (AQC) is based on the adiabatic theorem to approximate solutions of the Schrödinger equation. The design of an AQC algorithm involves the construction of a Hamiltonian that describes the behavior of the quantum system. This Hamiltonian is expressed as a linear interpolation of an initial Hamiltonian whose ground state is easy to compute, and a final Hamiltonian whose ground state corresponds to the solution of a given combinatorial optimization problem. The adiabatic theorem asserts that if the time evolution of a quantum system described by a Hamiltonian is l

Optimization in engineering sciences approximate and metaheuristic methods

CERN Document Server

Stefanoiu, Dan; Popescu, Dumitru; Filip, Florin Gheorghe; El Kamel, Abdelkader

2014-01-01

The purpose of this book is to present the main metaheuristics and approximate and stochastic methods for optimization of complex systems in Engineering Sciences. It has been written within the framework of the European Union project ERRIC (Empowering Romanian Research on Intelligent Information Technologies), which is funded by the EU's FP7 Research Potential program and has been developed in co-operation between French and Romanian teaching researchers. Through the principles of various proposed algorithms (with additional references) this book allows the reader to explore various methods o

Essays on variational approximation techniques for stochastic optimization problems

Science.gov (United States)

Deride Silva, Julio A.

This dissertation presents five essays on approximation and modeling techniques, based on variational analysis, applied to stochastic optimization problems. It is divided into two parts, where the first is devoted to equilibrium problems and maxinf optimization, and the second corresponds to two essays in statistics and uncertainty modeling. Stochastic optimization lies at the core of this research as we were interested in relevant equilibrium applications that contain an uncertain component, and the design of a solution strategy. In addition, every stochastic optimization problem relies heavily on the underlying probability distribution that models the uncertainty. We studied these distributions, in particular, their design process and theoretical properties such as their convergence. Finally, the last aspect of stochastic optimization that we covered is the scenario creation problem, in which we described a procedure based on a probabilistic model to create scenarios for the applied problem of power estimation of renewable energies. In the first part, Equilibrium problems and maxinf optimization, we considered three Walrasian equilibrium problems: from economics, we studied a stochastic general equilibrium problem in a pure exchange economy, described in Chapter 3, and a stochastic general equilibrium with financial contracts, in Chapter 4; finally from engineering, we studied an infrastructure planning problem in Chapter 5. We stated these problems as belonging to the maxinf optimization class and, in each instance, we provided an approximation scheme based on the notion of lopsided convergence and non-concave duality. This strategy is the foundation of the augmented Walrasian algorithm, whose convergence is guaranteed by lopsided convergence, that was implemented computationally, obtaining numerical results for relevant examples. The second part, Essays about statistics and uncertainty modeling, contains two essays covering a convergence problem for a sequence

Optimization strategies based on sequential quadratic programming applied for a fermentation process for butanol production.

Science.gov (United States)

Pinto Mariano, Adriano; Bastos Borba Costa, Caliane; de Franceschi de Angelis, Dejanira; Maugeri Filho, Francisco; Pires Atala, Daniel Ibraim; Wolf Maciel, Maria Regina; Maciel Filho, Rubens

2009-11-01

In this work, the mathematical optimization of a continuous flash fermentation process for the production of biobutanol was studied. The process consists of three interconnected units, as follows: fermentor, cell-retention system (tangential microfiltration), and vacuum flash vessel (responsible for the continuous recovery of butanol from the broth). The objective of the optimization was to maximize butanol productivity for a desired substrate conversion. Two strategies were compared for the optimization of the process. In one of them, the process was represented by a deterministic model with kinetic parameters determined experimentally and, in the other, by a statistical model obtained using the factorial design technique combined with simulation. For both strategies, the problem was written as a nonlinear programming problem and was solved with the sequential quadratic programming technique. The results showed that despite the very similar solutions obtained with both strategies, the problems found with the strategy using the deterministic model, such as lack of convergence and high computational time, make the use of the optimization strategy with the statistical model, which showed to be robust and fast, more suitable for the flash fermentation process, being recommended for real-time applications coupling optimization and control.

Simulated Stochastic Approximation Annealing for Global Optimization With a Square-Root Cooling Schedule

KAUST Repository

Liang, Faming

2014-04-03

Simulated annealing has been widely used in the solution of optimization problems. As known by many researchers, the global optima cannot be guaranteed to be located by simulated annealing unless a logarithmic cooling schedule is used. However, the logarithmic cooling schedule is so slow that no one can afford to use this much CPU time. This article proposes a new stochastic optimization algorithm, the so-called simulated stochastic approximation annealing algorithm, which is a combination of simulated annealing and the stochastic approximation Monte Carlo algorithm. Under the framework of stochastic approximation, it is shown that the new algorithm can work with a cooling schedule in which the temperature can decrease much faster than in the logarithmic cooling schedule, for example, a square-root cooling schedule, while guaranteeing the global optima to be reached when the temperature tends to zero. The new algorithm has been tested on a few benchmark optimization problems, including feed-forward neural network training and protein-folding. The numerical results indicate that the new algorithm can significantly outperform simulated annealing and other competitors. Supplementary materials for this article are available online.

Legendre-tau approximation for functional differential equations. Part 2: The linear quadratic optimal control problem

Science.gov (United States)

Ito, K.; Teglas, R.

1984-01-01

The numerical scheme based on the Legendre-tau approximation is proposed to approximate the feedback solution to the linear quadratic optimal control problem for hereditary differential systems. The convergence property is established using Trotter ideas. The method yields very good approximations at low orders and provides an approximation technique for computing closed-loop eigenvalues of the feedback system. A comparison with existing methods (based on averaging and spline approximations) is made.

A minimax procedure in the context of sequential mastery testing

NARCIS (Netherlands)

Vos, Hendrik J.

1999-01-01

The purpose of this paper is to derive optimal rules for sequential mastery tests. In a sequential mastery test, the decision is to classify a subject as a master or a nonmaster, or to continue sampling and administering another random test item. The framework of minimax sequential decision theory

Approximate dynamic programming approaches for appointment scheduling with patient preferences.

Science.gov (United States)

Li, Xin; Wang, Jin; Fung, Richard Y K

2018-04-01

During the appointment booking process in out-patient departments, the level of patient satisfaction can be affected by whether or not their preferences can be met, including the choice of physicians and preferred time slot. In addition, because the appointments are sequential, considering future possible requests is also necessary for a successful appointment system. This paper proposes a Markov decision process model for optimizing the scheduling of sequential appointments with patient preferences. In contrast to existing models, the evaluation of a booking decision in this model focuses on the extent to which preferences are satisfied. Characteristics of the model are analysed to develop a system for formulating booking policies. Based on these characteristics, two types of approximate dynamic programming algorithms are developed to avoid the curse of dimensionality. Experimental results suggest directions for further fine-tuning of the model, as well as improving the efficiency of the two proposed algorithms. Copyright © 2018 Elsevier B.V. All rights reserved.

Slow Growth and Optimal Approximation of Pseudoanalytic Functions on the Disk

Directory of Open Access Journals (Sweden)

Devendra Kumar

2013-07-01

Full Text Available Pseudoanalytic functions (PAF are constructed as complex combination of real-valued analytic solutions to the Stokes-Betrami System. These solutions include the generalized biaxisymmetric potentials. McCoy [10] considered the approximation of pseudoanalytic functions on the disk. Kumar et al. [9] studied the generalized order and generalized type of PAF in terms of the Fourier coefficients occurring in its local expansion and optimal approximation errors in Bernstein sense on the disk. The aim of this paper is to improve the results of McCoy [10] and Kumar et al. [9]. Our results apply satisfactorily for slow growth.

An A Posteriori Error Estimate for Symplectic Euler Approximation of Optimal Control Problems

KAUST Repository

Karlsson, Peer Jesper

2015-01-07

This work focuses on numerical solutions of optimal control problems. A time discretization error representation is derived for the approximation of the associated value function. It concerns Symplectic Euler solutions of the Hamiltonian system connected with the optimal control problem. The error representation has a leading order term consisting of an error density that is computable from Symplectic Euler solutions. Under an assumption of the pathwise convergence of the approximate dual function as the maximum time step goes to zero, we prove that the remainder is of higher order than the leading error density part in the error representation. With the error representation, it is possible to perform adaptive time stepping. We apply an adaptive algorithm originally developed for ordinary differential equations.

Acquisition of Inductive Biconditional Reasoning Skills: Training of Simultaneous and Sequential Processing.

Science.gov (United States)

Lee, Seong-Soo

1982-01-01

Tenth-grade students (n=144) received training on one of three processing methods: coding-mapping (simultaneous), coding only, or decision tree (sequential). The induced simultaneous processing strategy worked optimally under rule learning, while the sequential strategy was difficult to induce and/or not optimal for rule-learning operations.…

Unit Stratified Sampling as a Tool for Approximation of Stochastic Optimization Problems

Czech Academy of Sciences Publication Activity Database

Šmíd, Martin

2012-01-01

Roč. 19, č. 30 (2012), s. 153-169 ISSN 1212-074X R&D Projects: GA ČR GAP402/11/0150; GA ČR GAP402/10/0956; GA ČR GA402/09/0965 Institutional research plan: CEZ:AV0Z10750506 Institutional support: RVO:67985556 Keywords : Stochastic programming * approximation * stratified sampling Subject RIV: BB - Applied Statistics, Operational Research http://library.utia.cas.cz/separaty/2013/E/smid-unit stratified sampling as a tool for approximation of stochastic optimization problems.pdf

An A Posteriori Error Estimate for Symplectic Euler Approximation of Optimal Control Problems

KAUST Repository

Karlsson, Peer Jesper; Larsson, Stig; Sandberg, Mattias; Szepessy, Anders; Tempone, Raul

2015-01-01

This work focuses on numerical solutions of optimal control problems. A time discretization error representation is derived for the approximation of the associated value function. It concerns Symplectic Euler solutions of the Hamiltonian system

Global WASF-GA: An Evolutionary Algorithm in Multiobjective Optimization to Approximate the Whole Pareto Optimal Front.

Science.gov (United States)

Saborido, Rubén; Ruiz, Ana B; Luque, Mariano

2017-01-01

In this article, we propose a new evolutionary algorithm for multiobjective optimization called Global WASF-GA ( global weighting achievement scalarizing function genetic algorithm), which falls within the aggregation-based evolutionary algorithms. The main purpose of Global WASF-GA is to approximate the whole Pareto optimal front. Its fitness function is defined by an achievement scalarizing function (ASF) based on the Tchebychev distance, in which two reference points are considered (both utopian and nadir objective vectors) and the weight vector used is taken from a set of weight vectors whose inverses are well-distributed. At each iteration, all individuals are classified into different fronts. Each front is formed by the solutions with the lowest values of the ASF for the different weight vectors in the set, using the utopian vector and the nadir vector as reference points simultaneously. Varying the weight vector in the ASF while considering the utopian and the nadir vectors at the same time enables the algorithm to obtain a final set of nondominated solutions that approximate the whole Pareto optimal front. We compared Global WASF-GA to MOEA/D (different versions) and NSGA-II in two-, three-, and five-objective problems. The computational results obtained permit us to conclude that Global WASF-GA gets better performance, regarding the hypervolume metric and the epsilon indicator, than the other two algorithms in many cases, especially in three- and five-objective problems.

Robust approximate optimal guidance strategies for aeroassisted orbital transfer missions

Science.gov (United States)

Ilgen, Marc R.

This thesis presents the application of game theoretic and regular perturbation methods to the problem of determining robust approximate optimal guidance laws for aeroassisted orbital transfer missions with atmospheric density and navigated state uncertainties. The optimal guidance problem is reformulated as a differential game problem with the guidance law designer and Nature as opposing players. The resulting equations comprise the necessary conditions for the optimal closed loop guidance strategy in the presence of worst case parameter variations. While these equations are nonlinear and cannot be solved analytically, the presence of a small parameter in the equations of motion allows the method of regular perturbations to be used to solve the equations approximately. This thesis is divided into five parts. The first part introduces the class of problems to be considered and presents results of previous research. The second part then presents explicit semianalytical guidance law techniques for the aerodynamically dominated region of flight. These guidance techniques are applied to unconstrained and control constrained aeroassisted plane change missions and Mars aerocapture missions, all subject to significant atmospheric density variations. The third part presents a guidance technique for aeroassisted orbital transfer problems in the gravitationally dominated region of flight. Regular perturbations are used to design an implicit guidance technique similar to the second variation technique but that removes the need for numerically computing an optimal trajectory prior to flight. This methodology is then applied to a set of aeroassisted inclination change missions. In the fourth part, the explicit regular perturbation solution technique is extended to include the class of guidance laws with partial state information. This methodology is then applied to an aeroassisted plane change mission using inertial measurements and subject to uncertainties in the initial value

Spline Approximation-Based Optimization of Multi-component Disperse Reinforced Composites

Directory of Open Access Journals (Sweden)

Yu. I. Dimitrienko

2015-01-01

Full Text Available The paper suggests an algorithm for solving the problems of optimal design of multicomponent disperse-reinforced composite materials, which properties are defined by filler concentrations and are independent of their shape. It formulates the problem of conditional optimization of a composite with restrictions on its effective parameters - the elasticity modulus, tension and compression strengths, and heat-conductivity coefficient with minimized composite density. The effective characteristics of a composite were computed by finite-element solving the auxiliary local problems of elasticity and heat-conductivity theories appearing when the asymptotic averaging method is applied.The algorithm suggested to solve the optimization problem includes the following main stages:1 finding a set of solutions for direct problem to calculate the effective characteristics;2 constructing the curves of effective characteristics versus filler concentrations by means of approximating functions, which are offered for use as a thin plate spline with smoothing;3 constructing a set of points to satisfy restrictions and a boundary of the point set to satisfy restrictions obtaining, as a result, a contour which can be parameterized;4 defining a global density minimum over the contour through psi-transformation.A numerical example of solving the optimization problem was given for a dispersereinforced composite with two types of fillers being hollow microspheres: glass and phenolic. It was shown that the suggested algorithm allows us to find optimal filler concentrations efficiently enough.

Multidisciplinary Inverse Reliability Analysis Based on Collaborative Optimization with Combination of Linear Approximations

Directory of Open Access Journals (Sweden)

Xin-Jia Meng

2015-01-01

Full Text Available Multidisciplinary reliability is an important part of the reliability-based multidisciplinary design optimization (RBMDO. However, it usually has a considerable amount of calculation. The purpose of this paper is to improve the computational efficiency of multidisciplinary inverse reliability analysis. A multidisciplinary inverse reliability analysis method based on collaborative optimization with combination of linear approximations (CLA-CO is proposed in this paper. In the proposed method, the multidisciplinary reliability assessment problem is first transformed into a problem of most probable failure point (MPP search of inverse reliability, and then the process of searching for MPP of multidisciplinary inverse reliability is performed based on the framework of CLA-CO. This method improves the MPP searching process through two elements. One is treating the discipline analyses as the equality constraints in the subsystem optimization, and the other is using linear approximations corresponding to subsystem responses as the replacement of the consistency equality constraint in system optimization. With these two elements, the proposed method realizes the parallel analysis of each discipline, and it also has a higher computational efficiency. Additionally, there are no difficulties in applying the proposed method to problems with nonnormal distribution variables. One mathematical test problem and an electronic packaging problem are used to demonstrate the effectiveness of the proposed method.

Acoustical topology optimization of Zwicker's loudness with Padé approximation

DEFF Research Database (Denmark)

Kook, Junghwan; Jensen, Jakob Søndergaard; Wang, Semyung

2013-01-01

Zwicker's loudness is a conventional standard index for measuring human hearing annoyance and has been widely considered in many industrial fields for noise evaluations. The calculation of Zwicker's loudness, which is needed for a wide range of frequency responses with a fine frequency resolution......, this approach imposes prohibitively high computational costs. In this research, we propose a computationally-efficient approach to resolve the computational issue in the computation and optimization of Zwicker's loudness. We present an efficient approach which combines the finite element method (FEM......) with the Padé approximation (PA) procedure for obtaining Zwicker's loudness and for applying it in a gradient-based acoustical topology optimization procedure applied to the design of acoustic devices to minimize Zwicker's loudness. In this respect, the calculation of Zwicker's loudness is represented by the PA...

A parallel approximate string matching under Levenshtein distance on graphics processing units using warp-shuffle operations.

Directory of Open Access Journals (Sweden)

ThienLuan Ho

Full Text Available Approximate string matching with k-differences has a number of practical applications, ranging from pattern recognition to computational biology. This paper proposes an efficient memory-access algorithm for parallel approximate string matching with k-differences on Graphics Processing Units (GPUs. In the proposed algorithm, all threads in the same GPUs warp share data using warp-shuffle operation instead of accessing the shared memory. Moreover, we implement the proposed algorithm by exploiting the memory structure of GPUs to optimize its performance. Experiment results for real DNA packages revealed that the performance of the proposed algorithm and its implementation archived up to 122.64 and 1.53 times compared to that of sequential algorithm on CPU and previous parallel approximate string matching algorithm on GPUs, respectively.

Simultaneous parameter and tolerance optimization of structures via probability-interval mixed reliability model

DEFF Research Database (Denmark)

Luo, Yangjun; Wu, Xiaoxiang; Zhou, Mingdong

2015-01-01

Both structural sizes and dimensional tolerances strongly influence the manufacturing cost and the functional performance of a practical product. This paper presents an optimization method to simultaneously find the optimal combination of structural sizes and dimensional tolerances. Based...... transformed into their equivalent formulations by using the performance measure approach. The optimization problem is then solved with the sequential approximate programming. Meanwhile, a numerically stable algorithm based on the trust region method is proposed to efficiently update the target performance...

A sequential fuzzy diagnosis method for rotating machinery using ant colony optimization and possibility theory

Energy Technology Data Exchange (ETDEWEB)

Sun, Hao; Ping, Xueliang; Cao, Yi; Lie, Ke [Jiangnan University, Wuxi (China); Chen, Peng [Mie University, Mie (Japan); Wang, Huaqing [Beijing University, Beijing (China)

2014-04-15

This study proposes a novel intelligent fault diagnosis method for rotating machinery using ant colony optimization (ACO) and possibility theory. The non-dimensional symptom parameters (NSPs) in the frequency domain are defined to reflect the features of the vibration signals measured in each state. A sensitive evaluation method for selecting good symptom parameters using principal component analysis (PCA) is proposed for detecting and distinguishing faults in rotating machinery. By using ACO clustering algorithm, the synthesizing symptom parameters (SSP) for condition diagnosis are obtained. A fuzzy diagnosis method using sequential inference and possibility theory is also proposed, by which the conditions of the machinery can be identified sequentially. Lastly, the proposed method is compared with a conventional neural networks (NN) method. Practical examples of diagnosis for a V-belt driving equipment used in a centrifugal fan are provided to verify the effectiveness of the proposed method. The results verify that the faults that often occur in V-belt driving equipment, such as a pulley defect state, a belt defect state and a belt looseness state, are effectively identified by the proposed method, while these faults are difficult to detect using conventional NN.

Study on Feasibility of Applying Function Approximation Moment Method to Achieve Reliability-Based Design Optimization

International Nuclear Information System (INIS)

Huh, Jae Sung; Kwak, Byung Man

2011-01-01

Robust optimization or reliability-based design optimization are some of the methodologies that are employed to take into account the uncertainties of a system at the design stage. For applying such methodologies to solve industrial problems, accurate and efficient methods for estimating statistical moments and failure probability are required, and further, the results of sensitivity analysis, which is needed for searching direction during the optimization process, should also be accurate. The aim of this study is to employ the function approximation moment method into the sensitivity analysis formulation, which is expressed as an integral form, to verify the accuracy of the sensitivity results, and to solve a typical problem of reliability-based design optimization. These results are compared with those of other moment methods, and the feasibility of the function approximation moment method is verified. The sensitivity analysis formula with integral form is the efficient formulation for evaluating sensitivity because any additional function calculation is not needed provided the failure probability or statistical moments are calculated

Cost-effectiveness of simultaneous versus sequential surgery in head and neck reconstruction.

Science.gov (United States)

Wong, Kevin K; Enepekides, Danny J; Higgins, Kevin M

2011-02-01

To determine whether simultaneous (ablation and reconstruction overlaps by two teams) head and neck reconstruction is cost effective compared to sequentially (ablation followed by reconstruction) performed surgery. Case-controlled study. Tertiary care hospital. Oncology patients undergoing free flap reconstruction of the head and neck. A match paired comparison study was performed with a retrospective chart review examining the total time of surgery for sequential and simultaneous surgery. Nine patients were selected for both the sequential and simultaneous groups. Sequential head and neck reconstruction patients were pair matched with patients who had undergone similar oncologic ablative or reconstructive procedures performed in a simultaneous fashion. A detailed cost analysis using the microcosting method was then undertaken looking at the direct costs of the surgeons, anesthesiologist, operating room, and nursing. On average, simultaneous surgery required 3 hours 15 minutes less operating time, leading to a cost savings of approximately $1200/case when compared to sequential surgery. This represents approximately a 15% reduction in the cost of the entire operation. Simultaneous head and neck reconstruction is more cost effective when compared to sequential surgery.

Online Adaptive Optimal Control of Vehicle Active Suspension Systems Using Single-Network Approximate Dynamic Programming

Directory of Open Access Journals (Sweden)

Zhi-Jun Fu

2017-01-01

Full Text Available In view of the performance requirements (e.g., ride comfort, road holding, and suspension space limitation for vehicle suspension systems, this paper proposes an adaptive optimal control method for quarter-car active suspension system by using the approximate dynamic programming approach (ADP. Online optimal control law is obtained by using a single adaptive critic NN to approximate the solution of the Hamilton-Jacobi-Bellman (HJB equation. Stability of the closed-loop system is proved by Lyapunov theory. Compared with the classic linear quadratic regulator (LQR approach, the proposed ADP-based adaptive optimal control method demonstrates improved performance in the presence of parametric uncertainties (e.g., sprung mass and unknown road displacement. Numerical simulation results of a sedan suspension system are presented to verify the effectiveness of the proposed control strategy.

Optimized implementations of rational approximations for the Voigt and complex error function

International Nuclear Information System (INIS)

Schreier, Franz

2011-01-01

Rational functions are frequently used as efficient yet accurate numerical approximations for real and complex valued functions. For the complex error function w(x+iy), whose real part is the Voigt function K(x,y), code optimizations of rational approximations are investigated. An assessment of requirements for atmospheric radiative transfer modeling indicates a y range over many orders of magnitude and accuracy better than 10 -4 . Following a brief survey of complex error function algorithms in general and rational function approximations in particular the problems associated with subdivisions of the x, y plane (i.e., conditional branches in the code) are discussed and practical aspects of Fortran and Python implementations are considered. Benchmark tests of a variety of algorithms demonstrate that programming language, compiler choice, and implementation details influence computational speed and there is no unique ranking of algorithms. A new implementation, based on subdivision of the upper half-plane in only two regions, combining Weideman's rational approximation for small |x|+y<15 and Humlicek's rational approximation otherwise is shown to be efficient and accurate for all x, y.

TARGETED SEQUENTIAL DESIGN FOR TARGETED LEARNING INFERENCE OF THE OPTIMAL TREATMENT RULE AND ITS MEAN REWARD.

Science.gov (United States)

Chambaz, Antoine; Zheng, Wenjing; van der Laan, Mark J

2017-01-01

This article studies the targeted sequential inference of an optimal treatment rule (TR) and its mean reward in the non-exceptional case, i.e. , assuming that there is no stratum of the baseline covariates where treatment is neither beneficial nor harmful, and under a companion margin assumption. Our pivotal estimator, whose definition hinges on the targeted minimum loss estimation (TMLE) principle, actually infers the mean reward under the current estimate of the optimal TR. This data-adaptive statistical parameter is worthy of interest on its own. Our main result is a central limit theorem which enables the construction of confidence intervals on both mean rewards under the current estimate of the optimal TR and under the optimal TR itself. The asymptotic variance of the estimator takes the form of the variance of an efficient influence curve at a limiting distribution, allowing to discuss the efficiency of inference. As a by product, we also derive confidence intervals on two cumulated pseudo-regrets, a key notion in the study of bandits problems. A simulation study illustrates the procedure. One of the corner-stones of the theoretical study is a new maximal inequality for martingales with respect to the uniform entropy integral.

Enhanced approximate cloaking by optimal change of variables

International Nuclear Information System (INIS)

Griesmaier, Roland; Vogelius, Michael S

2014-01-01

The aim of (passive) cloaking with respect to electromagnetic (or acoustic) sensing is to surround a region of space with a material layer—the cloak—that renders its contents and even the existence of the layer undetectable by such measurements. At least theoretically this can be achieved using the coordinate invariance of the underlying wave equation, through so-called cloaking by mapping. However, a practical realization of the cloaking by mapping schemes discussed in the literature frequently requires the design of highly anisotropic materials with extreme dielectric properties. In this work we consider, in the electrostatic case, a regularized, approximate cloaking by mapping scheme and discuss the problem of optimal choice of radial maps, that determine the conductivity distribution of the cloak. We consider two different optimality criteria: minimal maximal anisotropy and minimal mean anisotropy of this conductivity distribution. Using both criteria we show that it is possible to achieve significantly lower anisotropy (for a prescribed level of invisibility) or significantly lower visibility (for a prescribed level of anisotropy). For example, in two dimensions one may achieve exponentially small visibility with a cloak, that in terms of anisotropy (and lowest and highest conductivity) is no worse than the traditional affine map cloak, which only yields quadratically small visibility. (paper)

Optimal periodic inspection of a deterioration process with sequential condition states

International Nuclear Information System (INIS)

Kallen, M.J.; Noortwijk, J.M. van

2006-01-01

The condition of components subject to visual inspections is often evaluated on a discrete scale. If at each inspection a decision is made to do nothing or to perform preventive or corrective maintenance, the proposed decision model allows us to determine the optimal time between periodic inspections, such that the expected average costs per unit of time are minimized. The model which describes the uncertain condition over time is based on a Markov process with sequential phases. The key quantities involved in the model are the probabilities of having to perform either preventive or corrective maintenance before or after an inspection. The costs functions for two scenarios are presented: a scenario in which failure is immediately detected without the need to perform an inspection and a scenario in which failure is only detected by inspection of the object. Analytical results for a special case and algorithmic results for a broad class of Markov processes are derived. The model is illustrated using an application to the periodic inspection of road bridges

Hypotension Risk Prediction via Sequential Contrast Patterns of ICU Blood Pressure.

Science.gov (United States)

Ghosh, Shameek; Feng, Mengling; Nguyen, Hung; Li, Jinyan

2016-09-01

Acute hypotension is a significant risk factor for in-hospital mortality at intensive care units. Prolonged hypotension can cause tissue hypoperfusion, leading to cellular dysfunction and severe injuries to multiple organs. Prompt medical interventions are thus extremely important for dealing with acute hypotensive episodes (AHE). Population level prognostic scoring systems for risk stratification of patients are suboptimal in such scenarios. However, the design of an efficient risk prediction system can significantly help in the identification of critical care patients, who are at risk of developing an AHE within a future time span. Toward this objective, a pattern mining algorithm is employed to extract informative sequential contrast patterns from hemodynamic data, for the prediction of hypotensive episodes. The hypotensive and normotensive patient groups are extracted from the MIMIC-II critical care research database, following an appropriate clinical inclusion criteria. The proposed method consists of a data preprocessing step to convert the blood pressure time series into symbolic sequences, using a symbolic aggregate approximation algorithm. Then, distinguishing subsequences are identified using the sequential contrast mining algorithm. These subsequences are used to predict the occurrence of an AHE in a future time window separated by a user-defined gap interval. Results indicate that the method performs well in terms of the prediction performance as well as in the generation of sequential patterns of clinical significance. Hence, the novelty of sequential patterns is in their usefulness as potential physiological biomarkers for building optimal patient risk stratification systems and for further clinical investigation of interesting patterns in critical care patients.

Optimization of approximate decision rules relative to number of misclassifications

KAUST Repository

Amin, Talha M.; Chikalov, Igor; Moshkov, Mikhail; Zielosko, Beata

2012-01-01

In the paper, we study an extension of dynamic programming approach which allows optimization of approximate decision rules relative to the number of misclassifications. We introduce an uncertainty measure J(T) which is a difference between the number of rows in a decision table T and the number of rows with the most common decision for T. For a nonnegative real number γ, we consider γ-decision rules that localize rows in subtables of T with uncertainty at most γ. The presented algorithm constructs a directed acyclic graph Δγ(T). Based on this graph we can describe the whole set of so-called irredundant γ-decision rules. We can optimize rules from this set according to the number of misclassifications. Results of experiments with decision tables from the UCI Machine Learning Repository are presented. © 2012 The authors and IOS Press. All rights reserved.

Optimization of approximate decision rules relative to number of misclassifications

KAUST Repository

Amin, Talha

2012-12-01

In the paper, we study an extension of dynamic programming approach which allows optimization of approximate decision rules relative to the number of misclassifications. We introduce an uncertainty measure J(T) which is a difference between the number of rows in a decision table T and the number of rows with the most common decision for T. For a nonnegative real number γ, we consider γ-decision rules that localize rows in subtables of T with uncertainty at most γ. The presented algorithm constructs a directed acyclic graph Δγ(T). Based on this graph we can describe the whole set of so-called irredundant γ-decision rules. We can optimize rules from this set according to the number of misclassifications. Results of experiments with decision tables from the UCI Machine Learning Repository are presented. © 2012 The authors and IOS Press. All rights reserved.

Adaptive finite element method for shape optimization

KAUST Repository

Morin, Pedro; Nochetto, Ricardo H.; Pauletti, Miguel S.; Verani, Marco

2012-01-01

We examine shape optimization problems in the context of inexact sequential quadratic programming. Inexactness is a consequence of using adaptive finite element methods (AFEM) to approximate the state and adjoint equations (via the dual weighted residual method), update the boundary, and compute the geometric functional. We present a novel algorithm that equidistributes the errors due to shape optimization and discretization, thereby leading to coarse resolution in the early stages and fine resolution upon convergence, and thus optimizing the computational effort. We discuss the ability of the algorithm to detect whether or not geometric singularities such as corners are genuine to the problem or simply due to lack of resolution - a new paradigm in adaptivity. © EDP Sciences, SMAI, 2012.

Adaptive finite element method for shape optimization

KAUST Repository

Morin, Pedro

2012-01-16

We examine shape optimization problems in the context of inexact sequential quadratic programming. Inexactness is a consequence of using adaptive finite element methods (AFEM) to approximate the state and adjoint equations (via the dual weighted residual method), update the boundary, and compute the geometric functional. We present a novel algorithm that equidistributes the errors due to shape optimization and discretization, thereby leading to coarse resolution in the early stages and fine resolution upon convergence, and thus optimizing the computational effort. We discuss the ability of the algorithm to detect whether or not geometric singularities such as corners are genuine to the problem or simply due to lack of resolution - a new paradigm in adaptivity. © EDP Sciences, SMAI, 2012.

Optimization Strategies for Bruch's Membrane Opening Minimum Rim Area Calculation: Sequential versus Simultaneous Minimization.

Science.gov (United States)

Enders, Philip; Adler, Werner; Schaub, Friederike; Hermann, Manuel M; Diestelhorst, Michael; Dietlein, Thomas; Cursiefen, Claus; Heindl, Ludwig M

2017-10-24

To compare a simultaneously optimized continuous minimum rim surface parameter between Bruch's membrane opening (BMO) and the internal limiting membrane to the standard sequential minimization used for calculating the BMO minimum rim area in spectral domain optical coherence tomography (SD-OCT). In this case-control, cross-sectional study, 704 eyes of 445 participants underwent SD-OCT of the optic nerve head (ONH), visual field testing, and clinical examination. Globally and clock-hour sector-wise optimized BMO-based minimum rim area was calculated independently. Outcome parameters included BMO-globally optimized minimum rim area (BMO-gMRA) and sector-wise optimized BMO-minimum rim area (BMO-MRA). BMO area was 1.89 ± 0.05 mm 2 . Mean global BMO-MRA was 0.97 ± 0.34 mm 2 , mean global BMO-gMRA was 1.01 ± 0.36 mm 2 . Both parameters correlated with r = 0.995 (P < 0.001); mean difference was 0.04 mm 2 (P < 0.001). In all sectors, parameters differed by 3.0-4.2%. In receiver operating characteristics, the calculated area under the curve (AUC) to differentiate glaucoma was 0.873 for BMO-MRA, compared to 0.866 for BMO-gMRA (P = 0.004). Among ONH sectors, the temporal inferior location showed the highest AUC. Optimization strategies to calculate BMO-based minimum rim area led to significantly different results. Imposing an additional adjacency constraint within calculation of BMO-MRA does not improve diagnostic power. Global and temporal inferior BMO-MRA performed best in differentiating glaucoma patients.

Multilevel Monte Carlo in Approximate Bayesian Computation

KAUST Repository

Jasra, Ajay

2017-02-13

In the following article we consider approximate Bayesian computation (ABC) inference. We introduce a method for numerically approximating ABC posteriors using the multilevel Monte Carlo (MLMC). A sequential Monte Carlo version of the approach is developed and it is shown under some assumptions that for a given level of mean square error, this method for ABC has a lower cost than i.i.d. sampling from the most accurate ABC approximation. Several numerical examples are given.

Cascade Optimization for Aircraft Engines With Regression and Neural Network Analysis - Approximators

Science.gov (United States)

Patnaik, Surya N.; Guptill, James D.; Hopkins, Dale A.; Lavelle, Thomas M.

2000-01-01

The NASA Engine Performance Program (NEPP) can configure and analyze almost any type of gas turbine engine that can be generated through the interconnection of a set of standard physical components. In addition, the code can optimize engine performance by changing adjustable variables under a set of constraints. However, for engine cycle problems at certain operating points, the NEPP code can encounter difficulties: nonconvergence in the currently implemented Powell's optimization algorithm and deficiencies in the Newton-Raphson solver during engine balancing. A project was undertaken to correct these deficiencies. Nonconvergence was avoided through a cascade optimization strategy, and deficiencies associated with engine balancing were eliminated through neural network and linear regression methods. An approximation-interspersed cascade strategy was used to optimize the engine's operation over its flight envelope. Replacement of Powell's algorithm by the cascade strategy improved the optimization segment of the NEPP code. The performance of the linear regression and neural network methods as alternative engine analyzers was found to be satisfactory. This report considers two examples-a supersonic mixed-flow turbofan engine and a subsonic waverotor-topped engine-to illustrate the results, and it discusses insights gained from the improved version of the NEPP code.

Sensitivity Analysis in Sequential Decision Models.

Science.gov (United States)

Chen, Qiushi; Ayer, Turgay; Chhatwal, Jagpreet

2017-02-01

Sequential decision problems are frequently encountered in medical decision making, which are commonly solved using Markov decision processes (MDPs). Modeling guidelines recommend conducting sensitivity analyses in decision-analytic models to assess the robustness of the model results against the uncertainty in model parameters. However, standard methods of conducting sensitivity analyses cannot be directly applied to sequential decision problems because this would require evaluating all possible decision sequences, typically in the order of trillions, which is not practically feasible. As a result, most MDP-based modeling studies do not examine confidence in their recommended policies. In this study, we provide an approach to estimate uncertainty and confidence in the results of sequential decision models. First, we provide a probabilistic univariate method to identify the most sensitive parameters in MDPs. Second, we present a probabilistic multivariate approach to estimate the overall confidence in the recommended optimal policy considering joint uncertainty in the model parameters. We provide a graphical representation, which we call a policy acceptability curve, to summarize the confidence in the optimal policy by incorporating stakeholders' willingness to accept the base case policy. For a cost-effectiveness analysis, we provide an approach to construct a cost-effectiveness acceptability frontier, which shows the most cost-effective policy as well as the confidence in that for a given willingness to pay threshold. We demonstrate our approach using a simple MDP case study. We developed a method to conduct sensitivity analysis in sequential decision models, which could increase the credibility of these models among stakeholders.

Efficient approach for reliability-based optimization based on weighted importance sampling approach

International Nuclear Information System (INIS)

Yuan, Xiukai; Lu, Zhenzhou

2014-01-01

An efficient methodology is presented to perform the reliability-based optimization (RBO). It is based on an efficient weighted approach for constructing an approximation of the failure probability as an explicit function of the design variables which is referred to as the ‘failure probability function (FPF)’. It expresses the FPF as a weighted sum of sample values obtained in the simulation-based reliability analysis. The required computational effort for decoupling in each iteration is just single reliability analysis. After the approximation of the FPF is established, the target RBO problem can be decoupled into a deterministic one. Meanwhile, the proposed weighted approach is combined with a decoupling approach and a sequential approximate optimization framework. Engineering examples are given to demonstrate the efficiency and accuracy of the presented methodology

Distributed approximation of Pareto surfaces in multicriteria radiation therapy treatment planning

International Nuclear Information System (INIS)

Bokrantz, Rasmus

2013-01-01

We consider multicriteria radiation therapy treatment planning by navigation over the Pareto surface, implemented by interpolation between discrete treatment plans. Current state of the art for calculation of a discrete representation of the Pareto surface is to sandwich this set between inner and outer approximations that are updated one point at a time. In this paper, we generalize this sequential method to an algorithm that permits parallelization. The principle of the generalization is to apply the sequential method to an approximation of an inexpensive model of the Pareto surface. The information gathered from the model is sub-sequently used for the calculation of points from the exact Pareto surface, which are processed in parallel. The model is constructed according to the current inner and outer approximations, and given a shape that is difficult to approximate, in order to avoid that parts of the Pareto surface are incorrectly disregarded. Approximations of comparable quality to those generated by the sequential method are demonstrated when the degree of parallelization is up to twice the number of dimensions of the objective space. For practical applications, the number of dimensions is typically at least five, so that a speed-up of one order of magnitude is obtained. (paper)

Distributed approximation of Pareto surfaces in multicriteria radiation therapy treatment planning.

Science.gov (United States)

Bokrantz, Rasmus

2013-06-07

We consider multicriteria radiation therapy treatment planning by navigation over the Pareto surface, implemented by interpolation between discrete treatment plans. Current state of the art for calculation of a discrete representation of the Pareto surface is to sandwich this set between inner and outer approximations that are updated one point at a time. In this paper, we generalize this sequential method to an algorithm that permits parallelization. The principle of the generalization is to apply the sequential method to an approximation of an inexpensive model of the Pareto surface. The information gathered from the model is sub-sequently used for the calculation of points from the exact Pareto surface, which are processed in parallel. The model is constructed according to the current inner and outer approximations, and given a shape that is difficult to approximate, in order to avoid that parts of the Pareto surface are incorrectly disregarded. Approximations of comparable quality to those generated by the sequential method are demonstrated when the degree of parallelization is up to twice the number of dimensions of the objective space. For practical applications, the number of dimensions is typically at least five, so that a speed-up of one order of magnitude is obtained.

Bounded-Degree Approximations of Stochastic Networks

Energy Technology Data Exchange (ETDEWEB)

Quinn, Christopher J.; Pinar, Ali; Kiyavash, Negar

2017-06-01

We propose algorithms to approximate directed information graphs. Directed information graphs are probabilistic graphical models that depict causal dependencies between stochastic processes in a network. The proposed algorithms identify optimal and near-optimal approximations in terms of Kullback-Leibler divergence. The user-chosen sparsity trades off the quality of the approximation against visual conciseness and computational tractability. One class of approximations contains graphs with speci ed in-degrees. Another class additionally requires that the graph is connected. For both classes, we propose algorithms to identify the optimal approximations and also near-optimal approximations, using a novel relaxation of submodularity. We also propose algorithms to identify the r-best approximations among these classes, enabling robust decision making.

Properties of simultaneous and sequential two-nucleon transfer

International Nuclear Information System (INIS)

Pinkston, W.T.; Satchler, G.R.

1982-01-01

Approximate forms of the first- and second-order distorted-wave Born amplitudes are used to study the overall structure, particularly the selection rules, of the amplitudes for simultaneous and sequential transfer of two nucleons. The role of the spin-state assumed for the intermediate deuterons in sequential (t, p) reactions is stressed. The similarity of one-step and two-step amplitudes for (α, d) reactions is exhibited, and the consequent absence of any obvious J-dependence in their interference is noted. (orig.)

Structure of the optimized effective Kohn-Sham exchange potential and its gradient approximations

International Nuclear Information System (INIS)

Gritsenko, O.; Van Leeuwen, R.; Baerends, E.J.

1996-01-01

An analysis of the structure of the optimized effective Kohn-Sham exchange potential v, and its gradient approximations is presented. The potential is decomposed into the Slater potential v s and the response of v s to density variations, v resp . The latter exhibits peaks that reflect the atomic shell structure. Kohn-Sham exchange potentials derived from current gradient approaches for the exchange energy are shown to be quite reasonable for the Slater potential, but they fail to approximate the response part, which leads to poor overall potentials. Improved potentials are constructed by a direct fit of v x with a gradient-dependent Pade approximant form. The potentials obtained possess proper asymptotic and scaling properties and reproduce the shell structure of the exact v x . 44 refs., 7 figs., 4 tabs

Programming for Sparse Minimax Optimization

DEFF Research Database (Denmark)

Jonasson, K.; Madsen, Kaj

1994-01-01

We present an algorithm for nonlinear minimax optimization which is well suited for large and sparse problems. The method is based on trust regions and sequential linear programming. On each iteration, a linear minimax problem is solved for a basic step. If necessary, this is followed...... by the determination of a minimum norm corrective step based on a first-order Taylor approximation. No Hessian information needs to be stored. Global convergence is proved. This new method has been extensively tested and compared with other methods, including two well known codes for nonlinear programming...

A material optimization model to approximate energy bounds for cellular materials under multiload conditions

DEFF Research Database (Denmark)

Guedes, J.M.; Rodrigues, H.C.; Bendsøe, Martin P.

2003-01-01

This paper describes a computational model, based on inverse homogenization and topology design, for approximating energy bounds for two-phase composites under multiple load cases. The approach allows for the identification of possible single-scale cellular materials that give rise to the optimal...

The Bacterial Sequential Markov Coalescent.

Science.gov (United States)

De Maio, Nicola; Wilson, Daniel J

2017-05-01

Bacteria can exchange and acquire new genetic material from other organisms directly and via the environment. This process, known as bacterial recombination, has a strong impact on the evolution of bacteria, for example, leading to the spread of antibiotic resistance across clades and species, and to the avoidance of clonal interference. Recombination hinders phylogenetic and transmission inference because it creates patterns of substitutions (homoplasies) inconsistent with the hypothesis of a single evolutionary tree. Bacterial recombination is typically modeled as statistically akin to gene conversion in eukaryotes, i.e. , using the coalescent with gene conversion (CGC). However, this model can be very computationally demanding as it needs to account for the correlations of evolutionary histories of even distant loci. So, with the increasing popularity of whole genome sequencing, the need has emerged for a faster approach to model and simulate bacterial genome evolution. We present a new model that approximates the coalescent with gene conversion: the bacterial sequential Markov coalescent (BSMC). Our approach is based on a similar idea to the sequential Markov coalescent (SMC)-an approximation of the coalescent with crossover recombination. However, bacterial recombination poses hurdles to a sequential Markov approximation, as it leads to strong correlations and linkage disequilibrium across very distant sites in the genome. Our BSMC overcomes these difficulties, and shows a considerable reduction in computational demand compared to the exact CGC, and very similar patterns in simulated data. We implemented our BSMC model within new simulation software FastSimBac. In addition to the decreased computational demand compared to previous bacterial genome evolution simulators, FastSimBac provides more general options for evolutionary scenarios, allowing population structure with migration, speciation, population size changes, and recombination hotspots. FastSimBac is

Optimisation of beryllium-7 gamma analysis following BCR sequential extraction

Energy Technology Data Exchange (ETDEWEB)

Taylor, A. [Plymouth University, School of Geography, Earth and Environmental Sciences, 8 Kirkby Place, Plymouth PL4 8AA (United Kingdom); Blake, W.H., E-mail: wblake@plymouth.ac.uk [Plymouth University, School of Geography, Earth and Environmental Sciences, 8 Kirkby Place, Plymouth PL4 8AA (United Kingdom); Keith-Roach, M.J. [Plymouth University, School of Geography, Earth and Environmental Sciences, 8 Kirkby Place, Plymouth PL4 8AA (United Kingdom); Kemakta Konsult, Stockholm (Sweden)

2012-03-30

Graphical abstract: Showing decrease in analytical uncertainty using the optimal (combined preconcentrated sample extract) method. nv (no value) where extract activities were Sequential extraction with natural {sup 7}Be returns high analytical uncertainties. Black-Right-Pointing-Pointer Preconcentrating extracts from a large sample mass improved analytical uncertainty. Black-Right-Pointing-Pointer This optimised method can be readily employed in studies using low activity samples. - Abstract: The application of cosmogenic {sup 7}Be as a sediment tracer at the catchment-scale requires an understanding of its geochemical associations in soil to underpin the assumption of irreversible adsorption. Sequential extractions offer a readily accessible means of determining the associations of {sup 7}Be with operationally defined soil phases. However, the subdivision of the low activity concentrations of fallout {sup 7}Be in soils into geochemical fractions can introduce high gamma counting uncertainties. Extending analysis time significantly is not always an option for batches of samples, owing to the on-going decay of {sup 7}Be (t{sub 1/2} = 53.3 days). Here, three different methods of preparing and quantifying {sup 7}Be extracted using the optimised BCR three-step scheme have been evaluated and compared with a focus on reducing analytical uncertainties. The optimal method involved carrying out the BCR extraction in triplicate, sub-sampling each set of triplicates for stable Be analysis before combining each set and coprecipitating the {sup 7}Be with metal oxyhydroxides to produce a thin source for gamma analysis. This method was applied to BCR extractions of natural {sup 7}Be in four agricultural soils. The approach gave good counting statistics from a 24 h analysis period ({approx}10% (2

Existence and discrete approximation for optimization problems governed by fractional differential equations

Science.gov (United States)

Bai, Yunru; Baleanu, Dumitru; Wu, Guo-Cheng

2018-06-01

We investigate a class of generalized differential optimization problems driven by the Caputo derivative. Existence of weak Carathe ´odory solution is proved by using Weierstrass existence theorem, fixed point theorem and Filippov implicit function lemma etc. Then a numerical approximation algorithm is introduced, and a convergence theorem is established. Finally, a nonlinear programming problem constrained by the fractional differential equation is illustrated and the results verify the validity of the algorithm.

Optimization of vehicle compartment low frequency noise based on Radial Basis Function Neuro-Network Approximation Model

Directory of Open Access Journals (Sweden)

HU Qi-guo

2017-01-01

Full Text Available For reducing the vehicle compartment low frequency noise, the Optimal Latin hypercube sampling method was applied to perform experimental design for sampling in the factorial design space. The thickness parameters of the panels with larger acoustic contribution was considered as factors, as well as the vehicle mass, seventh rank modal frequency of body, peak sound pressure of test point and sound pressure root-mean-square value as responses. By using the RBF(radial basis function neuro-network method, an approximation model of four responses about six factors was established. Further more, error analysis of established approximation model was performed in this paper. To optimize the panel’s thickness parameter, the adaptive simulated annealing algorithm was im-plemented. Optimization results show that the peak sound pressure of driver’s head was reduced by 4.45dB and 5.47dB at frequency 158HZ and 134Hz respec-tively. The test point pressure were significantly reduced at other frequency as well. The results indicate that through the optimization the vehicle interior cavity noise was reduced effectively, and the acoustical comfort of the vehicle was im-proved significantly.

Approximate ideal multi-objective solution Q(λ) learning for optimal carbon-energy combined-flow in multi-energy power systems

International Nuclear Information System (INIS)

Zhang, Xiaoshun; Yu, Tao; Yang, Bo; Zheng, Limin; Huang, Linni

2015-01-01

Highlights: • A novel optimal carbon-energy combined-flow (OCECF) model is firstly established. • A novel approximate ideal multi-objective solution Q(λ) learning is designed. • The proposed algorithm has a high convergence stability and reliability. • The proposed algorithm can be applied for OCECF in a large-scale power grid. - Abstract: This paper proposes a novel approximate ideal multi-objective solution Q(λ) learning for optimal carbon-energy combined-flow in multi-energy power systems. The carbon emissions, fuel cost, active power loss, voltage deviation and carbon emission loss are chosen as the optimization objectives, which are simultaneously optimized by five different Q-value matrices. The dynamic optimal weight of each objective is calculated online from the entire Q-value matrices such that the greedy action policy can be obtained. Case studies are carried out to evaluate the optimization performance for carbon-energy combined-flow in an IEEE 118-bus system and the regional power grid of southern China.

WE-AB-209-10: Optimizing the Delivery of Sequential Fluence Maps for Efficient VMAT Delivery

Energy Technology Data Exchange (ETDEWEB)

Craft, D [Massachusetts General Hospital, Cambridge, MA (United States); Balvert, M [Tilburg University, Tilburg (Netherlands)

2016-06-15

Purpose: To develop an optimization model and solution approach for computing MLC leaf trajectories and dose rates for high quality matching of a set of optimized fluence maps to be delivered sequentially around a patient in a VMAT treatment. Methods: We formulate the fluence map matching problem as a nonlinear optimization problem where time is discretized but dose rates and leaf positions are continuous variables. For a given allotted time, which is allocated across the fluence maps based on the complexity of each fluence map, the optimization problem searches for the best leaf trajectories and dose rates such that the original fluence maps are closely recreated. Constraints include maximum leaf speed, maximum dose rate, and leaf collision avoidance, as well as the constraint that the ending leaf positions for one map are the starting leaf positions for the next map. The resulting model is non-convex but smooth, and therefore we solve it by local searches from a variety of starting positions. We improve solution time by a custom decomposition approach which allows us to decouple the rows of the fluence maps and solve each leaf pair individually. This decomposition also makes the problem easily parallelized. Results: We demonstrate method on a prostate case and a head-and-neck case and show that one can recreate fluence maps to high degree of fidelity in modest total delivery time (minutes). Conclusion: We present a VMAT sequencing method that reproduces optimal fluence maps by searching over a vast number of possible leaf trajectories. By varying the total allotted time given, this approach is the first of its kind to allow users to produce VMAT solutions that span the range of wide-field coarse VMAT deliveries to narrow-field high-MU sliding window-like approaches.

Large-scale sequential quadratic programming algorithms

Energy Technology Data Exchange (ETDEWEB)

Eldersveld, S.K.

1992-09-01

The problem addressed is the general nonlinear programming problem: finding a local minimizer for a nonlinear function subject to a mixture of nonlinear equality and inequality constraints. The methods studied are in the class of sequential quadratic programming (SQP) algorithms, which have previously proved successful for problems of moderate size. Our goal is to devise an SQP algorithm that is applicable to large-scale optimization problems, using sparse data structures and storing less curvature information but maintaining the property of superlinear convergence. The main features are: 1. The use of a quasi-Newton approximation to the reduced Hessian of the Lagrangian function. Only an estimate of the reduced Hessian matrix is required by our algorithm. The impact of not having available the full Hessian approximation is studied and alternative estimates are constructed. 2. The use of a transformation matrix Q. This allows the QP gradient to be computed easily when only the reduced Hessian approximation is maintained. 3. The use of a reduced-gradient form of the basis for the null space of the working set. This choice of basis is more practical than an orthogonal null-space basis for large-scale problems. The continuity condition for this choice is proven. 4. The use of incomplete solutions of quadratic programming subproblems. Certain iterates generated by an active-set method for the QP subproblem are used in place of the QP minimizer to define the search direction for the nonlinear problem. An implementation of the new algorithm has been obtained by modifying the code MINOS. Results and comparisons with MINOS and NPSOL are given for the new algorithm on a set of 92 test problems.

Optimized random phase approximation for the structure of liquid alkali metals as electron-ion plasmas

International Nuclear Information System (INIS)

Senatore, G.; Tosi, M.P.; Trieste Univ.

1981-08-01

The purpose of this letter is to stress that the way towards an unconventional optimized-random-phase-approximation (ORPA) approach to the structure of liquid metals is indicated, and in fact already a good first-order solution for such an approach is provided

Parallel algorithms for unconstrained optimization by multisplitting with inexact subspace search - the abstract

Energy Technology Data Exchange (ETDEWEB)

Renaut, R.; He, Q. [Arizona State Univ., Tempe, AZ (United States)

1994-12-31

In a new parallel iterative algorithm for unconstrained optimization by multisplitting is proposed. In this algorithm the original problem is split into a set of small optimization subproblems which are solved using well known sequential algorithms. These algorithms are iterative in nature, e.g. DFP variable metric method. Here the authors use sequential algorithms based on an inexact subspace search, which is an extension to the usual idea of an inexact fine search. Essentially the idea of the inexact line search for nonlinear minimization is that at each iteration the authors only find an approximate minimum in the line search direction. Hence by inexact subspace search, they mean that, instead of finding the minimum of the subproblem at each interation, they do an incomplete down hill search to give an approximate minimum. Some convergence and numerical results for this algorithm will be presented. Further, the original theory will be generalized to the situation with a singular Hessian. Applications for nonlinear least squares problems will be presented. Experimental results will be presented for implementations on an Intel iPSC/860 Hypercube with 64 nodes as well as on the Intel Paragon.

Exact constants in approximation theory

CERN Document Server

Korneichuk, N

1991-01-01

This book is intended as a self-contained introduction for non-specialists, or as a reference work for experts, to the particular area of approximation theory that is concerned with exact constants. The results apply mainly to extremal problems in approximation theory, which in turn are closely related to numerical analysis and optimization. The book encompasses a wide range of questions and problems: best approximation by polynomials and splines; linear approximation methods, such as spline-approximation; optimal reconstruction of functions and linear functionals. Many of the results are base

The choice of optimal Discrete Interaction Approximation to the kinetic integral for ocean waves

Directory of Open Access Journals (Sweden)

V. G. Polnikov

2003-01-01

Full Text Available A lot of discrete configurations for the four-wave nonlinear interaction processes have been calculated and tested by the method proposed earlier in the frame of the concept of Fast Discrete Interaction Approximation to the Hasselmann's kinetic integral (Polnikov and Farina, 2002. It was found that there are several simple configurations, which are more efficient than the one proposed originally in Hasselmann et al. (1985. Finally, the optimal multiple Discrete Interaction Approximation (DIA to the kinetic integral for deep-water waves was found. Wave spectrum features have been intercompared for a number of different configurations of DIA, applied to a long-time solution of kinetic equation. On the basis of this intercomparison the better efficiency of the configurations proposed was confirmed. Certain recommendations were given for implementation of new approximations to the wave forecast practice.

STABILIZED SEQUENTIAL QUADRATIC PROGRAMMING: A SURVEY

Directory of Open Access Journals (Sweden)

Damián Fernández

2014-12-01

Full Text Available We review the motivation for, the current state-of-the-art in convergence results, and some open questions concerning the stabilized version of the sequential quadratic programming algorithm for constrained optimization. We also discuss the tools required for its local convergence analysis, globalization challenges, and extentions of the method to the more general variational problems.

Spacecraft Trajectory Generation by Successive Approximation for Powered Descent and Cyclers

Science.gov (United States)

Casoliva, Jordi

Methods for spacecraft trajectory generation must be reliable. Complex nonlinear dynamics and constraints impede straightforward approaches. The approach pursued in this dissertation is to use successive approximation, which entails solving a sequence of problems, each one of which can be solved reliably, leading to the solution of the problem of interest. First, contractive sequential convex programming (CSCP) is developed and then applied to the problem of optimal powered descent landing in the presence of complex constraints, aerodynamic force and nonlinear engine performance. Second, numerical continuation is applied to the generation of cycler (periodic) spacecraft trajectories in the Earth-Moon system, the challenge here being the multiple scales of the three-body dynamics. The first-order necessary conditions for minimum-fuel powered descent are derived and interpreted. Both a point-mass model with throttle and thrust angle control and a rigid-body model with throttle and angular velocity control are considered, with a more complete analysis of the rigid-body case than previously available in the literature. The effects of boundary conditions on the thrust direction and finite bounds on the angular velocities are analyzed for the rigid-body case. Minimum-fuel solutions, obtained numerically, illustrate the optimal strategies. The optimal powered descent landing problem considered in the development of CSCP has a convex cost function, nonlinear dynamics, convex state constraints and nonlinear non-convex control constraints. The non-convexity in the control constraints is handled with the lossless convexification technique which consists of a convex relaxation on the control constraints. The novelty of CSCP is the ability to account for nonlinear dynamics and nonlinear control bounds in the optimal control problem and the use of interior-point methods for second-order cone programs which are guaranteed to find the optimal solution. CSCP solves a convergent

A frozen Gaussian approximation-based multi-level particle swarm optimization for seismic inversion

Energy Technology Data Exchange (ETDEWEB)

Li, Jinglai, E-mail: jinglaili@sjtu.edu.cn [Institute of Natural Sciences, Department of Mathematics, and MOE Key Laboratory of Scientific and Engineering Computing, Shanghai Jiao Tong University, Shanghai 200240 (China); Lin, Guang, E-mail: lin491@purdue.edu [Department of Mathematics, School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907 (United States); Computational Sciences and Mathematics Division, Pacific Northwest National Laboratory, Richland, WA 99352 (United States); Yang, Xu, E-mail: xuyang@math.ucsb.edu [Department of Mathematics, University of California, Santa Barbara, CA 93106 (United States)

2015-09-01

In this paper, we propose a frozen Gaussian approximation (FGA)-based multi-level particle swarm optimization (MLPSO) method for seismic inversion of high-frequency wave data. The method addresses two challenges in it: First, the optimization problem is highly non-convex, which makes hard for gradient-based methods to reach global minima. This is tackled by MLPSO which can escape from undesired local minima. Second, the character of high-frequency of seismic waves requires a large number of grid points in direct computational methods, and thus renders an extremely high computational demand on the simulation of each sample in MLPSO. We overcome this difficulty by three steps: First, we use FGA to compute high-frequency wave propagation based on asymptotic analysis on phase plane; Then we design a constrained full waveform inversion problem to prevent the optimization search getting into regions of velocity where FGA is not accurate; Last, we solve the constrained optimization problem by MLPSO that employs FGA solvers with different fidelity. The performance of the proposed method is demonstrated by a two-dimensional full-waveform inversion example of the smoothed Marmousi model.

Management of a stage-structured insect pest: an application of approximate optimization.

Science.gov (United States)

Hackett, Sean C; Bonsall, Michael B

2018-06-01

Ecological decision problems frequently require the optimization of a sequence of actions over time where actions may have both immediate and downstream effects. Dynamic programming can solve such problems only if the dimensionality is sufficiently low. Approximate dynamic programming (ADP) provides a suite of methods applicable to problems of arbitrary complexity at the expense of guaranteed optimality. The most easily generalized method is the look-ahead policy: a brute-force algorithm that identifies reasonable actions by constructing and solving a series of temporally truncated approximations of the full problem over a defined planning horizon. We develop and apply this approach to a pest management problem inspired by the Mediterranean fruit fly, Ceratitis capitata. The model aims to minimize the cumulative costs of management actions and medfly-induced losses over a single 16-week season. The medfly population is stage-structured and grows continuously while management decisions are made at discrete, weekly intervals. For each week, the model chooses between inaction, insecticide application, or one of six sterile insect release ratios. Look-ahead policy performance is evaluated over a range of planning horizons, two levels of crop susceptibility to medfly and three levels of pesticide persistence. In all cases, the actions proposed by the look-ahead policy are contrasted to those of a myopic policy that minimizes costs over only the current week. We find that look-ahead policies always out-performed a myopic policy and decision quality is sensitive to the temporal distribution of costs relative to the planning horizon: it is beneficial to extend the planning horizon when it excludes pertinent costs. However, longer planning horizons may reduce decision quality when major costs are resolved imminently. ADP methods such as the look-ahead-policy-based approach developed here render questions intractable to dynamic programming amenable to inference but should be

Effects of simultaneous and optimized sequential cardiac resynchronization therapy on myocardial oxidative metabolism and efficiency.

Science.gov (United States)

Christenson, Stuart D; Chareonthaitawee, Panithaya; Burnes, John E; Hill, Michael R S; Kemp, Brad J; Khandheria, Bijoy K; Hayes, David L; Gibbons, Raymond J

2008-02-01

Cardiac resynchronization therapy (CRT) can improve left ventricular (LV) hemodynamics and function. Recent data suggest the energy cost of such improvement is favorable. The effects of sequential CRT on myocardial oxidative metabolism (MVO(2)) and efficiency have not been previously assessed. Eight patients with NYHA class III heart failure were studied 196 +/- 180 days after CRT implant. Dynamic [(11)C]acetate positron emission tomography (PET) and echocardiography were performed after 1 hour of: 1) AAI pacing, 2) simultaneous CRT, and 3) sequential CRT. MVO(2) was calculated using the monoexponential clearance rate of [(11)C]acetate (k(mono)). Myocardial efficiency was expressed in terms of the work metabolic index (WMI). P values represent overall significance from repeated measures analysis. Global LV and right ventricular (RV) MVO(2) were not significantly different between pacing modes, but the septal/lateral MVO(2) ratio differed significantly with the change in pacing mode (AAI pacing = 0.696 +/- 0.094 min(-1), simultaneous CRT = 0.975 +/- 0.143 min(-1), and sequential CRT = 0.938 +/- 0.189 min(-1); overall P = 0.001). Stroke volume index (SVI) (AAI pacing = 26.7 +/- 10.4 mL/m(2), simultaneous CRT = 30.6 +/- 11.2 mL/m(2), sequential CRT = 33.5 +/- 12.2 mL/m(2); overall P simultaneous CRT = 4.29 +/- 1.72 mmHg*mL/m(2)*10(6), sequential CRT = 4.79 +/- 1.92 mmHg*mL/m(2)*10(6); overall P = 0.002) also differed between pacing modes. Compared with simultaneous CRT, additional changes in septal/lateral MVO(2), SVI, and WMI with sequential CRT were not statistically significant on post hoc analysis. In this small selected population, CRT increases LV SVI without increasing MVO(2), resulting in improved myocardial efficiency. Additional improvements in LV work, oxidative metabolism, and efficiency from simultaneous to sequential CRT were not significant.

Strong approximations and sequential change-point analysis for diffusion processes

DEFF Research Database (Denmark)

Mihalache, Stefan-Radu

2012-01-01

In this paper ergodic diffusion processes depending on a parameter in the drift are considered under the assumption that the processes can be observed continuously. Strong approximations by Wiener processes for a stochastic integral and for the estimator process constructed by the one...

Comparison of Clenshaw–Curtis and Leja Quasi-Optimal Sparse Grids for the Approximation of Random PDEs

KAUST Repository

Nobile, Fabio; Tamellini, Lorenzo; Tempone, Raul

2015-01-01

In this work we compare different families of nested quadrature points, i.e. the classic Clenshaw–Curtis and various kinds of Leja points, in the context of the quasi-optimal sparse grid approximation of random elliptic PDEs. Numerical evidence

Sequential and parallel image restoration: neural network implementations.

Science.gov (United States)

Figueiredo, M T; Leitao, J N

1994-01-01

Sequential and parallel image restoration algorithms and their implementations on neural networks are proposed. For images degraded by linear blur and contaminated by additive white Gaussian noise, maximum a posteriori (MAP) estimation and regularization theory lead to the same high dimension convex optimization problem. The commonly adopted strategy (in using neural networks for image restoration) is to map the objective function of the optimization problem into the energy of a predefined network, taking advantage of its energy minimization properties. Departing from this approach, we propose neural implementations of iterative minimization algorithms which are first proved to converge. The developed schemes are based on modified Hopfield (1985) networks of graded elements, with both sequential and parallel updating schedules. An algorithm supported on a fully standard Hopfield network (binary elements and zero autoconnections) is also considered. Robustness with respect to finite numerical precision is studied, and examples with real images are presented.

Topology optimization of induction heating model using sequential linear programming based on move limit with adaptive relaxation

Science.gov (United States)

Masuda, Hiroshi; Kanda, Yutaro; Okamoto, Yoshifumi; Hirono, Kazuki; Hoshino, Reona; Wakao, Shinji; Tsuburaya, Tomonori

2017-12-01

It is very important to design electrical machineries with high efficiency from the point of view of saving energy. Therefore, topology optimization (TO) is occasionally used as a design method for improving the performance of electrical machinery under the reasonable constraints. Because TO can achieve a design with much higher degree of freedom in terms of structure, there is a possibility for deriving the novel structure which would be quite different from the conventional structure. In this paper, topology optimization using sequential linear programming using move limit based on adaptive relaxation is applied to two models. The magnetic shielding, in which there are many local minima, is firstly employed as firstly benchmarking for the performance evaluation among several mathematical programming methods. Secondly, induction heating model is defined in 2-D axisymmetric field. In this model, the magnetic energy stored in the magnetic body is maximized under the constraint on the volume of magnetic body. Furthermore, the influence of the location of the design domain on the solutions is investigated.

Event-Triggered Distributed Approximate Optimal State and Output Control of Affine Nonlinear Interconnected Systems.

Science.gov (United States)

Narayanan, Vignesh; Jagannathan, Sarangapani

2017-06-08

This paper presents an approximate optimal distributed control scheme for a known interconnected system composed of input affine nonlinear subsystems using event-triggered state and output feedback via a novel hybrid learning scheme. First, the cost function for the overall system is redefined as the sum of cost functions of individual subsystems. A distributed optimal control policy for the interconnected system is developed using the optimal value function of each subsystem. To generate the optimal control policy, forward-in-time, neural networks are employed to reconstruct the unknown optimal value function at each subsystem online. In order to retain the advantages of event-triggered feedback for an adaptive optimal controller, a novel hybrid learning scheme is proposed to reduce the convergence time for the learning algorithm. The development is based on the observation that, in the event-triggered feedback, the sampling instants are dynamic and results in variable interevent time. To relax the requirement of entire state measurements, an extended nonlinear observer is designed at each subsystem to recover the system internal states from the measurable feedback. Using a Lyapunov-based analysis, it is demonstrated that the system states and the observer errors remain locally uniformly ultimately bounded and the control policy converges to a neighborhood of the optimal policy. Simulation results are presented to demonstrate the performance of the developed controller.

Approximate analytical solution of diffusion equation with fractional time derivative using optimal homotopy analysis method

Directory of Open Access Journals (Sweden)

S. Das

2013-12-01

Full Text Available In this article, optimal homotopy-analysis method is used to obtain approximate analytic solution of the time-fractional diffusion equation with a given initial condition. The fractional derivatives are considered in the Caputo sense. Unlike usual Homotopy analysis method, this method contains at the most three convergence control parameters which describe the faster convergence of the solution. Effects of parameters on the convergence of the approximate series solution by minimizing the averaged residual error with the proper choices of parameters are calculated numerically and presented through graphs and tables for different particular cases.

Sequential Optimization Methods for Augmentation of Marine Enzymes Production in Solid-State Fermentation: l-Glutaminase Production a Case Study.

Science.gov (United States)

Sathish, T; Uppuluri, K B; Veera Bramha Chari, P; Kezia, D

There is an increased l-glutaminase market worldwide due to its relevant industrial applications. Salt tolerance l-glutaminases play a vital role in the increase of flavor of different types of foods like soya sauce and tofu. This chapter is presenting the economically viable l-glutaminases production in solid-state fermentation (SSF) by Aspergillus flavus MTCC 9972 as a case study. The enzyme production was improved following a three step optimization process. Initially mixture design (MD) (augmented simplex lattice design) was employed to optimize the solid substrate mixture. Such solid substrate mixture consisted of 59:41 of wheat bran and Bengal gram husk has given higher amounts of l-glutaminase. Glucose and l-glutamine were screened as a finest additional carbon and nitrogen sources for l-glutaminase production with help of Plackett-Burman Design (PBD). l-Glutamine also acting as a nitrogen source as well as inducer for secretion of l-glutaminase from A. flavus MTCC 9972. In the final step of optimization various environmental and nutritive parameters such as pH, temperature, moisture content, inoculum concentration, glucose, and l-glutamine levels were optimized through the use of hybrid feed forward neural networks (FFNNs) and genetic algorithm (GA). Through sequential optimization methods MD-PBD-FFNN-GA, the l-glutaminase production in SSF could be improved by 2.7-fold (453-1690U/g). © 2016 Elsevier Inc. All rights reserved.

Optimal Sales Schemes for Network Goods

DEFF Research Database (Denmark)

Parakhonyak, Alexei; Vikander, Nick

consumers simultaneously, serve them all sequentially, or employ any intermediate scheme. We show that the optimal sales scheme is purely sequential, where each consumer observes all previous sales before choosing whether to buy himself. A sequential scheme maximizes the amount of information available...

Sequential determination of important ecotoxic radionuclides in nuclear waste samples

International Nuclear Information System (INIS)

Bilohuscin, J.

2016-01-01

In the dissertation thesis we focused on the development and optimization of a sequential determination method for radionuclides 93 Zr, 94 Nb, 99 Tc and 126 Sn, employing extraction chromatography sorbents TEVA (R) Resin and Anion Exchange Resin, supplied by Eichrom Industries. Prior to the attestation of sequential separation of these proposed radionuclides from radioactive waste samples, a unique sequential procedure of 90 Sr, 239 Pu, 241 Am separation from urine matrices was tried, using molecular recognition sorbents of AnaLig (R) series and extraction chromatography sorbent DGA (R) Resin. On these experiments, four various sorbents were continually used for separation, including PreFilter Resin sorbent, which removes interfering organic materials present in raw urine. After the acquisition of positive results of this sequential procedure followed experiments with a 126 Sn separation using TEVA (R) Resin and Anion Exchange Resin sorbents. Radiochemical recoveries obtained from samples of radioactive evaporate concentrates and sludge showed high efficiency of the separation, while values of 126 Sn were under the minimum detectable activities MDA. Activity of 126 Sn was determined after ingrowth of daughter nuclide 126m Sb on HPGe gamma detector, with minimal contamination of gamma interfering radionuclides with decontamination factors (D f ) higher then 1400 for 60 Co and 47000 for 137 Cs. Based on the acquired experiments and results of these separation procedures, a complex method of sequential separation of 93 Zr, 94 Nb, 99 Tc and 126 Sn was proposed, which included optimization steps similar to those used in previous parts of the dissertation work. Application of the sequential separation method for sorbents TEVA (R) Resin and Anion Exchange Resin on real samples of radioactive wastes provided satisfactory results and an economical, time sparing, efficient method. (author)

Piecewise linear approximation: application to control rod step counting in a nuclear reactor core and image contours characterization

International Nuclear Information System (INIS)

Kaoutar, M.

1986-09-01

After a survey of main algorithms for piecewise linear approximation, a new method is suggested. It consists of two stages: a sequential detection stage and an optimization stage, which derives from general dynamic clustering principle. It is applied to control rod step counting in a nuclear reactor core and images contours characterization. Another version of our method is presented. Its originality cames from the variability of the line segments number during iterations. A comparative study is made by comparing the results of the proposed method with of another methods already existing thereby it attests the efficiency and reliability of our method [fr

Quantum chromodynamics as the sequential fragmenting with inactivation

International Nuclear Information System (INIS)

Botet, R.

1996-01-01

We investigate the relation between the modified leading log approximation of the perturbative QCD and the sequential binary fragmentation process. We will show that in the absence of inactivation, this process is equivalent to the QCD gluodynamics. The inactivation term yields a precise prescription of how to include the hadronization in the QCD equations. (authors)

A sequential/parallel track selector

CERN Document Server

Bertolino, F; Bressani, Tullio; Chiavassa, E; Costa, S; Dellacasa, G; Gallio, M; Musso, A

1980-01-01

A medium speed ( approximately 1 mu s) hardware pre-analyzer for the selection of events detected in four planes of drift chambers in the magnetic field of the Omicron Spectrometer at the CERN SC is described. Specific geometrical criteria determine patterns of hits in the four planes of vertical wires that have to be recognized and that are stored as patterns of '1's in random access memories. Pairs of good hits are found sequentially, then the RAMs are used as look-up tables. (6 refs).

The pursuit of balance in sequential randomized trials

Directory of Open Access Journals (Sweden)

Raymond P. Guiteras

2016-06-01

Full Text Available In many randomized trials, subjects enter the sample sequentially. Because the covariates for all units are not known in advance, standard methods of stratification do not apply. We describe and assess the method of DA-optimal sequential allocation (Atkinson, 1982 for balancing stratification covariates across treatment arms. We provide simulation evidence that the method can provide substantial improvements in precision over commonly employed alternatives. We also describe our experience implementing the method in a field trial of a clean water and handwashing intervention in Dhaka, Bangladesh, the first time the method has been used. We provide advice and software for future researchers.

Comparison of Clenshaw–Curtis and Leja Quasi-Optimal Sparse Grids for the Approximation of Random PDEs

KAUST Repository

Nobile, Fabio

2015-11-26

In this work we compare different families of nested quadrature points, i.e. the classic Clenshaw–Curtis and various kinds of Leja points, in the context of the quasi-optimal sparse grid approximation of random elliptic PDEs. Numerical evidence suggests that both families perform comparably within such framework.

Quantum chromodynamics as the sequential fragmenting with inactivation

Energy Technology Data Exchange (ETDEWEB)

Botet, R. [Paris-11 Univ., 91 - Orsay (France). Lab. de Physique des Solides; Ploszajczak, M. [Grand Accelerateur National d`Ions Lourds (GANIL), 14 - Caen (France)

1996-12-31

We investigate the relation between the modified leading log approximation of the perturbative QCD and the sequential binary fragmentation process. We will show that in the absence of inactivation, this process is equivalent to the QCD gluodynamics. The inactivation term yields a precise prescription of how to include the hadronization in the QCD equations. (authors). 15 refs.

Convergence of quasi-optimal sparse-grid approximation of Hilbert-space-valued functions: application to random elliptic PDEs

KAUST Repository

Nobile, F.; Tamellini, L.; Tempone, Raul

2015-01-01

In this work we provide a convergence analysis for the quasi-optimal version of the sparse-grids stochastic collocation method we presented in a previous work: “On the optimal polynomial approximation of stochastic PDEs by Galerkin and collocation methods” (Beck et al., Math Models Methods Appl Sci 22(09), 2012). The construction of a sparse grid is recast into a knapsack problem: a profit is assigned to each hierarchical surplus and only the most profitable ones are added to the sparse grid. The convergence rate of the sparse grid approximation error with respect to the number of points in the grid is then shown to depend on weighted summability properties of the sequence of profits. This is a very general argument that can be applied to sparse grids built with any uni-variate family of points, both nested and non-nested. As an example, we apply such quasi-optimal sparse grids to the solution of a particular elliptic PDE with stochastic diffusion coefficients, namely the “inclusions problem”: we detail the convergence estimates obtained in this case using polynomial interpolation on either nested (Clenshaw–Curtis) or non-nested (Gauss–Legendre) abscissas, verify their sharpness numerically, and compare the performance of the resulting quasi-optimal grids with a few alternative sparse-grid construction schemes recently proposed in the literature.

Convergence of quasi-optimal sparse-grid approximation of Hilbert-space-valued functions: application to random elliptic PDEs

KAUST Repository

Nobile, F.

2015-10-30

In this work we provide a convergence analysis for the quasi-optimal version of the sparse-grids stochastic collocation method we presented in a previous work: “On the optimal polynomial approximation of stochastic PDEs by Galerkin and collocation methods” (Beck et al., Math Models Methods Appl Sci 22(09), 2012). The construction of a sparse grid is recast into a knapsack problem: a profit is assigned to each hierarchical surplus and only the most profitable ones are added to the sparse grid. The convergence rate of the sparse grid approximation error with respect to the number of points in the grid is then shown to depend on weighted summability properties of the sequence of profits. This is a very general argument that can be applied to sparse grids built with any uni-variate family of points, both nested and non-nested. As an example, we apply such quasi-optimal sparse grids to the solution of a particular elliptic PDE with stochastic diffusion coefficients, namely the “inclusions problem”: we detail the convergence estimates obtained in this case using polynomial interpolation on either nested (Clenshaw–Curtis) or non-nested (Gauss–Legendre) abscissas, verify their sharpness numerically, and compare the performance of the resulting quasi-optimal grids with a few alternative sparse-grid construction schemes recently proposed in the literature.

Sequentially optimized reconstruction strategy: A meta-strategy for perimetry testing.

Directory of Open Access Journals (Sweden)

Şerife Seda Kucur

Full Text Available Perimetry testing is an automated method to measure visual function and is heavily used for diagnosing ophthalmic and neurological conditions. Its working principle is to sequentially query a subject about perceived light using different brightness levels at different visual field locations. At a given location, this query-patient-feedback process is expected to converge at a perceived sensitivity, such that a shown stimulus intensity is observed and reported 50% of the time. Given this inherently time-intensive and noisy process, fast testing strategies are necessary in order to measure existing regions more effectively and reliably. In this work, we present a novel meta-strategy which relies on the correlative nature of visual field locations in order to strongly reduce the necessary number of locations that need to be examined. To do this, we sequentially determine locations that most effectively reduce visual field estimation errors in an initial training phase. We then exploit these locations at examination time and show that our approach can easily be combined with existing perceived sensitivity estimation schemes to speed up the examinations. Compared to state-of-the-art strategies, our approach shows marked performance gains with a better accuracy-speed trade-off regime for both mixed and sub-populations.

Methodology for sensitivity analysis, approximate analysis, and design optimization in CFD for multidisciplinary applications. [computational fluid dynamics

Science.gov (United States)

Taylor, Arthur C., III; Hou, Gene W.

1992-01-01

Fundamental equations of aerodynamic sensitivity analysis and approximate analysis for the two dimensional thin layer Navier-Stokes equations are reviewed, and special boundary condition considerations necessary to apply these equations to isolated lifting airfoils on 'C' and 'O' meshes are discussed in detail. An efficient strategy which is based on the finite element method and an elastic membrane representation of the computational domain is successfully tested, which circumvents the costly 'brute force' method of obtaining grid sensitivity derivatives, and is also useful in mesh regeneration. The issue of turbulence modeling is addressed in a preliminary study. Aerodynamic shape sensitivity derivatives are efficiently calculated, and their accuracy is validated on two viscous test problems, including: (1) internal flow through a double throat nozzle, and (2) external flow over a NACA 4-digit airfoil. An automated aerodynamic design optimization strategy is outlined which includes the use of a design optimization program, an aerodynamic flow analysis code, an aerodynamic sensitivity and approximate analysis code, and a mesh regeneration and grid sensitivity analysis code. Application of the optimization methodology to the two test problems in each case resulted in a new design having a significantly improved performance in the aerodynamic response of interest.

Dependence of Computational Models on Input Dimension: Tractability of Approximation and Optimization Tasks

Czech Academy of Sciences Publication Activity Database

Kainen, P.C.; Kůrková, Věra; Sanguineti, M.

2012-01-01

Roč. 58, č. 2 (2012), s. 1203-1214 ISSN 0018-9448 R&D Projects: GA MŠk(CZ) ME10023; GA ČR GA201/08/1744; GA ČR GAP202/11/1368 Grant - others:CNR-AV ČR(CZ-IT) Project 2010–2012 Complexity of Neural -Network and Kernel Computational Models Institutional research plan: CEZ:AV0Z10300504 Keywords : dictionary-based computational models * high-dimensional approximation and optimization * model complexity * polynomial upper bounds Subject RIV: IN - Informatics, Computer Science Impact factor: 2.621, year: 2012

Hybrid Approximate Dynamic Programming Approach for Dynamic Optimal Energy Flow in the Integrated Gas and Power Systems

DEFF Research Database (Denmark)

Shuai, Hang; Ai, Xiaomeng; Wen, Jinyu

2017-01-01

This paper proposes a hybrid approximate dynamic programming (ADP) approach for the multiple time-period optimal power flow in integrated gas and power systems. ADP successively solves Bellman's equation to make decisions according to the current state of the system. So, the updated near future...

Control of minimum member size in parameter-free structural shape optimization by a medial axis approximation

Science.gov (United States)

Schmitt, Oliver; Steinmann, Paul

2017-09-01

We introduce a manufacturing constraint for controlling the minimum member size in structural shape optimization problems, which is for example of interest for components fabricated in a molding process. In a parameter-free approach, whereby the coordinates of the FE boundary nodes are used as design variables, the challenging task is to find a generally valid definition for the thickness of non-parametric geometries in terms of their boundary nodes. Therefore we use the medial axis, which is the union of all points with at least two closest points on the boundary of the domain. Since the effort for the exact computation of the medial axis of geometries given by their FE discretization highly increases with the number of surface elements we use the distance function instead to approximate the medial axis by a cloud of points. The approximation is demonstrated on three 2D examples. Moreover, the formulation of a minimum thickness constraint is applied to a sensitivity-based shape optimization problem of one 2D and one 3D model.

Sequential optimization of a polygeneration plant

International Nuclear Information System (INIS)

Rubio-Maya, Carlos; Uche, Javier; Martinez, Amaya

2011-01-01

Highlights: → A two-steps optimization procedure of a polygeneration unit was tested. → First step was the synthesis and design; the superstructure definition was used. → Second step optimized the operation with hourly data and energy storage systems. → Remarkable benefits for the analyzed case study (Spanish hotel) were found. - Abstract: This paper presents a two-steps optimization procedure of a polygeneration unit. The unit simultaneously provides power, heat, cooling and fresh water to a Spanish tourist resort (450 rooms). The first step consist on the synthesis and design of the polygeneration scheme: a 'superstructure' was constructed to allow the selection of the appropriate choice and size of the plant components, from both economic and environmental considerations. At that first step, only monthly averaged requirements are considered. The second step includes hourly data and analysis as well as energy storage systems. A detailed modelling of pre-selected devices is then required to also fulfil economic and environmental constraints. As a result, a better performance is obtained compared to the first step. Thus, the two-steps procedure explained here permits the complete design and operation of a decentralized plant producing simultaneously energy (power, heat and cooling) but also desalted water (that is, trigeneration + desalination). Remarkable benefits for the analyzed case study are found: a Net Present Value of almost 300,000 Euro , a primary energy saving ratio of about 18% and more than 850 ton per year of avoided CO 2 emissions.

The discrete-dipole-approximation code ADDA: capabilities and known limitations

NARCIS (Netherlands)

Yurkin, M.A.; Hoekstra, A.G.

2011-01-01

The open-source code ADDA is described, which implements the discrete dipole approximation (DDA), a method to simulate light scattering by finite 3D objects of arbitrary shape and composition. Besides standard sequential execution, ADDA can run on a multiprocessor distributed-memory system,

Eyewitness accuracy rates in sequential and simultaneous lineup presentations: a meta-analytic comparison.

Science.gov (United States)

Steblay, N; Dysart, J; Fulero, S; Lindsay, R C

2001-10-01

Most police lineups use a simultaneous presentation technique in which eyewitnesses view all lineup members at the same time. Lindsay and Wells (R. C. L. Lindsay & G. L. Wells, 1985) devised an alternative procedure, the sequential lineup, in which witnesses view one lineup member at a time and decide whether or not that person is the perpetrator prior to viewing the next lineup member. The present work uses the technique of meta-analysis to compare the accuracy rates of these presentation styles. Twenty-three papers were located (9 published and 14 unpublished), providing 30 tests of the hypothesis and including 4,145 participants. Results showed that identification of perpetrators from target-present lineups occurs at a higher rate from simultaneous than from sequential lineups. However, this difference largely disappears when moderator variables approximating real world conditions are considered. Also, correct rejection rates were significantly higher for sequential than simultaneous lineups and this difference is maintained or increased by greater approximation to real world conditions. Implications of these findings are discussed.

Approximating optimal behavioural strategies down to rules-of-thumb: energy reserve changes in pairs of social foragers.

Directory of Open Access Journals (Sweden)

Sean A Rands

Full Text Available Functional explanations of behaviour often propose optimal strategies for organisms to follow. These 'best' strategies could be difficult to perform given biological constraints such as neural architecture and physiological constraints. Instead, simple heuristics or 'rules-of-thumb' that approximate these optimal strategies may instead be performed. From a modelling perspective, rules-of-thumb are also useful tools for considering how group behaviour is shaped by the behaviours of individuals. Using simple rules-of-thumb reduces the complexity of these models, but care needs to be taken to use rules that are biologically relevant. Here, we investigate the similarity between the outputs of a two-player dynamic foraging game (which generated optimal but complex solutions and a computational simulation of the behaviours of the two members of a foraging pair, who instead followed a rule-of-thumb approximation of the game's output. The original game generated complex results, and we demonstrate here that the simulations following the much-simplified rules-of-thumb also generate complex results, suggesting that the rule-of-thumb was sufficient to make some of the model outcomes unpredictable. There was some agreement between both modelling techniques, but some differences arose - particularly when pair members were not identical in how they gained and lost energy. We argue that exploring how rules-of-thumb perform in comparison to their optimal counterparts is an important exercise for biologically validating the output of agent-based models of group behaviour.

Approximating optimal behavioural strategies down to rules-of-thumb: energy reserve changes in pairs of social foragers.

Science.gov (United States)

Rands, Sean A

2011-01-01

Functional explanations of behaviour often propose optimal strategies for organisms to follow. These 'best' strategies could be difficult to perform given biological constraints such as neural architecture and physiological constraints. Instead, simple heuristics or 'rules-of-thumb' that approximate these optimal strategies may instead be performed. From a modelling perspective, rules-of-thumb are also useful tools for considering how group behaviour is shaped by the behaviours of individuals. Using simple rules-of-thumb reduces the complexity of these models, but care needs to be taken to use rules that are biologically relevant. Here, we investigate the similarity between the outputs of a two-player dynamic foraging game (which generated optimal but complex solutions) and a computational simulation of the behaviours of the two members of a foraging pair, who instead followed a rule-of-thumb approximation of the game's output. The original game generated complex results, and we demonstrate here that the simulations following the much-simplified rules-of-thumb also generate complex results, suggesting that the rule-of-thumb was sufficient to make some of the model outcomes unpredictable. There was some agreement between both modelling techniques, but some differences arose - particularly when pair members were not identical in how they gained and lost energy. We argue that exploring how rules-of-thumb perform in comparison to their optimal counterparts is an important exercise for biologically validating the output of agent-based models of group behaviour.

Optimal Allocation of Power-Electronic Interfaced Wind Turbines Using a Genetic Algorithm - Monte Carlo Hybrid Optimization Method

DEFF Research Database (Denmark)

Chen, Peiyuan; Siano, Pierluigi; Chen, Zhe

2010-01-01

determined by the wind resource and geographic conditions, the location of wind turbines in a power system network may significantly affect the distribution of power flow, power losses, etc. Furthermore, modern WTs with power-electronic interface have the capability of controlling reactive power output...... limit requirements. The method combines the Genetic Algorithm (GA), gradient-based constrained nonlinear optimization algorithm and sequential Monte Carlo simulation (MCS). The GA searches for the optimal locations and capacities of WTs. The gradient-based optimization finds the optimal power factor...... setting of WTs. The sequential MCS takes into account the stochastic behaviour of wind power generation and load. The proposed hybrid optimization method is demonstrated on an 11 kV 69-bus distribution system....

Optimal control of parametric oscillations of compressed flexible bars

Science.gov (United States)

Alesova, I. M.; Babadzanjanz, L. K.; Pototskaya, I. Yu.; Pupysheva, Yu. Yu.; Saakyan, A. T.

2018-05-01

In this paper the problem of damping of the linear systems oscillations with piece-wise constant control is solved. The motion of bar construction is reduced to the form described by Hill's differential equation using the Bubnov-Galerkin method. To calculate switching moments of the one-side control the method of sequential linear programming is used. The elements of the fundamental matrix of the Hill's equation are approximated by trigonometric series. Examples of the optimal control of the systems for various initial conditions and different number of control stages have been calculated. The corresponding phase trajectories and transient processes are represented.

Sequential error concealment for video/images by weighted template matching

DEFF Research Database (Denmark)

Koloda, Jan; Østergaard, Jan; Jensen, Søren Holdt

2012-01-01

In this paper we propose a novel spatial error concealment algorithm for video and images based on convex optimization. Block-based coding schemes in packet loss environment are considered. Missing macro blocks are sequentially reconstructed by filling them with a weighted set of templates...

Optimization of core reload design for low-leakage fuel management in pressurized water reactors

International Nuclear Information System (INIS)

Kim, Y.J.; Downar, T.J.; Sesonske, A.

1987-01-01

A method was developed to optimize pressurized water reactor low-leakage core reload designs that features the decoupling and sequential optimization of the fuel arrangement and control problems. The two-stage optimization process provides the maximum cycle length for a given fresh fuel loading subject to power peaking constraints. In the first stage, a best fuel arrangement is determined at the end of cycle (EOC) in the absence of all control poisons by employing a direct search method. The constant power, Haling depletion is used to provide the cycle length and EOC power peaking for each candidate core fuel arrangement. In the second stage, the core control poison requirements to meet the core peaking constraints throughout the cycle are determined using an approximate nonlinear programming technique

Optimization of the analysis by means of liquid chromatography of metabolites of the Uncaria Tomentosa plant (cat's claw) using the sequential simplex method

International Nuclear Information System (INIS)

Romero Blanco, Eric

2005-01-01

A new method was developed for the analysis using liquid chromatography of the metabolites present in extracts of root bark of Uncaria Tomentosa (cat's claw) by applying the simplex sequential technique to determine the magnitude of the chromatographic variables; i.e. flow, temperature and stationary-phase composition, which allowed the optimizing the elusion time and the resolution of the chromatographic separation. The chromatographic analysis was performed in isocratic mode using a C12 (-urea) column of 15 cm in length and 4,6 mm of diameter and a UV detector. The magnitude of the chromatographic variables that optimized the separation turned out to be: flow of 1.80 mL/min, temperature of 27.5 centigrade and a mobile phase composition of 22:78 (Methanol: to butter). (Author) [es

Crashworthiness design optimization using multipoint sequential linear programming

NARCIS (Netherlands)

Etman, L.F.P.; Adriaens, J.M.T.A.; Slagmaat, van M.T.P.; Schoofs, A.J.G.

1996-01-01

A design optimization tool has been developed for the crash victim simulation software MADYMO. The crash worthiness optimization problem is characterized by a noisy behaviour of objective function and constraints. Additionally, objective function and constraint values follow from a computationally

All-Norm Approximation Algorithms

NARCIS (Netherlands)

Azar, Yossi; Epstein, Leah; Richter, Yossi; Woeginger, Gerhard J.; Penttonen, Martti; Meineche Schmidt, Erik

2002-01-01

A major drawback in optimization problems and in particular in scheduling problems is that for every measure there may be a different optimal solution. In many cases the various measures are different ℓ p norms. We address this problem by introducing the concept of an All-norm ρ-approximation

Robust real-time pattern matching using bayesian sequential hypothesis testing.

Science.gov (United States)

Pele, Ofir; Werman, Michael

2008-08-01

This paper describes a method for robust real time pattern matching. We first introduce a family of image distance measures, the "Image Hamming Distance Family". Members of this family are robust to occlusion, small geometrical transforms, light changes and non-rigid deformations. We then present a novel Bayesian framework for sequential hypothesis testing on finite populations. Based on this framework, we design an optimal rejection/acceptance sampling algorithm. This algorithm quickly determines whether two images are similar with respect to a member of the Image Hamming Distance Family. We also present a fast framework that designs a near-optimal sampling algorithm. Extensive experimental results show that the sequential sampling algorithm performance is excellent. Implemented on a Pentium 4 3 GHz processor, detection of a pattern with 2197 pixels, in 640 x 480 pixel frames, where in each frame the pattern rotated and was highly occluded, proceeds at only 0.022 seconds per frame.

Influence of Sequential vs. Simultaneous Dual-Task Exercise Training on Cognitive Function in Older Adults.

Science.gov (United States)

Tait, Jamie L; Duckham, Rachel L; Milte, Catherine M; Main, Luana C; Daly, Robin M

2017-01-01

Emerging research indicates that exercise combined with cognitive training may improve cognitive function in older adults. Typically these programs have incorporated sequential training, where exercise and cognitive training are undertaken separately. However, simultaneous or dual-task training, where cognitive and/or motor training are performed simultaneously with exercise, may offer greater benefits. This review summary provides an overview of the effects of combined simultaneous vs. sequential training on cognitive function in older adults. Based on the available evidence, there are inconsistent findings with regard to the cognitive benefits of sequential training in comparison to cognitive or exercise training alone. In contrast, simultaneous training interventions, particularly multimodal exercise programs in combination with secondary tasks regulated by sensory cues, have significantly improved cognition in both healthy older and clinical populations. However, further research is needed to determine the optimal characteristics of a successful simultaneous training program for optimizing cognitive function in older people.

Sequential extraction of uranium metal contamination

International Nuclear Information System (INIS)

Murry, M.M.; Spitz, H.B.; Connick, W.B.

2016-01-01

Samples of uranium contaminated dirt collected from the dirt floor of an abandoned metal rolling mill were analyzed for uranium using a sequential extraction protocol involving a series of five increasingly aggressive solvents. The quantity of uranium extracted from the contaminated dirt by each reagent can aid in predicting the fate and transport of the uranium contamination in the environment. Uranium was separated from each fraction using anion exchange, electrodeposition and analyzed by alpha spectroscopy analysis. Results demonstrate that approximately 77 % of the uranium was extracted using NH 4 Ac in 25 % acetic acid. (author)

Approximation by planar elastic curves

DEFF Research Database (Denmark)

Brander, David; Gravesen, Jens; Nørbjerg, Toke Bjerge

2016-01-01

We give an algorithm for approximating a given plane curve segment by a planar elastic curve. The method depends on an analytic representation of the space of elastic curve segments, together with a geometric method for obtaining a good initial guess for the approximating curve. A gradient......-driven optimization is then used to find the approximating elastic curve....

Genetic Spot Optimization for Peak Power Estimation in Large VLSI Circuits

Directory of Open Access Journals (Sweden)

Michael S. Hsiao

2002-01-01

Full Text Available Estimating peak power involves optimization of the circuit's switching function. The switching of a given gate is not only dependent on the output capacitance of the node, but also heavily dependent on the gate delays in the circuit, since multiple switching events can result from uneven circuit delay paths in the circuit. Genetic spot expansion and optimization are proposed in this paper to estimate tight peak power bounds for large sequential circuits. The optimization spot shifts and expands dynamically based on the maximum power potential (MPP of the nodes under optimization. Four genetic spot optimization heuristics are studied for sequential circuits. Experimental results showed an average of 70.7% tighter peak power bounds for large sequential benchmark circuits was achieved in short execution times.

An anomaly detection and isolation scheme with instance-based learning and sequential analysis

International Nuclear Information System (INIS)

Yoo, T. S.; Garcia, H. E.

2006-01-01

This paper presents an online anomaly detection and isolation (FDI) technique using an instance-based learning method combined with a sequential change detection and isolation algorithm. The proposed method uses kernel density estimation techniques to build statistical models of the given empirical data (null hypothesis). The null hypothesis is associated with the set of alternative hypotheses modeling the abnormalities of the systems. A decision procedure involves a sequential change detection and isolation algorithm. Notably, the proposed method enjoys asymptotic optimality as the applied change detection and isolation algorithm is optimal in minimizing the worst mean detection/isolation delay for a given mean time before a false alarm or a false isolation. Applicability of this methodology is illustrated with redundant sensor data set and its performance. (authors)

Obtaining Approximate Values of Exterior Orientation Elements of Multi-Intersection Images Using Particle Swarm Optimization

Science.gov (United States)

Li, X.; Li, S. W.

2012-07-01

In this paper, an efficient global optimization algorithm in the field of artificial intelligence, named Particle Swarm Optimization (PSO), is introduced into close range photogrammetric data processing. PSO can be applied to obtain the approximate values of exterior orientation elements under the condition that multi-intersection photography and a small portable plane control frame are used. PSO, put forward by an American social psychologist J. Kennedy and an electrical engineer R.C. Eberhart, is a stochastic global optimization method based on swarm intelligence, which was inspired by social behavior of bird flocking or fish schooling. The strategy of obtaining the approximate values of exterior orientation elements using PSO is as follows: in terms of image coordinate observed values and space coordinates of few control points, the equations of calculating the image coordinate residual errors can be given. The sum of absolute value of each image coordinate is minimized to be the objective function. The difference between image coordinate observed value and the image coordinate computed through collinear condition equation is defined as the image coordinate residual error. Firstly a gross area of exterior orientation elements is given, and then the adjustment of other parameters is made to get the particles fly in the gross area. After iterative computation for certain times, the satisfied approximate values of exterior orientation elements are obtained. By doing so, the procedures like positioning and measuring space control points in close range photogrammetry can be avoided. Obviously, this method can improve the surveying efficiency greatly and at the same time can decrease the surveying cost. And during such a process, only one small portable control frame with a couple of control points is employed, and there are no strict requirements for the space distribution of control points. In order to verify the effectiveness of this algorithm, two experiments are

OBTAINING APPROXIMATE VALUES OF EXTERIOR ORIENTATION ELEMENTS OF MULTI-INTERSECTION IMAGES USING PARTICLE SWARM OPTIMIZATION

Directory of Open Access Journals (Sweden)

X. Li

2012-07-01

Full Text Available In this paper, an efficient global optimization algorithm in the field of artificial intelligence, named Particle Swarm Optimization (PSO, is introduced into close range photogrammetric data processing. PSO can be applied to obtain the approximate values of exterior orientation elements under the condition that multi-intersection photography and a small portable plane control frame are used. PSO, put forward by an American social psychologist J. Kennedy and an electrical engineer R.C. Eberhart, is a stochastic global optimization method based on swarm intelligence, which was inspired by social behavior of bird flocking or fish schooling. The strategy of obtaining the approximate values of exterior orientation elements using PSO is as follows: in terms of image coordinate observed values and space coordinates of few control points, the equations of calculating the image coordinate residual errors can be given. The sum of absolute value of each image coordinate is minimized to be the objective function. The difference between image coordinate observed value and the image coordinate computed through collinear condition equation is defined as the image coordinate residual error. Firstly a gross area of exterior orientation elements is given, and then the adjustment of other parameters is made to get the particles fly in the gross area. After iterative computation for certain times, the satisfied approximate values of exterior orientation elements are obtained. By doing so, the procedures like positioning and measuring space control points in close range photogrammetry can be avoided. Obviously, this method can improve the surveying efficiency greatly and at the same time can decrease the surveying cost. And during such a process, only one small portable control frame with a couple of control points is employed, and there are no strict requirements for the space distribution of control points. In order to verify the effectiveness of this algorithm

Safeguarding a Lunar Rover with Wald's Sequential Probability Ratio Test

Science.gov (United States)

Furlong, Michael; Dille, Michael; Wong, Uland; Nefian, Ara

2016-01-01

The virtual bumper is a safeguarding mechanism for autonomous and remotely operated robots. In this paper we take a new approach to the virtual bumper system by using an old statistical test. By using a modified version of Wald's sequential probability ratio test we demonstrate that we can reduce the number of false positive reported by the virtual bumper, thereby saving valuable mission time. We use the concept of sequential probability ratio to control vehicle speed in the presence of possible obstacles in order to increase certainty about whether or not obstacles are present. Our new algorithm reduces the chances of collision by approximately 98 relative to traditional virtual bumper safeguarding without speed control.

Comparing two Poisson populations sequentially: an application

International Nuclear Information System (INIS)

Halteman, E.J.

1986-01-01

Rocky Flats Plant in Golden, Colorado monitors each of its employees for radiation exposure. Excess exposure is detected by comparing the means of two Poisson populations. A sequential probability ratio test (SPRT) is proposed as a replacement for the fixed sample normal approximation test. A uniformly most efficient SPRT exists, however logistics suggest using a truncated SPRT. The truncated SPRT is evaluated in detail and shown to possess large potential savings in average time spent by employees in the monitoring process

Sequential blind identification of underdetermined mixtures using a novel deflation scheme.

Science.gov (United States)

Zhang, Mingjian; Yu, Simin; Wei, Gang

2013-09-01

In this brief, we consider the problem of blind identification in underdetermined instantaneous mixture cases, where there are more sources than sensors. A new blind identification algorithm, which estimates the mixing matrix in a sequential fashion, is proposed. By using the rank-1 detecting device, blind identification is reformulated as a constrained optimization problem. The identification of one column of the mixing matrix hence reduces to an optimization task for which an efficient iterative algorithm is proposed. The identification of the other columns of the mixing matrix is then carried out by a generalized eigenvalue decomposition-based deflation method. The key merit of the proposed deflation method is that it does not suffer from error accumulation. The proposed sequential blind identification algorithm provides more flexibility and better robustness than its simultaneous counterpart. Comparative simulation results demonstrate the superior performance of the proposed algorithm over the simultaneous blind identification algorithm.

Multilevel weighted least squares polynomial approximation

KAUST Repository

Haji-Ali, Abdul-Lateef

2017-06-30

Weighted least squares polynomial approximation uses random samples to determine projections of functions onto spaces of polynomials. It has been shown that, using an optimal distribution of sample locations, the number of samples required to achieve quasi-optimal approximation in a given polynomial subspace scales, up to a logarithmic factor, linearly in the dimension of this space. However, in many applications, the computation of samples includes a numerical discretization error. Thus, obtaining polynomial approximations with a single level method can become prohibitively expensive, as it requires a sufficiently large number of samples, each computed with a sufficiently small discretization error. As a solution to this problem, we propose a multilevel method that utilizes samples computed with different accuracies and is able to match the accuracy of single-level approximations with reduced computational cost. We derive complexity bounds under certain assumptions about polynomial approximability and sample work. Furthermore, we propose an adaptive algorithm for situations where such assumptions cannot be verified a priori. Finally, we provide an efficient algorithm for the sampling from optimal distributions and an analysis of computationally favorable alternative distributions. Numerical experiments underscore the practical applicability of our method.

Estimation After a Group Sequential Trial.

Science.gov (United States)

Milanzi, Elasma; Molenberghs, Geert; Alonso, Ariel; Kenward, Michael G; Tsiatis, Anastasios A; Davidian, Marie; Verbeke, Geert

2015-10-01

Group sequential trials are one important instance of studies for which the sample size is not fixed a priori but rather takes one of a finite set of pre-specified values, dependent on the observed data. Much work has been devoted to the inferential consequences of this design feature. Molenberghs et al (2012) and Milanzi et al (2012) reviewed and extended the existing literature, focusing on a collection of seemingly disparate, but related, settings, namely completely random sample sizes, group sequential studies with deterministic and random stopping rules, incomplete data, and random cluster sizes. They showed that the ordinary sample average is a viable option for estimation following a group sequential trial, for a wide class of stopping rules and for random outcomes with a distribution in the exponential family. Their results are somewhat surprising in the sense that the sample average is not optimal, and further, there does not exist an optimal, or even, unbiased linear estimator. However, the sample average is asymptotically unbiased, both conditionally upon the observed sample size as well as marginalized over it. By exploiting ignorability they showed that the sample average is the conventional maximum likelihood estimator. They also showed that a conditional maximum likelihood estimator is finite sample unbiased, but is less efficient than the sample average and has the larger mean squared error. Asymptotically, the sample average and the conditional maximum likelihood estimator are equivalent. This previous work is restricted, however, to the situation in which the the random sample size can take only two values, N = n or N = 2 n . In this paper, we consider the more practically useful setting of sample sizes in a the finite set { n 1 , n 2 , …, n L }. It is shown that the sample average is then a justifiable estimator , in the sense that it follows from joint likelihood estimation, and it is consistent and asymptotically unbiased. We also show why

Simultaneous approximation in scales of Banach spaces

International Nuclear Information System (INIS)

Bramble, J.H.; Scott, R.

1978-01-01

The problem of verifying optimal approximation simultaneously in different norms in a Banach scale is reduced to verification of optimal approximation in the highest order norm. The basic tool used is the Banach space interpolation method developed by Lions and Peetre. Applications are given to several problems arising in the theory of finite element methods

Thermodynamics and structure of liquid metals from a consistent optimized random phase approximation

International Nuclear Information System (INIS)

Akinlade, O.; Badirkhan, Z.; Pastore, G.

2000-05-01

We study thermodynamics and structural properties of several liquid metals to assess the validity of the generalized non-local model potential (GNMP) of Li et. al. [J.Phys. F16,309 (1986)]. By using a new thermodynamically consistent version of the optimized random phase approximation (ORPA), especially adapted to continuous reference potentials, we improve our previous results obtained within the variational approach based on the Gibbs - Bogoliubov inequality. Hinging on the unified and very accurate evaluation of structure factors and thermodynamic quantities provided by the ORPA, we find that the GNMP yields satisfactory results for the alkali metals, however, those for the polyvalent metals point to a substantial inadequacy of the GNMP for high valence systems. (author)

Approximate Dynamic Programming: Combining Regional and Local State Following Approximations.

Science.gov (United States)

Deptula, Patryk; Rosenfeld, Joel A; Kamalapurkar, Rushikesh; Dixon, Warren E

2018-06-01

An infinite-horizon optimal regulation problem for a control-affine deterministic system is solved online using a local state following (StaF) kernel and a regional model-based reinforcement learning (R-MBRL) method to approximate the value function. Unlike traditional methods such as R-MBRL that aim to approximate the value function over a large compact set, the StaF kernel approach aims to approximate the value function in a local neighborhood of the state that travels within a compact set. In this paper, the value function is approximated using a state-dependent convex combination of the StaF-based and the R-MBRL-based approximations. As the state enters a neighborhood containing the origin, the value function transitions from being approximated by the StaF approach to the R-MBRL approach. Semiglobal uniformly ultimately bounded (SGUUB) convergence of the system states to the origin is established using a Lyapunov-based analysis. Simulation results are provided for two, three, six, and ten-state dynamical systems to demonstrate the scalability and performance of the developed method.

Sequential Power-Dependence Theory

NARCIS (Netherlands)

Buskens, Vincent; Rijt, Arnout van de

2008-01-01

Existing methods for predicting resource divisions in laboratory exchange networks do not take into account the sequential nature of the experimental setting. We extend network exchange theory by considering sequential exchange. We prove that Sequential Power-Dependence Theory—unlike

Enhancing product robustness in reliability-based design optimization

International Nuclear Information System (INIS)

Zhuang, Xiaotian; Pan, Rong; Du, Xiaoping

2015-01-01

Different types of uncertainties need to be addressed in a product design optimization process. In this paper, the uncertainties in both product design variables and environmental noise variables are considered. The reliability-based design optimization (RBDO) is integrated with robust product design (RPD) to concurrently reduce the production cost and the long-term operation cost, including quality loss, in the process of product design. This problem leads to a multi-objective optimization with probabilistic constraints. In addition, the model uncertainties associated with a surrogate model that is derived from numerical computation methods, such as finite element analysis, is addressed. A hierarchical experimental design approach, augmented by a sequential sampling strategy, is proposed to construct the response surface of product performance function for finding optimal design solutions. The proposed method is demonstrated through an engineering example. - Highlights: • A unifying framework for integrating RBDO and RPD is proposed. • Implicit product performance function is considered. • The design problem is solved by sequential optimization and reliability assessment. • A sequential sampling technique is developed for improving design optimization. • The comparison with traditional RBDO is provided

Extension of the KLI approximation toward the exact optimized effective potential.

Science.gov (United States)

Iafrate, G J; Krieger, J B

2013-03-07

The integral equation for the optimized effective potential (OEP) is utilized in a compact form from which an accurate OEP solution for the spin-unrestricted exchange-correlation potential, Vxcσ, is obtained for any assumed orbital-dependent exchange-correlation energy functional. The method extends beyond the Krieger-Li-Iafrate (KLI) approximation toward the exact OEP result. The compact nature of the OEP equation arises by replacing the integrals involving the Green's function terms in the traditional OEP equation by an equivalent first-order perturbation theory wavefunction often referred to as the "orbital shift" function. Significant progress is then obtained by solving the equation for the first order perturbation theory wavefunction by use of Dalgarno functions which are determined from well known methods of partial differential equations. The use of Dalgarno functions circumvents the need to explicitly address the Green's functions and the associated problems with "sum over states" numerics; as well, the Dalgarno functions provide ease in dealing with inherent singularities arising from the origin and the zeros of the occupied orbital wavefunctions. The Dalgarno approach for finding a solution to the OEP equation is described herein, and a detailed illustrative example is presented for the special case of a spherically symmetric exchange-correlation potential. For the case of spherical symmetry, the relevant Dalgarno function is derived by direct integration of the appropriate radial equation while utilizing a user friendly method which explicitly treats the singular behavior at the origin and at the nodal singularities arising from the zeros of the occupied states. The derived Dalgarno function is shown to be an explicit integral functional of the exact OEP Vxcσ, thus allowing for the reduction of the OEP equation to a self-consistent integral equation for the exact exchange-correlation potential; the exact solution to this integral equation can be

Sequentially generated states for the study of two dimensional systems

Energy Technology Data Exchange (ETDEWEB)

Banuls, Mari-Carmen; Cirac, J. Ignacio [Max-Planck-Institut fuer Quantenoptik, Garching (Germany); Perez-Garcia, David [Depto. Analisis Matematico, Universidad Complutense de Madrid (Spain); Wolf, Michael M. [Niels Bohr Institut, Copenhagen (Denmark); Verstraete, Frank [Fakultaet fuer Physik, Universitaet Wien (Austria)

2009-07-01

The family of Matrix Product States represents a powerful tool for the study of physical one-dimensional quantum many-body systems, such as spin chains. Besides, Matrix Product States can be defined as the family of quantum states that can be sequentially generated in a one-dimensional system. We have introduced a new family of states which extends this sequential definition to two dimensions. Like in Matrix Product States, expectation values of few body observables can be efficiently evaluated and, for the case of translationally invariant systems, the correlation functions decay exponentially with the distance. We show that such states are a subclass of Projected Entangled Pair States and investigate their suitability for approximating the ground states of local Hamiltonians.

Hazardous Traffic Event Detection Using Markov Blanket and Sequential Minimal Optimization (MB-SMO

Directory of Open Access Journals (Sweden)

Lixin Yan

2016-07-01

Full Text Available The ability to identify hazardous traffic events is already considered as one of the most effective solutions for reducing the occurrence of crashes. Only certain particular hazardous traffic events have been studied in previous studies, which were mainly based on dedicated video stream data and GPS data. The objective of this study is twofold: (1 the Markov blanket (MB algorithm is employed to extract the main factors associated with hazardous traffic events; (2 a model is developed to identify hazardous traffic event using driving characteristics, vehicle trajectory, and vehicle position data. Twenty-two licensed drivers were recruited to carry out a natural driving experiment in Wuhan, China, and multi-sensor information data were collected for different types of traffic events. The results indicated that a vehicle’s speed, the standard deviation of speed, the standard deviation of skin conductance, the standard deviation of brake pressure, turn signal, the acceleration of steering, the standard deviation of acceleration, and the acceleration in Z (G have significant influences on hazardous traffic events. The sequential minimal optimization (SMO algorithm was adopted to build the identification model, and the accuracy of prediction was higher than 86%. Moreover, compared with other detection algorithms, the MB-SMO algorithm was ranked best in terms of the prediction accuracy. The conclusions can provide reference evidence for the development of dangerous situation warning products and the design of intelligent vehicles.

Mathematical analysis, approximation theory and their applications

CERN Document Server

Gupta, Vijay

2016-01-01

Designed for graduate students, researchers, and engineers in mathematics, optimization, and economics, this self-contained volume presents theory, methods, and applications in mathematical analysis and approximation theory. Specific topics include: approximation of functions by linear positive operators with applications to computer aided geometric design, numerical analysis, optimization theory, and solutions of differential equations. Recent and significant developments in approximation theory, special functions and q-calculus along with their applications to mathematics, engineering, and social sciences are discussed and analyzed. Each chapter enriches the understanding of current research problems and theories in pure and applied research.

Approximations of Fuzzy Systems

Directory of Open Access Journals (Sweden)

Vinai K. Singh

2013-03-01

Full Text Available A fuzzy system can uniformly approximate any real continuous function on a compact domain to any degree of accuracy. Such results can be viewed as an existence of optimal fuzzy systems. Li-Xin Wang discussed a similar problem using Gaussian membership function and Stone-Weierstrass Theorem. He established that fuzzy systems, with product inference, centroid defuzzification and Gaussian functions are capable of approximating any real continuous function on a compact set to arbitrary accuracy. In this paper we study a similar approximation problem by using exponential membership functions

Maximum error-bounded Piecewise Linear Representation for online stream approximation

KAUST Repository

Xie, Qing; Pang, Chaoyi; Zhou, Xiaofang; Zhang, Xiangliang; Deng, Ke

2014-01-01

Given a time series data stream, the generation of error-bounded Piecewise Linear Representation (error-bounded PLR) is to construct a number of consecutive line segments to approximate the stream, such that the approximation error does not exceed a prescribed error bound. In this work, we consider the error bound in L∞ norm as approximation criterion, which constrains the approximation error on each corresponding data point, and aim on designing algorithms to generate the minimal number of segments. In the literature, the optimal approximation algorithms are effectively designed based on transformed space other than time-value space, while desirable optimal solutions based on original time domain (i.e., time-value space) are still lacked. In this article, we proposed two linear-time algorithms to construct error-bounded PLR for data stream based on time domain, which are named OptimalPLR and GreedyPLR, respectively. The OptimalPLR is an optimal algorithm that generates minimal number of line segments for the stream approximation, and the GreedyPLR is an alternative solution for the requirements of high efficiency and resource-constrained environment. In order to evaluate the superiority of OptimalPLR, we theoretically analyzed and compared OptimalPLR with the state-of-art optimal solution in transformed space, which also achieves linear complexity. We successfully proved the theoretical equivalence between time-value space and such transformed space, and also discovered the superiority of OptimalPLR on processing efficiency in practice. The extensive results of empirical evaluation support and demonstrate the effectiveness and efficiency of our proposed algorithms.

Maximum error-bounded Piecewise Linear Representation for online stream approximation

KAUST Repository

Xie, Qing

2014-04-04

Given a time series data stream, the generation of error-bounded Piecewise Linear Representation (error-bounded PLR) is to construct a number of consecutive line segments to approximate the stream, such that the approximation error does not exceed a prescribed error bound. In this work, we consider the error bound in L∞ norm as approximation criterion, which constrains the approximation error on each corresponding data point, and aim on designing algorithms to generate the minimal number of segments. In the literature, the optimal approximation algorithms are effectively designed based on transformed space other than time-value space, while desirable optimal solutions based on original time domain (i.e., time-value space) are still lacked. In this article, we proposed two linear-time algorithms to construct error-bounded PLR for data stream based on time domain, which are named OptimalPLR and GreedyPLR, respectively. The OptimalPLR is an optimal algorithm that generates minimal number of line segments for the stream approximation, and the GreedyPLR is an alternative solution for the requirements of high efficiency and resource-constrained environment. In order to evaluate the superiority of OptimalPLR, we theoretically analyzed and compared OptimalPLR with the state-of-art optimal solution in transformed space, which also achieves linear complexity. We successfully proved the theoretical equivalence between time-value space and such transformed space, and also discovered the superiority of OptimalPLR on processing efficiency in practice. The extensive results of empirical evaluation support and demonstrate the effectiveness and efficiency of our proposed algorithms.

Optimization algorithms and applications

CERN Document Server

Arora, Rajesh Kumar

2015-01-01

Choose the Correct Solution Method for Your Optimization ProblemOptimization: Algorithms and Applications presents a variety of solution techniques for optimization problems, emphasizing concepts rather than rigorous mathematical details and proofs. The book covers both gradient and stochastic methods as solution techniques for unconstrained and constrained optimization problems. It discusses the conjugate gradient method, Broyden-Fletcher-Goldfarb-Shanno algorithm, Powell method, penalty function, augmented Lagrange multiplier method, sequential quadratic programming, method of feasible direc

Adaptive kernels in approximate filtering of state-space models

Czech Academy of Sciences Publication Activity Database

Dedecius, Kamil

2017-01-01

Roč. 31, č. 6 (2017), s. 938-952 ISSN 0890-6327 R&D Projects: GA ČR(CZ) GP14-06678P Institutional support: RVO:67985556 Keywords : filtering * nonlinear filters * Bayesian filtering * sequential Monte Carlo * approximate filtering Subject RIV: BB - Applied Statistics, Operational Research OBOR OECD: Statistics and probability Impact factor: 1.708, year: 2016 http://library.utia.cs.cz/separaty/2016/AS/dedecius-0466448.pdf

On-line Flagging of Anomalies and Adaptive Sequential Hypothesis Testing for Fine-feature Characterization of Geosynchronous Satellites

Science.gov (United States)

2015-10-18

model-based evidence. This work resolves cross-tag using three methods (Z-test for dependent data, classical sequential analysis and Brownian motion...Slider Movement The two-facet model is used as the Inversion Model. It represents a three-axis stabilized satellite as two facets, namely a body...the sequential analysis. If is independent and has an approximately normal distribution then Brownian motion drift analysis is used. If is

Sequential Triangle Strip Generator based on Hopfield Networks

Czech Academy of Sciences Publication Activity Database

Šíma, Jiří; Lněnička, Radim

2009-01-01

Roč. 21, č. 2 (2009), s. 583-617 ISSN 0899-7667 R&D Projects: GA MŠk(CZ) 1M0545; GA AV ČR 1ET100300517; GA MŠk 1M0572 Institutional research plan: CEZ:AV0Z10300504; CEZ:AV0Z10750506 Keywords : sequential triangle strip * combinatorial optimization * Hopfield network * minimum energy * simulated annealing Subject RIV: IN - Informatics, Computer Science Impact factor: 2.175, year: 2009

A Data-Driven Method for Selecting Optimal Models Based on Graphical Visualisation of Differences in Sequentially Fitted ROC Model Parameters

Directory of Open Access Journals (Sweden)

K S Mwitondi

2013-05-01

Full Text Available Differences in modelling techniques and model performance assessments typically impinge on the quality of knowledge extraction from data. We propose an algorithm for determining optimal patterns in data by separately training and testing three decision tree models in the Pima Indians Diabetes and the Bupa Liver Disorders datasets. Model performance is assessed using ROC curves and the Youden Index. Moving differences between sequential fitted parameters are then extracted, and their respective probability density estimations are used to track their variability using an iterative graphical data visualisation technique developed for this purpose. Our results show that the proposed strategy separates the groups more robustly than the plain ROC/Youden approach, eliminates obscurity, and minimizes over-fitting. Further, the algorithm can easily be understood by non-specialists and demonstrates multi-disciplinary compliance.

[Optimization and Prognosis of Cell Radiosensitivity Enhancement in vitro and in vivo after Sequential Thermoradiactive Action].

Science.gov (United States)

Belkina, S V; Petin, V G

2016-01-01

Previously developed mathematical model of simultaneous action of two inactivating agents has been adapted and tested to describe the results of sequential action. The possibility of applying the mathematical model to the interpretation and prognosis of the increase in radio-sensitivity of tumor cells as well as mammalian cells after sequential action of two high temperatures or hyperthermia and ionizing radiation is analyzed. The model predicts the value of the thermal enhancement ratio depending on the duration of thermal exposure, its greatest value, and the condition under which it is achieved.

Polygonal-path approximation on the path spaces of quantum mechanical systems: extended Feynman maps

International Nuclear Information System (INIS)

Exner, R.; Kolerov, G.I.

1981-01-01

Various types of polygonal-path approximations appearing in the functional-integration theory are discussed. The uniform approximation is applied to extend the definition of the Feynman maps from our previous paper and to prove consistency of this extension. Relations of the extended Fsub(-i)-map to the Wiener integral are given. In particular, the basic theorem about the sequential Wiener integral by Cameron is improved [ru

Moving mesh generation with a sequential approach for solving PDEs

DEFF Research Database (Denmark)

In moving mesh methods, physical PDEs and a mesh equation derived from equidistribution of an error metrics (so-called the monitor function) are simultaneously solved and meshes are dynamically concentrated on steep regions (Lim et al., 2001). However, the simultaneous solution procedure...... a simple and robust moving mesh algorithm in one or multidimension. In this study, we propose a sequential solution procedure including two separate parts: prediction step to obtain an approximate solution to a next time level (integration of physical PDEs) and regriding step at the next time level (mesh...... generation and solution interpolation). Convection terms, which appear in physical PDEs and a mesh equation, are discretized by a WENO (Weighted Essentially Non-Oscillatory) scheme under the consrvative form. This sequential approach is to keep the advantages of robustness and simplicity for the static...

Sequentially Integrated Optimization of the Conditions to Obtain a High-Protein and Low-Antinutritional Factors Protein Isolate from Edible Jatropha curcas Seed Cake.

Science.gov (United States)

León-López, Liliana; Dávila-Ortiz, Gloria; Jiménez-Martínez, Cristian; Hernández-Sánchez, Humberto

2013-01-01

Jatropha curcas seed cake is a protein-rich byproduct of oil extraction which could be used to produce protein isolates. The purpose of this study was the optimization of the protein isolation process from the seed cake of an edible provenance of J. curcas by an alkaline extraction followed by isoelectric precipitation method via a sequentially integrated optimization approach. The influence of four different factors (solubilization pH, extraction temperature, NaCl addition, and precipitation pH) on the protein and antinutritional compounds content of the isolate was evaluated. The estimated optimal conditions were an extraction temperature of 20°C, a precipitation pH of 4, and an amount of NaCl in the extraction solution of 0.6 M for a predicted protein content of 93.3%. Under these conditions, it was possible to obtain experimentally a protein isolate with 93.21% of proteins, 316.5 mg 100 g(-1) of total phenolics, 2891.84 mg 100 g(-1) of phytates and 168 mg 100 g(-1) of saponins. The protein content of the this isolate was higher than the content reported by other authors.

Optimal trajectories of aircraft and spacecraft

Science.gov (United States)

Miele, A.

1990-01-01

Work done on algorithms for the numerical solutions of optimal control problems and their application to the computation of optimal flight trajectories of aircraft and spacecraft is summarized. General considerations on calculus of variations, optimal control, numerical algorithms, and applications of these algorithms to real-world problems are presented. The sequential gradient-restoration algorithm (SGRA) is examined for the numerical solution of optimal control problems of the Bolza type. Both the primal formulation and the dual formulation are discussed. Aircraft trajectories, in particular, the application of the dual sequential gradient-restoration algorithm (DSGRA) to the determination of optimal flight trajectories in the presence of windshear are described. Both take-off trajectories and abort landing trajectories are discussed. Take-off trajectories are optimized by minimizing the peak deviation of the absolute path inclination from a reference value. Abort landing trajectories are optimized by minimizing the peak drop of altitude from a reference value. Abort landing trajectories are optimized by minimizing the peak drop of altitude from a reference value. The survival capability of an aircraft in a severe windshear is discussed, and the optimal trajectories are found to be superior to both constant pitch trajectories and maximum angle of attack trajectories. Spacecraft trajectories, in particular, the application of the primal sequential gradient-restoration algorithm (PSGRA) to the determination of optimal flight trajectories for aeroassisted orbital transfer are examined. Both the coplanar case and the noncoplanar case are discussed within the frame of three problems: minimization of the total characteristic velocity; minimization of the time integral of the square of the path inclination; and minimization of the peak heating rate. The solution of the second problem is called nearly-grazing solution, and its merits are pointed out as a useful

Non-euclidean simplex optimization

International Nuclear Information System (INIS)

Silver, G.L.

1977-01-01

Geometric optimization techniques useful for studying chemical equilibrium traditionally rely upon principles of euclidean geometry, but such algorithms may also be based upon principles of a non-euclidean geometry. The sequential simplex method is adapted to the hyperbolic plane, and application of optimization to problems such as the potentiometric titration of plutonium is suggested

Rational approximations for tomographic reconstructions

International Nuclear Information System (INIS)

Reynolds, Matthew; Beylkin, Gregory; Monzón, Lucas

2013-01-01

We use optimal rational approximations of projection data collected in x-ray tomography to improve image resolution. Under the assumption that the object of interest is described by functions with jump discontinuities, for each projection we construct its rational approximation with a small (near optimal) number of terms for a given accuracy threshold. This allows us to augment the measured data, i.e., double the number of available samples in each projection or, equivalently, extend (double) the domain of their Fourier transform. We also develop a new, fast, polar coordinate Fourier domain algorithm which uses our nonlinear approximation of projection data in a natural way. Using augmented projections of the Shepp–Logan phantom, we provide a comparison between the new algorithm and the standard filtered back-projection algorithm. We demonstrate that the reconstructed image has improved resolution without additional artifacts near sharp transitions in the image. (paper)

Metal fractionation of atmospheric aerosols via sequential chemical extraction: a review

Energy Technology Data Exchange (ETDEWEB)

Smichowski, Patricia; Gomez, Dario [Unidad de Actividad Quimica, Comision Nacional de Energia Atomica, San Martin (Argentina); Polla, Griselda [Unidad de Actividad Fisica, Comision Nacional de Energia Atomica, San Martin (Argentina)

2005-01-01

This review surveys schemes used to sequentially chemically fractionate metals and metalloids present in airborne particulate matter. It focuses mainly on sequential chemical fractionation schemes published over the last 15 years. These schemes have been classified into five main categories: (1) based on Tessier's procedure, (2) based on Chester's procedure, (3) based on Zatka's procedure, (4) based on BCR procedure, and (5) other procedures. The operational characteristics as well as the state of the art in metal fractionation of airborne particulate matter, fly ashes and workroom aerosols, in terms of applications, optimizations and innovations, are also described. Many references to other works in this area are provided. (orig.)

Variational Gaussian approximation for Poisson data

Science.gov (United States)

Arridge, Simon R.; Ito, Kazufumi; Jin, Bangti; Zhang, Chen

2018-02-01

The Poisson model is frequently employed to describe count data, but in a Bayesian context it leads to an analytically intractable posterior probability distribution. In this work, we analyze a variational Gaussian approximation to the posterior distribution arising from the Poisson model with a Gaussian prior. This is achieved by seeking an optimal Gaussian distribution minimizing the Kullback-Leibler divergence from the posterior distribution to the approximation, or equivalently maximizing the lower bound for the model evidence. We derive an explicit expression for the lower bound, and show the existence and uniqueness of the optimal Gaussian approximation. The lower bound functional can be viewed as a variant of classical Tikhonov regularization that penalizes also the covariance. Then we develop an efficient alternating direction maximization algorithm for solving the optimization problem, and analyze its convergence. We discuss strategies for reducing the computational complexity via low rank structure of the forward operator and the sparsity of the covariance. Further, as an application of the lower bound, we discuss hierarchical Bayesian modeling for selecting the hyperparameter in the prior distribution, and propose a monotonically convergent algorithm for determining the hyperparameter. We present extensive numerical experiments to illustrate the Gaussian approximation and the algorithms.

Markov decision processes: a tool for sequential decision making under uncertainty.

Science.gov (United States)

Alagoz, Oguzhan; Hsu, Heather; Schaefer, Andrew J; Roberts, Mark S

2010-01-01

We provide a tutorial on the construction and evaluation of Markov decision processes (MDPs), which are powerful analytical tools used for sequential decision making under uncertainty that have been widely used in many industrial and manufacturing applications but are underutilized in medical decision making (MDM). We demonstrate the use of an MDP to solve a sequential clinical treatment problem under uncertainty. Markov decision processes generalize standard Markov models in that a decision process is embedded in the model and multiple decisions are made over time. Furthermore, they have significant advantages over standard decision analysis. We compare MDPs to standard Markov-based simulation models by solving the problem of the optimal timing of living-donor liver transplantation using both methods. Both models result in the same optimal transplantation policy and the same total life expectancies for the same patient and living donor. The computation time for solving the MDP model is significantly smaller than that for solving the Markov model. We briefly describe the growing literature of MDPs applied to medical decisions.

Multilevel sequential Monte-Carlo samplers

KAUST Repository

Jasra, Ajay

2016-01-01

Multilevel Monte-Carlo methods provide a powerful computational technique for reducing the computational cost of estimating expectations for a given computational effort. They are particularly relevant for computational problems when approximate distributions are determined via a resolution parameter h, with h=0 giving the theoretical exact distribution (e.g. SDEs or inverse problems with PDEs). The method provides a benefit by coupling samples from successive resolutions, and estimating differences of successive expectations. We develop a methodology that brings Sequential Monte-Carlo (SMC) algorithms within the framework of the Multilevel idea, as SMC provides a natural set-up for coupling samples over different resolutions. We prove that the new algorithm indeed preserves the benefits of the multilevel principle, even if samples at all resolutions are now correlated.

Multilevel sequential Monte-Carlo samplers

KAUST Repository

Jasra, Ajay

2016-01-05

Multilevel Monte-Carlo methods provide a powerful computational technique for reducing the computational cost of estimating expectations for a given computational effort. They are particularly relevant for computational problems when approximate distributions are determined via a resolution parameter h, with h=0 giving the theoretical exact distribution (e.g. SDEs or inverse problems with PDEs). The method provides a benefit by coupling samples from successive resolutions, and estimating differences of successive expectations. We develop a methodology that brings Sequential Monte-Carlo (SMC) algorithms within the framework of the Multilevel idea, as SMC provides a natural set-up for coupling samples over different resolutions. We prove that the new algorithm indeed preserves the benefits of the multilevel principle, even if samples at all resolutions are now correlated.

Further Developments on Optimum Structural Design Using MSC/Nastran and Sequential Quadratic Programming

DEFF Research Database (Denmark)

Holzleitner, Ludwig

1996-01-01

, here the shape of two dimensional parts with different thickness areas will be optimized. As in the previos paper, a methodology for structural optimization using the commercial finite element package MSC/NASTRAN for structural analysis is described. Three different methods for design sensitivity......This work is closely connected to the paper: K.G. MAHMOUD, H.W. ENGL and HOLZLEITNER: "OPTIMUM STRUCTURAL DESIGN USING MSC/NASTRAN AND SEQUENTIAL QUADRATIC PROGRAMMING", Computers & Structures, Vol. 52, No. 3, pp. 437-447, (1994). In contrast to that paper, where thickness optimization is described...

Mathematical programming methods for large-scale topology optimization problems

DEFF Research Database (Denmark)

Rojas Labanda, Susana

for mechanical problems, but has rapidly extended to many other disciplines, such as fluid dynamics and biomechanical problems. However, the novelty and improvements of optimization methods has been very limited. It is, indeed, necessary to develop of new optimization methods to improve the final designs......, and at the same time, reduce the number of function evaluations. Nonlinear optimization methods, such as sequential quadratic programming and interior point solvers, have almost not been embraced by the topology optimization community. Thus, this work is focused on the introduction of this kind of second...... for the classical minimum compliance problem. Two of the state-of-the-art optimization algorithms are investigated and implemented for this structural topology optimization problem. A Sequential Quadratic Programming (TopSQP) and an interior point method (TopIP) are developed exploiting the specific mathematical...

Deterministic global optimization algorithm based on outer approximation for the parameter estimation of nonlinear dynamic biological systems.

Science.gov (United States)

Miró, Anton; Pozo, Carlos; Guillén-Gosálbez, Gonzalo; Egea, Jose A; Jiménez, Laureano

2012-05-10

The estimation of parameter values for mathematical models of biological systems is an optimization problem that is particularly challenging due to the nonlinearities involved. One major difficulty is the existence of multiple minima in which standard optimization methods may fall during the search. Deterministic global optimization methods overcome this limitation, ensuring convergence to the global optimum within a desired tolerance. Global optimization techniques are usually classified into stochastic and deterministic. The former typically lead to lower CPU times but offer no guarantee of convergence to the global minimum in a finite number of iterations. In contrast, deterministic methods provide solutions of a given quality (i.e., optimality gap), but tend to lead to large computational burdens. This work presents a deterministic outer approximation-based algorithm for the global optimization of dynamic problems arising in the parameter estimation of models of biological systems. Our approach, which offers a theoretical guarantee of convergence to global minimum, is based on reformulating the set of ordinary differential equations into an equivalent set of algebraic equations through the use of orthogonal collocation methods, giving rise to a nonconvex nonlinear programming (NLP) problem. This nonconvex NLP is decomposed into two hierarchical levels: a master mixed-integer linear programming problem (MILP) that provides a rigorous lower bound on the optimal solution, and a reduced-space slave NLP that yields an upper bound. The algorithm iterates between these two levels until a termination criterion is satisfied. The capabilities of our approach were tested in two benchmark problems, in which the performance of our algorithm was compared with that of the commercial global optimization package BARON. The proposed strategy produced near optimal solutions (i.e., within a desired tolerance) in a fraction of the CPU time required by BARON.

Strategic Path Planning by Sequential Parametric Bayesian Decisions

Directory of Open Access Journals (Sweden)

Baro Hyun

2013-11-01

Full Text Available The objective of this research is to generate a path for a mobile agent that carries sensors used for classification, where the path is to optimize strategic objectives that account for misclassification and the consequences of misclassification, and where the weights assigned to these consequences are chosen by a strategist. We propose a model that accounts for the interaction between the agent kinematics (i.e., the ability to move, informatics (i.e., the ability to process data to information, classification (i.e., the ability to classify objects based on the information, and strategy (i.e., the mission objective. Within this model, we pose and solve a sequential decision problem that accounts for strategist preferences and the solution to the problem yields a sequence of kinematic decisions of a moving agent. The solution of the sequential decision problem yields the following flying tactics: “approach only objects whose suspected identity matters to the strategy”. These tactics are numerically illustrated in several scenarios.

Optimisation of beryllium-7 gamma analysis following BCR sequential extraction

International Nuclear Information System (INIS)

Taylor, A.; Blake, W.H.; Keith-Roach, M.J.

2012-01-01

Graphical abstract: Showing decrease in analytical uncertainty using the optimal (combined preconcentrated sample extract) method. nv (no value) where extract activities were 7 Be geochemical behaviour is required to support tracer studies. ► Sequential extraction with natural 7 Be returns high analytical uncertainties. ► Preconcentrating extracts from a large sample mass improved analytical uncertainty. ► This optimised method can be readily employed in studies using low activity samples. - Abstract: The application of cosmogenic 7 Be as a sediment tracer at the catchment-scale requires an understanding of its geochemical associations in soil to underpin the assumption of irreversible adsorption. Sequential extractions offer a readily accessible means of determining the associations of 7 Be with operationally defined soil phases. However, the subdivision of the low activity concentrations of fallout 7 Be in soils into geochemical fractions can introduce high gamma counting uncertainties. Extending analysis time significantly is not always an option for batches of samples, owing to the on-going decay of 7 Be (t 1/2 = 53.3 days). Here, three different methods of preparing and quantifying 7 Be extracted using the optimised BCR three-step scheme have been evaluated and compared with a focus on reducing analytical uncertainties. The optimal method involved carrying out the BCR extraction in triplicate, sub-sampling each set of triplicates for stable Be analysis before combining each set and coprecipitating the 7 Be with metal oxyhydroxides to produce a thin source for gamma analysis. This method was applied to BCR extractions of natural 7 Be in four agricultural soils. The approach gave good counting statistics from a 24 h analysis period (∼10% (2σ) where extract activity >40% of total activity) and generated statistically useful sequential extraction profiles. Total recoveries of 7 Be fell between 84 and 112%. The stable Be data demonstrated that the

ROAM: A Radial-Basis-Function Optimization Approximation Method for Diagnosing the Three-Dimensional Coronal Magnetic Field

International Nuclear Information System (INIS)

Dalmasse, Kevin; Nychka, Douglas W.; Gibson, Sarah E.; Fan, Yuhong; Flyer, Natasha

2016-01-01

The Coronal Multichannel Polarimeter (CoMP) routinely performs coronal polarimetric measurements using the Fe XIII 10747 and 10798 lines, which are sensitive to the coronal magnetic field. However, inverting such polarimetric measurements into magnetic field data is a difficult task because the corona is optically thin at these wavelengths and the observed signal is therefore the integrated emission of all the plasma along the line of sight. To overcome this difficulty, we take on a new approach that combines a parameterized 3D magnetic field model with forward modeling of the polarization signal. For that purpose, we develop a new, fast and efficient, optimization method for model-data fitting: the Radial-basis-functions Optimization Approximation Method (ROAM). Model-data fitting is achieved by optimizing a user-specified log-likelihood function that quantifies the differences between the observed polarization signal and its synthetic/predicted analog. Speed and efficiency are obtained by combining sparse evaluation of the magnetic model with radial-basis-function (RBF) decomposition of the log-likelihood function. The RBF decomposition provides an analytical expression for the log-likelihood function that is used to inexpensively estimate the set of parameter values optimizing it. We test and validate ROAM on a synthetic test bed of a coronal magnetic flux rope and show that it performs well with a significantly sparse sample of the parameter space. We conclude that our optimization method is well-suited for fast and efficient model-data fitting and can be exploited for converting coronal polarimetric measurements, such as the ones provided by CoMP, into coronal magnetic field data.

Q-Learning Multi-Objective Sequential Optimal Sensor Parameter Weights

Directory of Open Access Journals (Sweden)

Raquel Cohen

2016-04-01

Full Text Available The goal of our solution is to deliver trustworthy decision making analysis tools which evaluate situations and potential impacts of such decisions through acquired information and add efficiency for continuing mission operations and analyst information.We discuss the use of cooperation in modeling and simulation and show quantitative results for design choices to resource allocation. The key contribution of our paper is to combine remote sensing decision making with Nash Equilibrium for sensor parameter weighting optimization. By calculating all Nash Equilibrium possibilities per period, optimization of sensor allocation is achieved for overall higher system efficiency. Our tool provides insight into what are the most important or optimal weights for sensor parameters and can be used to efficiently tune those weights.

Trajectory averaging for stochastic approximation MCMC algorithms

KAUST Repository

Liang, Faming

2010-10-01

The subject of stochastic approximation was founded by Robbins and Monro [Ann. Math. Statist. 22 (1951) 400-407]. After five decades of continual development, it has developed into an important area in systems control and optimization, and it has also served as a prototype for the development of adaptive algorithms for on-line estimation and control of stochastic systems. Recently, it has been used in statistics with Markov chain Monte Carlo for solving maximum likelihood estimation problems and for general simulation and optimizations. In this paper, we first show that the trajectory averaging estimator is asymptotically efficient for the stochastic approximation MCMC (SAMCMC) algorithm under mild conditions, and then apply this result to the stochastic approximation Monte Carlo algorithm [Liang, Liu and Carroll J. Amer. Statist. Assoc. 102 (2007) 305-320]. The application of the trajectory averaging estimator to other stochastic approximationMCMC algorithms, for example, a stochastic approximation MLE algorithm for missing data problems, is also considered in the paper. © Institute of Mathematical Statistics, 2010.

Optimization of growth medium and fermentation conditions for ...

African Journals Online (AJOL)

A sequential optimization approach based on statistical experimental designs was employed to optimize growth medium and fermentation conditions, in order to improve the antibiotic activity of Xenorhabdus nematophila TB. Tryptone soyptone broth (TSB) was chosen as the original medium for optimization. Glucose and ...

More than 10 years survival with sequential therapy in a patient with advanced renal cell carcinoma: a case report

Energy Technology Data Exchange (ETDEWEB)

Yuan, J.L.; Wang, F.L.; Yi, X.M.; Qin, W.J.; Wu, G.J. [Department of Urology, Xijing Hospital, Fourth Military Medical University, Xi' an, Shaanxi (China); Huan, Y. [Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi' an, Shaanxi (China); Yang, L.J.; Zhang, G.; Yu, L.; Zhang, Y.T.; Qin, R.L.; Tian, C.J. [Department of Urology, Xijing Hospital, Fourth Military Medical University, Xi' an, Shaanxi (China)

2014-10-31

Although radical nephrectomy alone is widely accepted as the standard of care in localized treatment for renal cell carcinoma (RCC), it is not sufficient for the treatment of metastatic RCC (mRCC), which invariably leads to an unfavorable outcome despite the use of multiple therapies. Currently, sequential targeted agents are recommended for the management of mRCC, but the optimal drug sequence is still debated. This case was a 57-year-old man with clear-cell mRCC who received multiple therapies following his first operation in 2003 and has survived for over 10 years with a satisfactory quality of life. The treatments given included several surgeries, immunotherapy, and sequentially administered sorafenib, sunitinib, and everolimus regimens. In the course of mRCC treatment, well-planned surgeries, effective sequential targeted therapies and close follow-up are all of great importance for optimal management and a satisfactory outcome.

Energy optimization in mobile sensor networks

Science.gov (United States)

Yu, Shengwei

consuming negligible amount of energy for mobility cost. For the second problem, the problem is extended to accommodate mobile robotic nodes with energy harvesting capability, which makes it a non-convex optimization problem. The non-convexity issue is tackled by using the existing sequential convex approximation method, based on which we propose a novel procedure of modified sequential convex approximation that has fast convergence speed. For the third problem, the proposed procedure is used to solve another challenging non-convex problem, which results in utilizing mobility and routing simultaneously in mobile robotic sensor networks to prolong the network lifetime. The results indicate that joint design of mobility and routing has an edge over other methods in prolonging network lifetime, which is also the justification for the use of mobility in mobile sensor networks for energy efficiency purpose. For the fourth problem, we include the dynamics of the robotic nodes in the problem by modeling the networked robotic system using hybrid systems theory. A novel distributed method for the networked hybrid system is used to solve the optimal moving trajectories for robotic nodes and optimal network links, which are not answered by previous approaches. Finally, the fact that mobility is more effective in prolonging network lifetime for a data-intensive network leads us to apply our methods to study mobile visual sensor networks, which are useful in many applications. We investigate the joint design of mobility, data routing, and encoding power to help improving the video quality while maximizing the network lifetime. This study leads to a better understanding of the role mobility can play in data-intensive surveillance sensor networks.

Modelling sequentially scored item responses

NARCIS (Netherlands)

Akkermans, W.

2000-01-01

The sequential model can be used to describe the variable resulting from a sequential scoring process. In this paper two more item response models are investigated with respect to their suitability for sequential scoring: the partial credit model and the graded response model. The investigation is

Approximate representation of optimal strategies from influence diagrams

DEFF Research Database (Denmark)

Jensen, Finn V.

2008-01-01

, and where the policy functions for the decisions have so large do- mains that they cannot be represented directly in a strategy tree. The approach is to have separate ID representations for each decision variable. In each representation the actual information is fully exploited, however the representation...... of policies for future decisions are approximations. We call the approximation information abstraction. It consists in introducing a dummy structure connecting the past with the decision. We study how to specify, implement and learn information abstraction.......There are three phases in the life of a decision problem, specification, solution, and rep- resentation of solution. The specification and solution phases are off-line, while the rep- resention of solution often shall serve an on-line situation with rather tough constraints on time and space. One...

Time optimization of 90Sr measurements: Sequential measurement of multiple samples during ingrowth of 90Y

International Nuclear Information System (INIS)

Holmgren, Stina; Tovedal, Annika; Björnham, Oscar; Ramebäck, Henrik

2016-01-01

The aim of this paper is to contribute to a more rapid determination of a series of samples containing 90 Sr by making the Cherenkov measurement of the daughter nuclide 90 Y more time efficient. There are many instances when an optimization of the measurement method might be favorable, such as; situations requiring rapid results in order to make urgent decisions or, on the other hand, to maximize the throughput of samples in a limited available time span. In order to minimize the total analysis time, a mathematical model was developed which calculates the time of ingrowth as well as individual measurement times for n samples in a series. This work is focused on the measurement of 90 Y during ingrowth, after an initial chemical separation of strontium, in which it is assumed that no other radioactive strontium isotopes are present. By using a fixed minimum detectable activity (MDA) and iterating the measurement time for each consecutive sample the total analysis time will be less, compared to using the same measurement time for all samples. It was found that by optimization, the total analysis time for 10 samples can be decreased greatly, from 21 h to 6.5 h, when assuming a MDA of 1 Bq/L and at a background count rate of approximately 0.8 cpm. - Highlights: • An approach roughly a factor of three more efficient than an un-optimized method. • The optimization gives a more efficient use of instrument time. • The efficiency increase ranges from a factor of three to 10, for 10 to 40 samples.

A Feedback Optimal Control Algorithm with Optimal Measurement Time Points

Directory of Open Access Journals (Sweden)

Felix Jost

2017-02-01

Full Text Available Nonlinear model predictive control has been established as a powerful methodology to provide feedback for dynamic processes over the last decades. In practice it is usually combined with parameter and state estimation techniques, which allows to cope with uncertainty on many levels. To reduce the uncertainty it has also been suggested to include optimal experimental design into the sequential process of estimation and control calculation. Most of the focus so far was on dual control approaches, i.e., on using the controls to simultaneously excite the system dynamics (learning as well as minimizing a given objective (performing. We propose a new algorithm, which sequentially solves robust optimal control, optimal experimental design, state and parameter estimation problems. Thus, we decouple the control and the experimental design problems. This has the advantages that we can analyze the impact of measurement timing (sampling independently, and is practically relevant for applications with either an ethical limitation on system excitation (e.g., chemotherapy treatment or the need for fast feedback. The algorithm shows promising results with a 36% reduction of parameter uncertainties for the Lotka-Volterra fishing benchmark example.

An overview on polynomial approximation of NP-hard problems

Directory of Open Access Journals (Sweden)

Paschos Vangelis Th.

2009-01-01

Full Text Available The fact that polynomial time algorithm is very unlikely to be devised for an optimal solving of the NP-hard problems strongly motivates both the researchers and the practitioners to try to solve such problems heuristically, by making a trade-off between computational time and solution's quality. In other words, heuristic computation consists of trying to find not the best solution but one solution which is 'close to' the optimal one in reasonable time. Among the classes of heuristic methods for NP-hard problems, the polynomial approximation algorithms aim at solving a given NP-hard problem in poly-nomial time by computing feasible solutions that are, under some predefined criterion, as near to the optimal ones as possible. The polynomial approximation theory deals with the study of such algorithms. This survey first presents and analyzes time approximation algorithms for some classical examples of NP-hard problems. Secondly, it shows how classical notions and tools of complexity theory, such as polynomial reductions, can be matched with polynomial approximation in order to devise structural results for NP-hard optimization problems. Finally, it presents a quick description of what is commonly called inapproximability results. Such results provide limits on the approximability of the problems tackled.

Simultaneous Versus Sequential Complementarity in the Adoption of Technological and Organizational Innovations

DEFF Research Database (Denmark)

Battisti, Giuliana; Rabbiosi, Larissa; Colombo, Massimo G.

2015-01-01

It is generally suggested that technological and organizational innovations, being complementary, need to be adopted simultaneously. Nevertheless, sequential rather than simultaneous adoption of these two types of innovation may be optimal. In this paper, we analyze the pattern of mutual causation...... of technological and organizational innovations and contribute to the understanding of their interdependencies......

An effective algorithm for approximating adaptive behavior in seasonal environments

DEFF Research Database (Denmark)

Sainmont, Julie; Andersen, Ken Haste; Thygesen, Uffe Høgsbro

2015-01-01

Behavior affects most aspects of ecological processes and rates, and yet modeling frameworks which efficiently predict and incorporate behavioral responses into ecosystem models remain elusive. Behavioral algorithms based on life-time optimization, adaptive dynamics or game theory are unsuited...... for large global models because of their high computational demand. We compare an easily integrated, computationally efficient behavioral algorithm known as Gilliam's rule against the solution from a life-history optimization. The approximation takes into account only the current conditions to optimize...... behavior; the so-called "myopic approximation", "short sighted", or "static optimization". We explore the performance of the myopic approximation with diel vertical migration (DVM) as an example of a daily routine, a behavior with seasonal dependence that trades off predation risk with foraging...

Approximation in two-stage stochastic integer programming

NARCIS (Netherlands)

W. Romeijnders; L. Stougie (Leen); M. van der Vlerk

2014-01-01

htmlabstractApproximation algorithms are the prevalent solution methods in the field of stochastic programming. Problems in this field are very hard to solve. Indeed, most of the research in this field has concentrated on designing solution methods that approximate the optimal solution value.

Approximation in two-stage stochastic integer programming

NARCIS (Netherlands)

Romeijnders, W.; Stougie, L.; van der Vlerk, M.H.

2014-01-01

Approximation algorithms are the prevalent solution methods in the field of stochastic programming. Problems in this field are very hard to solve. Indeed, most of the research in this field has concentrated on designing solution methods that approximate the optimal solution value. However,

Strategies for Optimal Design of Structural Systems

DEFF Research Database (Denmark)

Enevoldsen, I.; Sørensen, John Dalsgaard

1992-01-01

Reliability-based design of structural systems is considered. Especially systems where the reliability model is a series system of parallel systems are analysed. A sensitivity analysis for this class of problems is presented. Direct and sequential optimization procedures to solve the optimization...

Multi-agent sequential hypothesis testing

KAUST Repository

Kim, Kwang-Ki K.

2014-12-15

This paper considers multi-agent sequential hypothesis testing and presents a framework for strategic learning in sequential games with explicit consideration of both temporal and spatial coordination. The associated Bayes risk functions explicitly incorporate costs of taking private/public measurements, costs of time-difference and disagreement in actions of agents, and costs of false declaration/choices in the sequential hypothesis testing. The corresponding sequential decision processes have well-defined value functions with respect to (a) the belief states for the case of conditional independent private noisy measurements that are also assumed to be independent identically distributed over time, and (b) the information states for the case of correlated private noisy measurements. A sequential investment game of strategic coordination and delay is also discussed as an application of the proposed strategic learning rules.

Data-Driven Markov Decision Process Approximations for Personalized Hypertension Treatment Planning

Directory of Open Access Journals (Sweden)

Greggory J. Schell PhD

2016-10-01

Full Text Available Background: Markov decision process (MDP models are powerful tools. They enable the derivation of optimal treatment policies but may incur long computational times and generate decision rules that are challenging to interpret by physicians. Methods: In an effort to improve usability and interpretability, we examined whether Poisson regression can approximate optimal hypertension treatment policies derived by an MDP for maximizing a patient’s expected discounted quality-adjusted life years. Results: We found that our Poisson approximation to the optimal treatment policy matched the optimal policy in 99% of cases. This high accuracy translates to nearly identical health outcomes for patients. Furthermore, the Poisson approximation results in 104 additional quality-adjusted life years per 1000 patients compared to the Seventh Joint National Committee’s treatment guidelines for hypertension. The comparative health performance of the Poisson approximation was robust to the cardiovascular disease risk calculator used and calculator calibration error. Limitations: Our results are based on Markov chain modeling. Conclusions: Poisson model approximation for blood pressure treatment planning has high fidelity to optimal MDP treatment policies, which can improve usability and enhance transparency of more personalized treatment policies.

Sequential capillary electrophoresis analysis using optically gated sample injection and UV/vis detection.

Science.gov (United States)

Liu, Xiaoxia; Tian, Miaomiao; Camara, Mohamed Amara; Guo, Liping; Yang, Li

2015-10-01

We present sequential CE analysis of amino acids and L-asparaginase-catalyzed enzyme reaction, by combing the on-line derivatization, optically gated (OG) injection and commercial-available UV-Vis detection. Various experimental conditions for sequential OG-UV/vis CE analysis were investigated and optimized by analyzing a standard mixture of amino acids. High reproducibility of the sequential CE analysis was demonstrated with RSD values (n = 20) of 2.23, 2.57, and 0.70% for peak heights, peak areas, and migration times, respectively, and the LOD of 5.0 μM (for asparagine) and 2.0 μM (for aspartic acid) were obtained. With the application of the OG-UV/vis CE analysis, sequential online CE enzyme assay of L-asparaginase-catalyzed enzyme reaction was carried out by automatically and continuously monitoring the substrate consumption and the product formation every 12 s from the beginning to the end of the reaction. The Michaelis constants for the reaction were obtained and were found to be in good agreement with the results of traditional off-line enzyme assays. The study demonstrated the feasibility and reliability of integrating the OG injection with UV/vis detection for sequential online CE analysis, which could be of potential value for online monitoring various chemical reaction and bioprocesses. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A Bayesian sequential design with adaptive randomization for 2-sided hypothesis test.

Science.gov (United States)

Yu, Qingzhao; Zhu, Lin; Zhu, Han

2017-11-01

Bayesian sequential and adaptive randomization designs are gaining popularity in clinical trials thanks to their potentials to reduce the number of required participants and save resources. We propose a Bayesian sequential design with adaptive randomization rates so as to more efficiently attribute newly recruited patients to different treatment arms. In this paper, we consider 2-arm clinical trials. Patients are allocated to the 2 arms with a randomization rate to achieve minimum variance for the test statistic. Algorithms are presented to calculate the optimal randomization rate, critical values, and power for the proposed design. Sensitivity analysis is implemented to check the influence on design by changing the prior distributions. Simulation studies are applied to compare the proposed method and traditional methods in terms of power and actual sample sizes. Simulations show that, when total sample size is fixed, the proposed design can obtain greater power and/or cost smaller actual sample size than the traditional Bayesian sequential design. Finally, we apply the proposed method to a real data set and compare the results with the Bayesian sequential design without adaptive randomization in terms of sample sizes. The proposed method can further reduce required sample size. Copyright © 2017 John Wiley & Sons, Ltd.

Pareto-Optimal Model Selection via SPRINT-Race.

Science.gov (United States)

Zhang, Tiantian; Georgiopoulos, Michael; Anagnostopoulos, Georgios C

2018-02-01

In machine learning, the notion of multi-objective model selection (MOMS) refers to the problem of identifying the set of Pareto-optimal models that optimize by compromising more than one predefined objectives simultaneously. This paper introduces SPRINT-Race, the first multi-objective racing algorithm in a fixed-confidence setting, which is based on the sequential probability ratio with indifference zone test. SPRINT-Race addresses the problem of MOMS with multiple stochastic optimization objectives in the proper Pareto-optimality sense. In SPRINT-Race, a pairwise dominance or non-dominance relationship is statistically inferred via a non-parametric, ternary-decision, dual-sequential probability ratio test. The overall probability of falsely eliminating any Pareto-optimal models or mistakenly returning any clearly dominated models is strictly controlled by a sequential Holm's step-down family-wise error rate control method. As a fixed-confidence model selection algorithm, the objective of SPRINT-Race is to minimize the computational effort required to achieve a prescribed confidence level about the quality of the returned models. The performance of SPRINT-Race is first examined via an artificially constructed MOMS problem with known ground truth. Subsequently, SPRINT-Race is applied on two real-world applications: 1) hybrid recommender system design and 2) multi-criteria stock selection. The experimental results verify that SPRINT-Race is an effective and efficient tool for such MOMS problems. code of SPRINT-Race is available at https://github.com/watera427/SPRINT-Race.

Sequential charged particle reaction

International Nuclear Information System (INIS)

Hori, Jun-ichi; Ochiai, Kentaro; Sato, Satoshi; Yamauchi, Michinori; Nishitani, Takeo

2004-01-01

The effective cross sections for producing the sequential reaction products in F82H, pure vanadium and LiF with respect to the 14.9-MeV neutron were obtained and compared with the estimation ones. Since the sequential reactions depend on the secondary charged particles behavior, the effective cross sections are corresponding to the target nuclei and the material composition. The effective cross sections were also estimated by using the EAF-libraries and compared with the experimental ones. There were large discrepancies between estimated and experimental values. Additionally, we showed the contribution of the sequential reaction on the induced activity and dose rate in the boundary region with water. From the present study, it has been clarified that the sequential reactions are of great importance to evaluate the dose rates around the surface of cooling pipe and the activated corrosion products. (author)

Optimization of Approximate Inhibitory Rules Relative to Number of Misclassifications

KAUST Repository

Alsolami, Fawaz

2013-10-04

In this work, we consider so-called nonredundant inhibitory rules, containing an expression “attribute:F value” on the right- hand side, for which the number of misclassifications is at most a threshold γ. We study a dynamic programming approach for description of the considered set of rules. This approach allows also the optimization of nonredundant inhibitory rules relative to the length and coverage. The aim of this paper is to investigate an additional possibility of optimization relative to the number of misclassifications. The results of experiments with decision tables from the UCI Machine Learning Repository show this additional optimization achieves a fewer misclassifications. Thus, the proposed optimization procedure is promising.

Evaluation of optimization strategies and the effect of initial conditions on IMAT optimization using a leaf position optimization algorithm

International Nuclear Information System (INIS)

Oliver, Mike; Jensen, Michael; Chen, Jeff; Wong, Eugene

2009-01-01

Intensity-modulated arc therapy (IMAT) is a rotational variant of intensity-modulated radiation therapy (IMRT) that can be implemented with or without angular dose rate variation. The purpose of this study is to assess optimization strategies and initial conditions using a leaf position optimization (LPO) algorithm altered for variable dose rate IMAT. A concave planning target volume (PTV) with a central cylindrical organ at risk (OAR) was used in this study. The initial IMAT arcs were approximated by multiple static beams at 5 deg. angular increments where multi-leaf collimator (MLC) leaf positions were determined from the beam's eye view to irradiate the PTV but avoid the OAR. For the optimization strategy, two arcs with arc ranges of 280 deg. and 150 deg. were employed and plans were created using LPO alone, variable dose rate optimization (VDRO) alone, simultaneous LPO and VDRO and sequential combinations of these strategies. To assess the MLC initialization effect, three single 360 deg. arc plans with different initial MLC configurations were generated using the simultaneous LPO and VDRO. The effect of changing optimization degrees of freedom was investigated by employing 3 deg., 5 deg. and 10 deg. angular sampling intervals for the two 280 deg., two 150 deg. and single arc plans using LPO and VDRO. The objective function value, a conformity index, a dose homogeneity index, mean dose to OAR and normal tissues were computed and used to evaluate the treatment plans. This study shows that the best optimization strategy for a concave target is to use simultaneous MLC LPO and VDRO. We found that the optimization result is sensitive to the choice of initial MLC aperture shapes suggesting that an LPO-based IMAT plan may not be able to overcome local minima for this geometry. In conclusion, simultaneous MLC leaf position and VDRO are needed with the most appropriate initial conditions (MLC positions, arc ranges and number of arcs) for IMAT.

The metabolic network of Clostridium acetobutylicum: Comparison of the approximate Bayesian computation via sequential Monte Carlo (ABC-SMC) and profile likelihood estimation (PLE) methods for determinability analysis.

Science.gov (United States)

Thorn, Graeme J; King, John R

2016-01-01

The Gram-positive bacterium Clostridium acetobutylicum is an anaerobic endospore-forming species which produces acetone, butanol and ethanol via the acetone-butanol (AB) fermentation process, leading to biofuels including butanol. In previous work we looked to estimate the parameters in an ordinary differential equation model of the glucose metabolism network using data from pH-controlled continuous culture experiments. Here we combine two approaches, namely the approximate Bayesian computation via an existing sequential Monte Carlo (ABC-SMC) method (to compute credible intervals for the parameters), and the profile likelihood estimation (PLE) (to improve the calculation of confidence intervals for the same parameters), the parameters in both cases being derived from experimental data from forward shift experiments. We also apply the ABC-SMC method to investigate which of the models introduced previously (one non-sporulation and four sporulation models) have the greatest strength of evidence. We find that the joint approximate posterior distribution of the parameters determines the same parameters as previously, including all of the basal and increased enzyme production rates and enzyme reaction activity parameters, as well as the Michaelis-Menten kinetic parameters for glucose ingestion, while other parameters are not as well-determined, particularly those connected with the internal metabolites acetyl-CoA, acetoacetyl-CoA and butyryl-CoA. We also find that the approximate posterior is strongly non-Gaussian, indicating that our previous assumption of elliptical contours of the distribution is not valid, which has the effect of reducing the numbers of pairs of parameters that are (linearly) correlated with each other. Calculations of confidence intervals using the PLE method back this up. Finally, we find that all five of our models are equally likely, given the data available at present. Copyright © 2015 Elsevier Inc. All rights reserved.

Eyewitness confidence in simultaneous and sequential lineups: a criterion shift account for sequential mistaken identification overconfidence.

Science.gov (United States)

Dobolyi, David G; Dodson, Chad S

2013-12-01

Confidence judgments for eyewitness identifications play an integral role in determining guilt during legal proceedings. Past research has shown that confidence in positive identifications is strongly associated with accuracy. Using a standard lineup recognition paradigm, we investigated accuracy using signal detection and ROC analyses, along with the tendency to choose a face with both simultaneous and sequential lineups. We replicated past findings of reduced rates of choosing with sequential as compared to simultaneous lineups, but notably found an accuracy advantage in favor of simultaneous lineups. Moreover, our analysis of the confidence-accuracy relationship revealed two key findings. First, we observed a sequential mistaken identification overconfidence effect: despite an overall reduction in false alarms, confidence for false alarms that did occur was higher with sequential lineups than with simultaneous lineups, with no differences in confidence for correct identifications. This sequential mistaken identification overconfidence effect is an expected byproduct of the use of a more conservative identification criterion with sequential than with simultaneous lineups. Second, we found a steady drop in confidence for mistaken identifications (i.e., foil identifications and false alarms) from the first to the last face in sequential lineups, whereas confidence in and accuracy of correct identifications remained relatively stable. Overall, we observed that sequential lineups are both less accurate and produce higher confidence false identifications than do simultaneous lineups. Given the increasing prominence of sequential lineups in our legal system, our data argue for increased scrutiny and possibly a wholesale reevaluation of this lineup format. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Uncertainty quantification using evidence theory in multidisciplinary design optimization

International Nuclear Information System (INIS)

Agarwal, Harish; Renaud, John E.; Preston, Evan L.; Padmanabhan, Dhanesh

2004-01-01

Advances in computational performance have led to the development of large-scale simulation tools for design. Systems generated using such simulation tools can fail in service if the uncertainty of the simulation tool's performance predictions is not accounted for. In this research an investigation of how uncertainty can be quantified in multidisciplinary systems analysis subject to epistemic uncertainty associated with the disciplinary design tools and input parameters is undertaken. Evidence theory is used to quantify uncertainty in terms of the uncertain measures of belief and plausibility. To illustrate the methodology, multidisciplinary analysis problems are introduced as an extension to the epistemic uncertainty challenge problems identified by Sandia National Laboratories. After uncertainty has been characterized mathematically the designer seeks the optimum design under uncertainty. The measures of uncertainty provided by evidence theory are discontinuous functions. Such non-smooth functions cannot be used in traditional gradient-based optimizers because the sensitivities of the uncertain measures are not properly defined. In this research surrogate models are used to represent the uncertain measures as continuous functions. A sequential approximate optimization approach is used to drive the optimization process. The methodology is illustrated in application to multidisciplinary example problems

Thermodynamic performance analysis of sequential Carnot cycles using heat sources with finite heat capacity

International Nuclear Information System (INIS)

Park, Hansaem; Kim, Min Soo

2014-01-01

The maximum efficiency of a heat engine is able to be estimated by using a Carnot cycle. Even though, in terms of efficiency, the Carnot cycle performs the role of reference very well, its application is limited to the case of infinite heat reservoirs, which is not that realistic. Moreover, considering that one of the recent key issues is to produce maximum work from low temperature and finite heat sources, which are called renewable energy sources, more advanced theoretical cycles, which can present a new standard, and the research about them are necessary. Therefore, in this paper, a sequential Carnot cycle, where multiple Carnot cycles are connected in parallel, is studied. The cycle adopts a finite heat source, which has a certain initial temperature and heat capacity, and an infinite heat sink, which is assumed to be ambient air. Heat transfer processes in the cycle occur with the temperature difference between a heat reservoir and a cycle. In order to resolve the heat transfer rate in those processes, the product of an overall heat transfer coefficient and a heat transfer area is introduced. Using these conditions, the performance of a sequential Carnot cycle is analytically calculated. Furthermore, as the efforts for enhancing the work of the cycle, the optimization research is also conducted with numerical calculation. - Highlights: • Modified sequential Carnot cycles are proposed for evaluating low grade heat sources. • Performance of sequential Carnot cycles is calculated analytically. • Optimization study for the cycle is conducted with numerical solver. • Maximum work from a heat source under a certain condition is obtained by equations

A Sequential Statistical Approach towards an Optimized Production of a Broad Spectrum Bacteriocin Substance from a Soil Bacterium Bacillus sp. YAS 1 Strain

Directory of Open Access Journals (Sweden)

Amira M. Embaby

2014-01-01

Full Text Available Bacteriocins, ribosomally synthesized antimicrobial peptides, display potential applications in agriculture, medicine, and industry. The present study highlights integral statistical optimization and partial characterization of a bacteriocin substance from a soil bacterium taxonomically affiliated as Bacillus sp. YAS 1 after biochemical and molecular identifications. A sequential statistical approach (Plackett-Burman and Box-Behnken was employed to optimize bacteriocin (BAC YAS 1 production. Using optimal levels of three key determinants (yeast extract (0.48% (w/v, incubation time (62 hrs, and agitation speed (207 rpm in peptone yeast beef based production medium resulted in 1.6-fold enhancement in BAC YAS 1 level (470 AU/mL arbitrary units against Erwinia amylovora. BAC YAS 1 showed activity over a wide range of pH (1–13 and temperature (45–80°C. A wide spectrum antimicrobial activity of BAC YAS 1 against the human pathogens (Clostridium perfringens, Staphylococcus epidermidis, Campylobacter jejuni, Enterobacter aerogenes, Enterococcus sp., Proteus sp., Klebsiella sp., and Salmonella typhimurium, the plant pathogen (E. amylovora, and the food spoiler (Listeria innocua was demonstrated. On top and above, BAC YAS 1 showed no antimicrobial activity towards lactic acid bacteria (Lactobacillus bulgaricus, L. casei, L. lactis, and L. reuteri. Promising characteristics of BAC YAS 1 prompt its commercialization for efficient utilization in several industries.

Choosing the Optimal Number of B-spline Control Points (Part 1: Methodology and Approximation of Curves)

Science.gov (United States)

Harmening, Corinna; Neuner, Hans

2016-09-01

Due to the establishment of terrestrial laser scanner, the analysis strategies in engineering geodesy change from pointwise approaches to areal ones. These areal analysis strategies are commonly built on the modelling of the acquired point clouds. Freeform curves and surfaces like B-spline curves/surfaces are one possible approach to obtain space continuous information. A variety of parameters determines the B-spline's appearance; the B-spline's complexity is mostly determined by the number of control points. Usually, this number of control points is chosen quite arbitrarily by intuitive trial-and-error-procedures. In this paper, the Akaike Information Criterion and the Bayesian Information Criterion are investigated with regard to a justified and reproducible choice of the optimal number of control points of B-spline curves. Additionally, we develop a method which is based on the structural risk minimization of the statistical learning theory. Unlike the Akaike and the Bayesian Information Criteria this method doesn't use the number of parameters as complexity measure of the approximating functions but their Vapnik-Chervonenkis-dimension. Furthermore, it is also valid for non-linear models. Thus, the three methods differ in their target function to be minimized and consequently in their definition of optimality. The present paper will be continued by a second paper dealing with the choice of the optimal number of control points of B-spline surfaces.

Complex energies from real perturbation series for the LoSurdo-Stark effect in hydrogen by Borel-Pade approximants

Energy Technology Data Exchange (ETDEWEB)

Franceschini, V.; Grecchi, V.; Silverstone, H.J.

1985-09-01

The resonance energies for the hydrogen atom in an electric field, both the real and imaginary parts, have been calculated together from the real Rayleigh-Schroedinger perturbation series by Borel summation. Pade approximants were used to evaluate the Borel transform. The numerical results compare well with values obtained by the complex-coordinate variational method and by sequential use of Pade approximants.

Approximation properties of haplotype tagging

Directory of Open Access Journals (Sweden)

Dreiseitl Stephan

2006-01-01

Full Text Available Abstract Background Single nucleotide polymorphisms (SNPs are locations at which the genomic sequences of population members differ. Since these differences are known to follow patterns, disease association studies are facilitated by identifying SNPs that allow the unique identification of such patterns. This process, known as haplotype tagging, is formulated as a combinatorial optimization problem and analyzed in terms of complexity and approximation properties. Results It is shown that the tagging problem is NP-hard but approximable within 1 + ln((n2 - n/2 for n haplotypes but not approximable within (1 - ε ln(n/2 for any ε > 0 unless NP ⊂ DTIME(nlog log n. A simple, very easily implementable algorithm that exhibits the above upper bound on solution quality is presented. This algorithm has running time O((2m - p + 1 ≤ O(m(n2 - n/2 where p ≤ min(n, m for n haplotypes of size m. As we show that the approximation bound is asymptotically tight, the algorithm presented is optimal with respect to this asymptotic bound. Conclusion The haplotype tagging problem is hard, but approachable with a fast, practical, and surprisingly simple algorithm that cannot be significantly improved upon on a single processor machine. Hence, significant improvement in computatational efforts expended can only be expected if the computational effort is distributed and done in parallel.

Choosing of optimal start approximation for laplace equation ...

African Journals Online (AJOL)

We investigate Dirichlet problem for a case of two-dimensional area with lime border, numerical scheme for solving this equation is widely knowns it finite difference method. One of the major stages in the algorithm for that numerical solution is choosing of start approximation, usually as the initial values of the unknown ...

Remarks on sequential designs in risk assessment

International Nuclear Information System (INIS)

Seidenfeld, T.

1982-01-01

The special merits of sequential designs are reviewed in light of particular challenges that attend risk assessment for human population. The kinds of ''statistical inference'' are distinguished and the problem of design which is pursued is the clash between Neyman-Pearson and Bayesian programs of sequential design. The value of sequential designs is discussed and the Neyman-Pearson vs. Bayesian sequential designs are probed in particular. Finally, warnings with sequential designs are considered, especially in relation to utilitarianism

Evaluation of uranium and arsenic retention by soil from a low level radioactive waste management site using sequential extraction

International Nuclear Information System (INIS)

Evans, G.J.; Dhoum, R.T.

1998-01-01

The European Communities Bureau of Reference (BCR) and Chunguo sequential extraction procedures were employed to evaluate the retention of U and As by a soil contaminated with low level radioactive waste. Modifications were made to both procedures to optimize the measurement of soil and extractant samples using epithermal neutron activation analysis. Based on the BCR procedure, approximately 20% of the U appeared to be bound to the carbonate fraction, 10% to the mineral oxide fraction and 20% to the organic fraction. In the case of As, the majority was strongly bound in the residue fraction. The results obtained with the Chunguo procedure supported these conclusions to some extent, in that the majority of the U and As was found to be strongly bound to the soil in a manner consistent with its presence in the residue fraction. (Copyright (c) 1998 Elsevier Science B.V., Amsterdam. All rights reserved.)

Speciation fingerprints of binary mixtures by the optimized sequential two-phase separation

International Nuclear Information System (INIS)

Macasek, F.

1995-01-01

The analysis of the separation methods suitable for chemical speciation of radionuclides and metals, and advantages of sequential (double) distribution technique were discussed. The equilibria are relatively easy to control and the method enables to minimize a matrix composition adjustment, and therefore it minimizes also the disturbance of original (native) state of elements. The technique may consist in the repeat solvent extraction of sample, or the replicate equilibration with sorbent. The common condition of applicability is a linear separation isotherm of the species, what is mostly a reasonable condition in case of trace concentrations. The equations used for simultaneous fitting were written in general form. 1 tab., 1 fig., 2 refs

Optimization of approximate decision rules relative to number of misclassifications: Comparison of greedy and dynamic programming approaches

KAUST Repository

Amin, Talha

2013-01-01

In the paper, we present a comparison of dynamic programming and greedy approaches for construction and optimization of approximate decision rules relative to the number of misclassifications. We use an uncertainty measure that is a difference between the number of rows in a decision table T and the number of rows with the most common decision for T. For a nonnegative real number γ, we consider γ-decision rules that localize rows in subtables of T with uncertainty at most γ. Experimental results with decision tables from the UCI Machine Learning Repository are also presented. © 2013 Springer-Verlag.

Sequential lineup laps and eyewitness accuracy.

Science.gov (United States)

Steblay, Nancy K; Dietrich, Hannah L; Ryan, Shannon L; Raczynski, Jeanette L; James, Kali A

2011-08-01

Police practice of double-blind sequential lineups prompts a question about the efficacy of repeated viewings (laps) of the sequential lineup. Two laboratory experiments confirmed the presence of a sequential lap effect: an increase in witness lineup picks from first to second lap, when the culprit was a stranger. The second lap produced more errors than correct identifications. In Experiment 2, lineup diagnosticity was significantly higher for sequential lineup procedures that employed a single versus double laps. Witnesses who elected to view a second lap made significantly more errors than witnesses who chose to stop after one lap or those who were required to view two laps. Witnesses with prior exposure to the culprit did not exhibit a sequential lap effect.

Stochastic optimization methods

CERN Document Server

Marti, Kurt

2005-01-01

Optimization problems arising in practice involve random parameters. For the computation of robust optimal solutions, i.e., optimal solutions being insensitive with respect to random parameter variations, deterministic substitute problems are needed. Based on the distribution of the random data, and using decision theoretical concepts, optimization problems under stochastic uncertainty are converted into deterministic substitute problems. Due to the occurring probabilities and expectations, approximative solution techniques must be applied. Deterministic and stochastic approximation methods and their analytical properties are provided: Taylor expansion, regression and response surface methods, probability inequalities, First Order Reliability Methods, convex approximation/deterministic descent directions/efficient points, stochastic approximation methods, differentiation of probability and mean value functions. Convergence results of the resulting iterative solution procedures are given.

Robustness of the Sequential Lineup Advantage

Science.gov (United States)

Gronlund, Scott D.; Carlson, Curt A.; Dailey, Sarah B.; Goodsell, Charles A.

2009-01-01

A growing movement in the United States and around the world involves promoting the advantages of conducting an eyewitness lineup in a sequential manner. We conducted a large study (N = 2,529) that included 24 comparisons of sequential versus simultaneous lineups. A liberal statistical criterion revealed only 2 significant sequential lineup…

Iterative regularization in intensity-modulated radiation therapy optimization

International Nuclear Information System (INIS)

Carlsson, Fredrik; Forsgren, Anders

2006-01-01

A common way to solve intensity-modulated radiation therapy (IMRT) optimization problems is to use a beamlet-based approach. The approach is usually employed in a three-step manner: first a beamlet-weight optimization problem is solved, then the fluence profiles are converted into step-and-shoot segments, and finally postoptimization of the segment weights is performed. A drawback of beamlet-based approaches is that beamlet-weight optimization problems are ill-conditioned and have to be regularized in order to produce smooth fluence profiles that are suitable for conversion. The purpose of this paper is twofold: first, to explain the suitability of solving beamlet-based IMRT problems by a BFGS quasi-Newton sequential quadratic programming method with diagonal initial Hessian estimate, and second, to empirically show that beamlet-weight optimization problems should be solved in relatively few iterations when using this optimization method. The explanation of the suitability is based on viewing the optimization method as an iterative regularization method. In iterative regularization, the optimization problem is solved approximately by iterating long enough to obtain a solution close to the optimal one, but terminating before too much noise occurs. Iterative regularization requires an optimization method that initially proceeds in smooth directions and makes rapid initial progress. Solving ten beamlet-based IMRT problems with dose-volume objectives and bounds on the beamlet-weights, we find that the considered optimization method fulfills the requirements for performing iterative regularization. After segment-weight optimization, the treatments obtained using 35 beamlet-weight iterations outperform the treatments obtained using 100 beamlet-weight iterations, both in terms of objective value and of target uniformity. We conclude that iterating too long may in fact deteriorate the quality of the deliverable plan

Multi-agent sequential hypothesis testing

KAUST Repository

Kim, Kwang-Ki K.; Shamma, Jeff S.

2014-01-01

incorporate costs of taking private/public measurements, costs of time-difference and disagreement in actions of agents, and costs of false declaration/choices in the sequential hypothesis testing. The corresponding sequential decision processes have well

Reliability-Based Optimization of Series Systems of Parallel Systems

DEFF Research Database (Denmark)

Enevoldsen, I.; Sørensen, John Dalsgaard

Reliability-based design of structural systems is considered. Especially systems where the reliability model is a series system of parallel systems are analysed. A sensitivity analysis for this class of problems is presented. Direct and sequential optimization procedures to solve the optimization...

Sequential Therapy in Metastatic Renal Cell Carcinoma

Directory of Open Access Journals (Sweden)

Bradford R Hirsch

2016-04-01

Full Text Available The treatment of metastatic renal cell carcinoma (mRCC has changed dramatically in the past decade. As the number of available agents, and related volume of research, has grown, it is increasingly complex to know how to optimally treat patients. The authors are practicing medical oncologists at the US Oncology Network, the largest community-based network of oncology providers in the country, and represent the leadership of the Network's Genitourinary Research Committee. We outline our thought process in approaching sequential therapy of mRCC and the use of real-world data to inform our approach. We also highlight the evolving literature that will impact practicing oncologists in the near future.

Sequential Ensembles Tolerant to Synthetic Aperture Radar (SAR Soil Moisture Retrieval Errors

Directory of Open Access Journals (Sweden)

Ju Hyoung Lee

2016-04-01

Full Text Available Due to complicated and undefined systematic errors in satellite observation, data assimilation integrating model states with satellite observations is more complicated than field measurements-based data assimilation at a local scale. In the case of Synthetic Aperture Radar (SAR soil moisture, the systematic errors arising from uncertainties in roughness conditions are significant and unavoidable, but current satellite bias correction methods do not resolve the problems very well. Thus, apart from the bias correction process of satellite observation, it is important to assess the inherent capability of satellite data assimilation in such sub-optimal but more realistic observational error conditions. To this end, time-evolving sequential ensembles of the Ensemble Kalman Filter (EnKF is compared with stationary ensemble of the Ensemble Optimal Interpolation (EnOI scheme that does not evolve the ensembles over time. As the sensitivity analysis demonstrated that the surface roughness is more sensitive to the SAR retrievals than measurement errors, it is a scope of this study to monitor how data assimilation alters the effects of roughness on SAR soil moisture retrievals. In results, two data assimilation schemes all provided intermediate values between SAR overestimation, and model underestimation. However, under the same SAR observational error conditions, the sequential ensembles approached a calibrated model showing the lowest Root Mean Square Error (RMSE, while the stationary ensemble converged towards the SAR observations exhibiting the highest RMSE. As compared to stationary ensembles, sequential ensembles have a better tolerance to SAR retrieval errors. Such inherent nature of EnKF suggests an operational merit as a satellite data assimilation system, due to the limitation of bias correction methods currently available.

Sequential Optimization of Global Sequence Alignments Relative to Different Cost Functions

KAUST Repository

Odat, Enas M.

2011-01-01

The algorithm has been simulated using C#.Net programming language and a number of experiments have been done to verify the proved statements. The results of these experiments show that the number of optimal alignments is reduced after each step of optimization. Furthermore, it has been verified that as the sequence length increased linearly then the number of optimal alignments increased exponentially which also depends on the cost function that is used. Finally, the number of executed operations increases polynomially as the sequence length increase linearly.

Parameter sampling capabilities of sequential and simultaneous data assimilation: I. Analytical comparison

International Nuclear Information System (INIS)

Fossum, Kristian; Mannseth, Trond

2014-01-01

We assess the parameter sampling capabilities of some Bayesian, ensemble-based, joint state-parameter (JS) estimation methods. The forward model is assumed to be non-chaotic and have nonlinear components, and the emphasis is on results obtained for the parameters in the state-parameter vector. A variety of approximate sampling methods exist, and a number of numerical comparisons between such methods have been performed. Often, more than one of the defining characteristics vary from one method to another, so it can be difficult to point out which characteristic of the more successful method in such a comparison was decisive. In this study, we single out one defining characteristic for comparison; whether or not data are assimilated sequentially or simultaneously. The current paper is concerned with analytical investigations into this issue. We carefully select one sequential and one simultaneous JS method for the comparison. We also design a corresponding pair of pure parameter estimation methods, and we show how the JS methods and the parameter estimation methods are pairwise related. It is shown that the sequential and the simultaneous parameter estimation methods are equivalent for one particular combination of observations with different degrees of nonlinearity. Strong indications are presented for why one may expect the sequential parameter estimation method to outperform the simultaneous parameter estimation method for all other combinations of observations. Finally, the conditions for when similar relations can be expected to hold between the corresponding JS methods are discussed. A companion paper, part II (Fossum and Mannseth 2014 Inverse Problems 30 114003), is concerned with statistical analysis of results from a range of numerical experiments involving sequential and simultaneous JS estimation, where the design of the numerical investigation is motivated by our findings in the current paper. (paper)

Fully vs. Sequentially Coupled Loads Analysis of Offshore Wind Turbines

Energy Technology Data Exchange (ETDEWEB)

Damiani, Rick; Wendt, Fabian; Musial, Walter; Finucane, Z.; Hulliger, L.; Chilka, S.; Dolan, D.; Cushing, J.; O' Connell, D.; Falk, S.

2017-06-19

The design and analysis methods for offshore wind turbines must consider the aerodynamic and hydrodynamic loads and response of the entire system (turbine, tower, substructure, and foundation) coupled to the turbine control system dynamics. Whereas a fully coupled (turbine and support structure) modeling approach is more rigorous, intellectual property concerns can preclude this approach. In fact, turbine control system algorithms and turbine properties are strictly guarded and often not shared. In many cases, a partially coupled analysis using separate tools and an exchange of reduced sets of data via sequential coupling may be necessary. In the sequentially coupled approach, the turbine and substructure designers will independently determine and exchange an abridged model of their respective subsystems to be used in their partners' dynamic simulations. Although the ability to achieve design optimization is sacrificed to some degree with a sequentially coupled analysis method, the central question here is whether this approach can deliver the required safety and how the differences in the results from the fully coupled method could affect the design. This work summarizes the scope and preliminary results of a study conducted for the Bureau of Safety and Environmental Enforcement aimed at quantifying differences between these approaches through aero-hydro-servo-elastic simulations of two offshore wind turbines on a monopile and jacket substructure.

Sequential unconstrained minimization algorithms for constrained optimization

International Nuclear Information System (INIS)

Byrne, Charles

2008-01-01

The problem of minimizing a function f(x):R J → R, subject to constraints on the vector variable x, occurs frequently in inverse problems. Even without constraints, finding a minimizer of f(x) may require iterative methods. We consider here a general class of iterative algorithms that find a solution to the constrained minimization problem as the limit of a sequence of vectors, each solving an unconstrained minimization problem. Our sequential unconstrained minimization algorithm (SUMMA) is an iterative procedure for constrained minimization. At the kth step we minimize the function G k (x)=f(x)+g k (x), to obtain x k . The auxiliary functions g k (x):D subset of R J → R + are nonnegative on the set D, each x k is assumed to lie within D, and the objective is to minimize the continuous function f:R J → R over x in the set C = D-bar, the closure of D. We assume that such minimizers exist, and denote one such by x-circumflex. We assume that the functions g k (x) satisfy the inequalities 0≤g k (x)≤G k-1 (x)-G k-1 (x k-1 ), for k = 2, 3, .... Using this assumption, we show that the sequence {(x k )} is decreasing and converges to f(x-circumflex). If the restriction of f(x) to D has bounded level sets, which happens if x-circumflex is unique and f(x) is closed, proper and convex, then the sequence {x k } is bounded, and f(x*)=f(x-circumflex), for any cluster point x*. Therefore, if x-circumflex is unique, x* = x-circumflex and {x k } → x-circumflex. When x-circumflex is not unique, convergence can still be obtained, in particular cases. The SUMMA includes, as particular cases, the well-known barrier- and penalty-function methods, the simultaneous multiplicative algebraic reconstruction technique (SMART), the proximal minimization algorithm of Censor and Zenios, the entropic proximal methods of Teboulle, as well as certain cases of gradient descent and the Newton–Raphson method. The proof techniques used for SUMMA can be extended to obtain related results

Sequential and simultaneous choices: testing the diet selection and sequential choice models.

Science.gov (United States)

Freidin, Esteban; Aw, Justine; Kacelnik, Alex

2009-03-01

We investigate simultaneous and sequential choices in starlings, using Charnov's Diet Choice Model (DCM) and Shapiro, Siller and Kacelnik's Sequential Choice Model (SCM) to integrate function and mechanism. During a training phase, starlings encountered one food-related option per trial (A, B or R) in random sequence and with equal probability. A and B delivered food rewards after programmed delays (shorter for A), while R ('rejection') moved directly to the next trial without reward. In this phase we measured latencies to respond. In a later, choice, phase, birds encountered the pairs A-B, A-R and B-R, the first implementing a simultaneous choice and the second and third sequential choices. The DCM predicts when R should be chosen to maximize intake rate, and SCM uses latencies of the training phase to predict choices between any pair of options in the choice phase. The predictions of both models coincided, and both successfully predicted the birds' preferences. The DCM does not deal with partial preferences, while the SCM does, and experimental results were strongly correlated to this model's predictions. We believe that the SCM may expose a very general mechanism of animal choice, and that its wider domain of success reflects the greater ecological significance of sequential over simultaneous choices.

Sequential memory: Binding dynamics

Science.gov (United States)

Afraimovich, Valentin; Gong, Xue; Rabinovich, Mikhail

2015-10-01

Temporal order memories are critical for everyday animal and human functioning. Experiments and our own experience show that the binding or association of various features of an event together and the maintaining of multimodality events in sequential order are the key components of any sequential memories—episodic, semantic, working, etc. We study a robustness of binding sequential dynamics based on our previously introduced model in the form of generalized Lotka-Volterra equations. In the phase space of the model, there exists a multi-dimensional binding heteroclinic network consisting of saddle equilibrium points and heteroclinic trajectories joining them. We prove here the robustness of the binding sequential dynamics, i.e., the feasibility phenomenon for coupled heteroclinic networks: for each collection of successive heteroclinic trajectories inside the unified networks, there is an open set of initial points such that the trajectory going through each of them follows the prescribed collection staying in a small neighborhood of it. We show also that the symbolic complexity function of the system restricted to this neighborhood is a polynomial of degree L - 1, where L is the number of modalities.

A dynamic regrouping based sequential dynamic programming algorithm for unit commitment of combined heat and power systems

DEFF Research Database (Denmark)

Rong, Aiying; Hakonen, Henri; Lahdelma, Risto

2009-01-01

efficiency of the plants. We introduce in this paper the DRDP-RSC algorithm, which is a dynamic regrouping based dynamic programming (DP) algorithm based on linear relaxation of the ON/OFF states of the units, sequential commitment of units in small groups. Relaxed states of the plants are used to reduce...... the dimension of the UC problem and dynamic regrouping is used to improve the solution quality. Numerical results based on real-life data sets show that this algorithm is efficient and optimal or near-optimal solutions with very small optimality gap are obtained....

Sequential Probability Ration Tests : Conservative and Robust

NARCIS (Netherlands)

Kleijnen, J.P.C.; Shi, Wen

2017-01-01

In practice, most computers generate simulation outputs sequentially, so it is attractive to analyze these outputs through sequential statistical methods such as sequential probability ratio tests (SPRTs). We investigate several SPRTs for choosing between two hypothesized values for the mean output

APPROXIMATIONS TO PERFORMANCE MEASURES IN QUEUING SYSTEMS

Directory of Open Access Journals (Sweden)

Kambo, N. S.

2012-11-01

Full Text Available Approximations to various performance measures in queuing systems have received considerable attention because these measures have wide applicability. In this paper we propose two methods to approximate the queuing characteristics of a GI/M/1 system. The first method is non-parametric in nature, using only the first three moments of the arrival distribution. The second method treads the known path of approximating the arrival distribution by a mixture of two exponential distributions by matching the first three moments. Numerical examples and optimal analysis of performance measures of GI/M/1 queues are provided to illustrate the efficacy of the methods, and are compared with benchmark approximations.

Optimal Computing Budget Allocation for Particle Swarm Optimization in Stochastic Optimization.

Science.gov (United States)

Zhang, Si; Xu, Jie; Lee, Loo Hay; Chew, Ek Peng; Wong, Wai Peng; Chen, Chun-Hung

2017-04-01

Particle Swarm Optimization (PSO) is a popular metaheuristic for deterministic optimization. Originated in the interpretations of the movement of individuals in a bird flock or fish school, PSO introduces the concept of personal best and global best to simulate the pattern of searching for food by flocking and successfully translate the natural phenomena to the optimization of complex functions. Many real-life applications of PSO cope with stochastic problems. To solve a stochastic problem using PSO, a straightforward approach is to equally allocate computational effort among all particles and obtain the same number of samples of fitness values. This is not an efficient use of computational budget and leaves considerable room for improvement. This paper proposes a seamless integration of the concept of optimal computing budget allocation (OCBA) into PSO to improve the computational efficiency of PSO for stochastic optimization problems. We derive an asymptotically optimal allocation rule to intelligently determine the number of samples for all particles such that the PSO algorithm can efficiently select the personal best and global best when there is stochastic estimation noise in fitness values. We also propose an easy-to-implement sequential procedure. Numerical tests show that our new approach can obtain much better results using the same amount of computational effort.

Uncertainty relations for approximation and estimation

Energy Technology Data Exchange (ETDEWEB)

Lee, Jaeha, E-mail: jlee@post.kek.jp [Department of Physics, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033 (Japan); Tsutsui, Izumi, E-mail: izumi.tsutsui@kek.jp [Department of Physics, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033 (Japan); Theory Center, Institute of Particle and Nuclear Studies, High Energy Accelerator Research Organization (KEK), 1-1 Oho, Tsukuba, Ibaraki 305-0801 (Japan)

2016-05-27

We present a versatile inequality of uncertainty relations which are useful when one approximates an observable and/or estimates a physical parameter based on the measurement of another observable. It is shown that the optimal choice for proxy functions used for the approximation is given by Aharonov's weak value, which also determines the classical Fisher information in parameter estimation, turning our inequality into the genuine Cramér–Rao inequality. Since the standard form of the uncertainty relation arises as a special case of our inequality, and since the parameter estimation is available as well, our inequality can treat both the position–momentum and the time–energy relations in one framework albeit handled differently. - Highlights: • Several inequalities interpreted as uncertainty relations for approximation/estimation are derived from a single ‘versatile inequality’. • The ‘versatile inequality’ sets a limit on the approximation of an observable and/or the estimation of a parameter by another observable. • The ‘versatile inequality’ turns into an elaboration of the Robertson–Kennard (Schrödinger) inequality and the Cramér–Rao inequality. • Both the position–momentum and the time–energy relation are treated in one framework. • In every case, Aharonov's weak value arises as a key geometrical ingredient, deciding the optimal choice for the proxy functions.

Uncertainty relations for approximation and estimation

International Nuclear Information System (INIS)

Lee, Jaeha; Tsutsui, Izumi

2016-01-01

We present a versatile inequality of uncertainty relations which are useful when one approximates an observable and/or estimates a physical parameter based on the measurement of another observable. It is shown that the optimal choice for proxy functions used for the approximation is given by Aharonov's weak value, which also determines the classical Fisher information in parameter estimation, turning our inequality into the genuine Cramér–Rao inequality. Since the standard form of the uncertainty relation arises as a special case of our inequality, and since the parameter estimation is available as well, our inequality can treat both the position–momentum and the time–energy relations in one framework albeit handled differently. - Highlights: • Several inequalities interpreted as uncertainty relations for approximation/estimation are derived from a single ‘versatile inequality’. • The ‘versatile inequality’ sets a limit on the approximation of an observable and/or the estimation of a parameter by another observable. • The ‘versatile inequality’ turns into an elaboration of the Robertson–Kennard (Schrödinger) inequality and the Cramér–Rao inequality. • Both the position–momentum and the time–energy relation are treated in one framework. • In every case, Aharonov's weak value arises as a key geometrical ingredient, deciding the optimal choice for the proxy functions.

Sequential lineup presentation: Patterns and policy

OpenAIRE

Lindsay, R C L; Mansour, Jamal K; Beaudry, J L; Leach, A-M; Bertrand, M I

2009-01-01

Sequential lineups were offered as an alternative to the traditional simultaneous lineup. Sequential lineups reduce incorrect lineup selections; however, the accompanying loss of correct identifications has resulted in controversy regarding adoption of the technique. We discuss the procedure and research relevant to (1) the pattern of results found using sequential versus simultaneous lineups; (2) reasons (theory) for differences in witness responses; (3) two methodological issues; and (4) im...

Sequential Product of Quantum Effects: An Overview

Science.gov (United States)

Gudder, Stan

2010-12-01

This article presents an overview for the theory of sequential products of quantum effects. We first summarize some of the highlights of this relatively recent field of investigation and then provide some new results. We begin by discussing sequential effect algebras which are effect algebras endowed with a sequential product satisfying certain basic conditions. We then consider sequential products of (discrete) quantum measurements. We next treat transition effect matrices (TEMs) and their associated sequential product. A TEM is a matrix whose entries are effects and whose rows form quantum measurements. We show that TEMs can be employed for the study of quantum Markov chains. Finally, we prove some new results concerning TEMs and vector densities.

Low Rank Approximation Algorithms, Implementation, Applications

CERN Document Server

Markovsky, Ivan

2012-01-01

Matrix low-rank approximation is intimately related to data modelling; a problem that arises frequently in many different fields. Low Rank Approximation: Algorithms, Implementation, Applications is a comprehensive exposition of the theory, algorithms, and applications of structured low-rank approximation. Local optimization methods and effective suboptimal convex relaxations for Toeplitz, Hankel, and Sylvester structured problems are presented. A major part of the text is devoted to application of the theory. Applications described include: system and control theory: approximate realization, model reduction, output error, and errors-in-variables identification; signal processing: harmonic retrieval, sum-of-damped exponentials, finite impulse response modeling, and array processing; machine learning: multidimensional scaling and recommender system; computer vision: algebraic curve fitting and fundamental matrix estimation; bioinformatics for microarray data analysis; chemometrics for multivariate calibration; ...

Digital-Control-Based Approximation of Optimal Wave Disturbances Attenuation for Nonlinear Offshore Platforms

Directory of Open Access Journals (Sweden)

Xiao-Fang Zhong

2017-12-01

Full Text Available The irregular wave disturbance attenuation problem for jacket-type offshore platforms involving the nonlinear characteristics is studied. The main contribution is that a digital-control-based approximation of optimal wave disturbances attenuation controller (AOWDAC is proposed based on iteration control theory, which consists of a feedback item of offshore state, a feedforward item of wave force and a nonlinear compensated component with iterative sequences. More specifically, by discussing the discrete model of nonlinear offshore platform subject to wave forces generated from the Joint North Sea Wave Project (JONSWAP wave spectrum and linearized wave theory, the original wave disturbances attenuation problem is formulated as the nonlinear two-point-boundary-value (TPBV problem. By introducing two vector sequences of system states and nonlinear compensated item, the solution of introduced nonlinear TPBV problem is obtained. Then, a numerical algorithm is designed to realize the feasibility of AOWDAC based on the deviation of performance index between the adjacent iteration processes. Finally, applied the proposed AOWDAC to a jacket-type offshore platform in Bohai Bay, the vibration amplitudes of the displacement and the velocity, and the required energy consumption can be reduced significantly.

Approximate convex hull of affine iterated function system attractors

International Nuclear Information System (INIS)

Mishkinis, Anton; Gentil, Christian; Lanquetin, Sandrine; Sokolov, Dmitry

2012-01-01

Highlights: ► We present an iterative algorithm to approximate affine IFS attractor convex hull. ► Elimination of the interior points significantly reduces the complexity. ► To optimize calculations, we merge the convex hull images at each iteration. ► Approximation by ellipses increases speed of convergence to the exact convex hull. ► We present a method of the output convex hull simplification. - Abstract: In this paper, we present an algorithm to construct an approximate convex hull of the attractors of an affine iterated function system (IFS). We construct a sequence of convex hull approximations for any required precision using the self-similarity property of the attractor in order to optimize calculations. Due to the affine properties of IFS transformations, the number of points considered in the construction is reduced. The time complexity of our algorithm is a linear function of the number of iterations and the number of points in the output approximate convex hull. The number of iterations and the execution time increases logarithmically with increasing accuracy. In addition, we introduce a method to simplify the approximate convex hull without loss of accuracy.

Prestack wavefield approximations

KAUST Repository

Alkhalifah, Tariq

2013-01-01

The double-square-root (DSR) relation offers a platform to perform prestack imaging using an extended single wavefield that honors the geometrical configuration between sources, receivers, and the image point, or in other words, prestack wavefields. Extrapolating such wavefields, nevertheless, suffers from limitations. Chief among them is the singularity associated with horizontally propagating waves. I have devised highly accurate approximations free of such singularities which are highly accurate. Specifically, I use Padé expansions with denominators given by a power series that is an order lower than that of the numerator, and thus, introduce a free variable to balance the series order and normalize the singularity. For the higher-order Padé approximation, the errors are negligible. Additional simplifications, like recasting the DSR formula as a function of scattering angle, allow for a singularity free form that is useful for constant-angle-gather imaging. A dynamic form of this DSR formula can be supported by kinematic evaluations of the scattering angle to provide efficient prestack wavefield construction. Applying a similar approximation to the dip angle yields an efficient 1D wave equation with the scattering and dip angles extracted from, for example, DSR ray tracing. Application to the complex Marmousi data set demonstrates that these approximations, although they may provide less than optimal results, allow for efficient and flexible implementations. © 2013 Society of Exploration Geophysicists.

Prestack wavefield approximations

KAUST Repository

Alkhalifah, Tariq

2013-09-01

The double-square-root (DSR) relation offers a platform to perform prestack imaging using an extended single wavefield that honors the geometrical configuration between sources, receivers, and the image point, or in other words, prestack wavefields. Extrapolating such wavefields, nevertheless, suffers from limitations. Chief among them is the singularity associated with horizontally propagating waves. I have devised highly accurate approximations free of such singularities which are highly accurate. Specifically, I use Padé expansions with denominators given by a power series that is an order lower than that of the numerator, and thus, introduce a free variable to balance the series order and normalize the singularity. For the higher-order Padé approximation, the errors are negligible. Additional simplifications, like recasting the DSR formula as a function of scattering angle, allow for a singularity free form that is useful for constant-angle-gather imaging. A dynamic form of this DSR formula can be supported by kinematic evaluations of the scattering angle to provide efficient prestack wavefield construction. Applying a similar approximation to the dip angle yields an efficient 1D wave equation with the scattering and dip angles extracted from, for example, DSR ray tracing. Application to the complex Marmousi data set demonstrates that these approximations, although they may provide less than optimal results, allow for efficient and flexible implementations. © 2013 Society of Exploration Geophysicists.

Introducing sequential managed aquifer recharge technology (SMART) - From laboratory to full-scale application.

Science.gov (United States)

Regnery, Julia; Wing, Alexandre D; Kautz, Jessica; Drewes, Jörg E

2016-07-01

Previous lab-scale studies demonstrated that stimulating the indigenous soil microbial community of groundwater recharge systems by manipulating the availability of biodegradable organic carbon (BDOC) and establishing sequential redox conditions in the subsurface resulted in enhanced removal of compounds with redox-dependent removal behavior such as trace organic chemicals. The aim of this study is to advance this concept from laboratory to full-scale application by introducing sequential managed aquifer recharge technology (SMART). To validate the concept of SMART, a full-scale managed aquifer recharge (MAR) facility in Colorado was studied for three years that featured the proposed sequential configuration: A short riverbank filtration passage followed by subsequent re-aeration and artificial recharge and recovery. Our findings demonstrate that sequential subsurface treatment zones characterized by carbon-rich (>3 mg/L BDOC) to carbon-depleted (≤1 mg/L BDOC) and predominant oxic redox conditions can be established at full-scale MAR facilities adopting the SMART concept. The sequential configuration resulted in substantially improved trace organic chemical removal (i.e. higher biodegradation rate coefficients) for moderately biodegradable compounds compared to conventional MAR systems with extended travel times in an anoxic aquifer. Furthermore, sorption batch experiments with clay materials dispersed in the subsurface implied that sorptive processes might also play a role in the attenuation and retardation of chlorinated flame retardants during MAR. Hence, understanding key factors controlling trace organic chemical removal performance during SMART allows for systems to be engineered for optimal efficiency, resulting in improved removal of constituents at shorter subsurface travel times and a potentially reduced physical footprint of MAR installations. Copyright © 2016 Elsevier Ltd. All rights reserved.

Results of simultaneous and sequential pediatric liver and kidney transplantation.

Science.gov (United States)

Rogers, J; Bueno, J; Shapiro, R; Scantlebury, V; Mazariegos, G; Fung, J; Reyes, J

2001-11-27

(86%) of seven sequentially transplanted kidneys developed acute cellular rejection compared with only two (25%) of eight simultaneously transplanted kidneys (P<0.04). Simultaneously transplanted kidneys were less likely to develop rejection than sequentially transplanted kidneys in this series. This did not have any bearing on patient or graft survival rates. Mortality correlated directly with the severity of United Network of Organ Sharing status at the time of kidney transplantation. Candidates for simultaneous or sequential LTx/KTx should be prioritized based on medical stability to optimize distribution of scarce renal allografts.

Erratum to ''Johnson's algorithm : A key to solve optimally or approximately flowshop scheduling problems with unavailability periods'' [International Journal of Production Economics 121 (2009) 81-87

OpenAIRE

Rapine , Christophe

2013-01-01

International audience; In Allaoui H., Artiba A, ''Johnson's algorithm : A key to solve optimally or approximately flowshop scheduling problems with unavailability periods'' [International Journal of Production Economics 121 (2009)] the authors propose optimality conditions for the Johnson sequence in presence of one unavailability period on the first machine and pretend for a performance guarantee of 2 when several unavailability periods may occur. We establish in this note that these condit...

Optimal Advertising with Stochastic Demand

OpenAIRE

George E. Monahan

1983-01-01

A stochastic, sequential model is developed to determine optimal advertising expenditures as a function of product maturity and past advertising. Random demand for the product depends upon an aggregate measure of current and past advertising called "goodwill," and the position of the product in its life cycle measured by sales-to-date. Conditions on the parameters of the model are established that insure that it is optimal to advertise less as the product matures. Additional characteristics o...

PARTICLE FILTERING WITH SEQUENTIAL PARAMETER LEARNING FOR NONLINEAR BOLD fMRI SIGNALS.

Science.gov (United States)

Xia, Jing; Wang, Michelle Yongmei

Analyzing the blood oxygenation level dependent (BOLD) effect in the functional magnetic resonance imaging (fMRI) is typically based on recent ground-breaking time series analysis techniques. This work represents a significant improvement over existing approaches to system identification using nonlinear hemodynamic models. It is important for three reasons. First, instead of using linearized approximations of the dynamics, we present a nonlinear filtering based on the sequential Monte Carlo method to capture the inherent nonlinearities in the physiological system. Second, we simultaneously estimate the hidden physiological states and the system parameters through particle filtering with sequential parameter learning to fully take advantage of the dynamic information of the BOLD signals. Third, during the unknown static parameter learning, we employ the low-dimensional sufficient statistics for efficiency and avoiding potential degeneration of the parameters. The performance of the proposed method is validated using both the simulated data and real BOLD fMRI data.

Parameter sampling capabilities of sequential and simultaneous data assimilation: II. Statistical analysis of numerical results

International Nuclear Information System (INIS)

Fossum, Kristian; Mannseth, Trond

2014-01-01

We assess and compare parameter sampling capabilities of one sequential and one simultaneous Bayesian, ensemble-based, joint state-parameter (JS) estimation method. In the companion paper, part I (Fossum and Mannseth 2014 Inverse Problems 30 114002), analytical investigations lead us to propose three claims, essentially stating that the sequential method can be expected to outperform the simultaneous method for weakly nonlinear forward models. Here, we assess the reliability and robustness of these claims through statistical analysis of results from a range of numerical experiments. Samples generated by the two approximate JS methods are compared to samples from the posterior distribution generated by a Markov chain Monte Carlo method, using four approximate measures of distance between probability distributions. Forward-model nonlinearity is assessed from a stochastic nonlinearity measure allowing for sufficiently large model dimensions. Both toy models (with low computational complexity, and where the nonlinearity is fairly easy to control) and two-phase porous-media flow models (corresponding to down-scaled versions of problems to which the JS methods have been frequently applied recently) are considered in the numerical experiments. Results from the statistical analysis show strong support of all three claims stated in part I. (paper)

Quantum Inequalities and Sequential Measurements

International Nuclear Information System (INIS)

Candelpergher, B.; Grandouz, T.; Rubinx, J.L.

2011-01-01

In this article, the peculiar context of sequential measurements is chosen in order to analyze the quantum specificity in the two most famous examples of Heisenberg and Bell inequalities: Results are found at some interesting variance with customary textbook materials, where the context of initial state re-initialization is described. A key-point of the analysis is the possibility of defining Joint Probability Distributions for sequential random variables associated to quantum operators. Within the sequential context, it is shown that Joint Probability Distributions can be defined in situations where not all of the quantum operators (corresponding to random variables) do commute two by two. (authors)

Results of improvement of simultaneous and sequential x-ray fluorescence equipment for quantitative routine analysis

International Nuclear Information System (INIS)

Zsamboky, Jozsef

1985-01-01

Two main types of x-ray fluorescence analyzers measuring sequentially and simultaneously, respectively, the intensities at given wave lengths are described. The main parts of an up to date x-ray fluorescence analyzer are surveyed in detail. The advantages and disadvantages of both methods are discussed. Some results on calibration and optimization are given. (D.Gy.)

Efficient approximation of black-box functions and Pareto sets

NARCIS (Netherlands)

Rennen, G.

2009-01-01

In the case of time-consuming simulation models or other so-called black-box functions, we determine a metamodel which approximates the relation between the input- and output-variables of the simulation model. To solve multi-objective optimization problems, we approximate the Pareto set, i.e. the

Impact of controlling the sum of error probability in the sequential probability ratio test

Directory of Open Access Journals (Sweden)

Bijoy Kumarr Pradhan

2013-05-01

Full Text Available A generalized modified method is proposed to control the sum of error probabilities in sequential probability ratio test to minimize the weighted average of the two average sample numbers under a simple null hypothesis and a simple alternative hypothesis with the restriction that the sum of error probabilities is a pre-assigned constant to find the optimal sample size and finally a comparison is done with the optimal sample size found from fixed sample size procedure. The results are applied to the cases when the random variate follows a normal law as well as Bernoullian law.

Improved Reliability-Based Optimization with Support Vector Machines and Its Application in Aircraft Wing Design

Directory of Open Access Journals (Sweden)

Yu Wang

2015-01-01

Full Text Available A new reliability-based design optimization (RBDO method based on support vector machines (SVM and the Most Probable Point (MPP is proposed in this work. SVM is used to create a surrogate model of the limit-state function at the MPP with the gradient information in the reliability analysis. This guarantees that the surrogate model not only passes through the MPP but also is tangent to the limit-state function at the MPP. Then, importance sampling (IS is used to calculate the probability of failure based on the surrogate model. This treatment significantly improves the accuracy of reliability analysis. For RBDO, the Sequential Optimization and Reliability Assessment (SORA is employed as well, which decouples deterministic optimization from the reliability analysis. The improved SVM-based reliability analysis is used to amend the error from linear approximation for limit-state function in SORA. A mathematical example and a simplified aircraft wing design demonstrate that the improved SVM-based reliability analysis is more accurate than FORM and needs less training points than the Monte Carlo simulation and that the proposed optimization strategy is efficient.

Applications of sub-optimality in dynamic programming to location and construction of nuclear fuel processing plant

International Nuclear Information System (INIS)

Thiriet, L.; Deledicq, A.

1968-09-01

First, the point of applying Dynamic Programming to optimization and Operational Research problems in chemical industries are recalled, as well as the conditions in which a dynamic program is illustrated by a sequential graph. A new algorithm for the determination of sub-optimal politics in a sequential graph is then developed. Finally, the applications of sub-optimality concept is shown when taking into account the indirect effects related to possible strategies, or in the case of stochastic choices and of problems of the siting of plants... application examples are given. (authors) [fr

Information/disturbance trade-off in single and sequential measurements on a qudit signal

Energy Technology Data Exchange (ETDEWEB)

Genoni, Marco G; Paris, Matteo G A [Dipartimento di Fisica, Universita degli studi di Milano (Italy)

2007-05-15

We address the trade-off between information gain and state disturbance in measurement performed on qudit systems and devise a class of optimal measurement schemes that saturate the ultimate bound imposed by quantum mechanics to estimation and transmission fidelities. The schemes are minimal, i.e. they involve a single additional probe qudit, and optimal, i.e. they provide the maximum amount of information compatible with a given level of disturbance. The performances of optimal single-user schemes in extracting information by sequential measurements in a N-user transmission line are also investigated, and the optimality is analyzed by explicit evaluation of fidelities. We found that the estimation fidelity does not depend on the number of users, neither for single-measure inference nor for collective one, whereas the transmission fidelity decreases with N. The resulting trade-off is no longer optimal and degrades with increasing N. We found that optimality can be restored by an effective preparation of the probe states and present explicitly calculations for the 2-user case.

Sequential reduction–oxidation for photocatalytic degradation of tetrabromobisphenol A: Kinetics and intermediates

International Nuclear Information System (INIS)

Guo, Yaoguang; Lou, Xiaoyi; Xiao, Dongxue; Xu, Lei; Wang, Zhaohui; Liu, Jianshe

2012-01-01

Highlights: ► Sequential photocatalytic reduction–oxidation degradation of TBBPA was firstly examined. ► Different atmospheres were found to have significant effect on debromination reaction. ► A possible sequential photocatalytic reduction–oxidation pathway was proposed. - Abstract: C-Br bond cleavage is considered as a key step to reduce their toxicities and increase degradation rates for most brominated organic pollutants. Here a sequential reduction/oxidation strategy (i.e. debromination followed by photocatalytic oxidation) for photocatalytic degradation of tetrabromobisphenol A (TBBPA), one of the most frequently used brominated flame retardants, was proposed on the basis of kinetic analysis and intermediates identification. The results demonstrated that the rates of debromination and even photodegradation of TBBPA strongly depended on the atmospheres, initial TBBPA concentrations, pH of the reaction solution, hydrogen donors, and electron acceptors. These kinetic data and byproducts identification obtained by GC–MS measurement indicated that reductive debromination reaction by photo-induced electrons dominated under N 2 -saturated condition, while oxidation reaction by photoexcited holes or hydroxyl radicals played a leading role when air was saturated. It also suggested that the reaction might be further optimized for pretreatment of TBBPA-contaminated wastewater by a two-stage reductive debromination/subsequent oxidative decomposition process in the UV-TiO 2 system by changing the reaction atmospheres.

Using Chebyshev polynomials and approximate inverse triangular factorizations for preconditioning the conjugate gradient method

Science.gov (United States)

Kaporin, I. E.

2012-02-01

In order to precondition a sparse symmetric positive definite matrix, its approximate inverse is examined, which is represented as the product of two sparse mutually adjoint triangular matrices. In this way, the solution of the corresponding system of linear algebraic equations (SLAE) by applying the preconditioned conjugate gradient method (CGM) is reduced to performing only elementary vector operations and calculating sparse matrix-vector products. A method for constructing the above preconditioner is described and analyzed. The triangular factor has a fixed sparsity pattern and is optimal in the sense that the preconditioned matrix has a minimum K-condition number. The use of polynomial preconditioning based on Chebyshev polynomials makes it possible to considerably reduce the amount of scalar product operations (at the cost of an insignificant increase in the total number of arithmetic operations). The possibility of an efficient massively parallel implementation of the resulting method for solving SLAEs is discussed. For a sequential version of this method, the results obtained by solving 56 test problems from the Florida sparse matrix collection (which are large-scale and ill-conditioned) are presented. These results show that the method is highly reliable and has low computational costs.

NMPC for Oil Reservoir Production Optimization

DEFF Research Database (Denmark)

Völcker, Carsten; Jørgensen, John Bagterp; Thomsen, Per Grove

2011-01-01

this problem numerically using a single shooting sequential quadratic programming (SQP) based optimization method. Explicit singly diagonally implicit Runge-Kutta (ESDIRK) methods are used for integration of the stiff system of differential equations describing the two-phase flow, and the adjoint method...

Steering Evolution with Sequential Therapy to Prevent the Emergence of Bacterial Antibiotic Resistance.

Directory of Open Access Journals (Sweden)

Daniel Nichol

2015-09-01

Full Text Available The increasing rate of antibiotic resistance and slowing discovery of novel antibiotic treatments presents a growing threat to public health. Here, we consider a simple model of evolution in asexually reproducing populations which considers adaptation as a biased random walk on a fitness landscape. This model associates the global properties of the fitness landscape with the algebraic properties of a Markov chain transition matrix and allows us to derive general results on the non-commutativity and irreversibility of natural selection as well as antibiotic cycling strategies. Using this formalism, we analyze 15 empirical fitness landscapes of E. coli under selection by different β-lactam antibiotics and demonstrate that the emergence of resistance to a given antibiotic can be either hindered or promoted by different sequences of drug application. Specifically, we demonstrate that the majority, approximately 70%, of sequential drug treatments with 2-4 drugs promote resistance to the final antibiotic. Further, we derive optimal drug application sequences with which we can probabilistically 'steer' the population through genotype space to avoid the emergence of resistance. This suggests a new strategy in the war against antibiotic-resistant organisms: drug sequencing to shepherd evolution through genotype space to states from which resistance cannot emerge and by which to maximize the chance of successful therapy.

Multilevel sequential Monte Carlo samplers

KAUST Repository

Beskos, Alexandros; Jasra, Ajay; Law, Kody; Tempone, Raul; Zhou, Yan

2016-01-01

In this article we consider the approximation of expectations w.r.t. probability distributions associated to the solution of partial differential equations (PDEs); this scenario appears routinely in Bayesian inverse problems. In practice, one often has to solve the associated PDE numerically, using, for instance finite element methods which depend on the step-size level . hL. In addition, the expectation cannot be computed analytically and one often resorts to Monte Carlo methods. In the context of this problem, it is known that the introduction of the multilevel Monte Carlo (MLMC) method can reduce the amount of computational effort to estimate expectations, for a given level of error. This is achieved via a telescoping identity associated to a Monte Carlo approximation of a sequence of probability distributions with discretization levels . ∞>h0>h1⋯>hL. In many practical problems of interest, one cannot achieve an i.i.d. sampling of the associated sequence and a sequential Monte Carlo (SMC) version of the MLMC method is introduced to deal with this problem. It is shown that under appropriate assumptions, the attractive property of a reduction of the amount of computational effort to estimate expectations, for a given level of error, can be maintained within the SMC context. That is, relative to exact sampling and Monte Carlo for the distribution at the finest level . hL. The approach is numerically illustrated on a Bayesian inverse problem. © 2016 Elsevier B.V.

Multilevel sequential Monte Carlo samplers

KAUST Repository

Beskos, Alexandros

2016-08-29

In this article we consider the approximation of expectations w.r.t. probability distributions associated to the solution of partial differential equations (PDEs); this scenario appears routinely in Bayesian inverse problems. In practice, one often has to solve the associated PDE numerically, using, for instance finite element methods which depend on the step-size level . hL. In addition, the expectation cannot be computed analytically and one often resorts to Monte Carlo methods. In the context of this problem, it is known that the introduction of the multilevel Monte Carlo (MLMC) method can reduce the amount of computational effort to estimate expectations, for a given level of error. This is achieved via a telescoping identity associated to a Monte Carlo approximation of a sequence of probability distributions with discretization levels . ∞>h0>h1⋯>hL. In many practical problems of interest, one cannot achieve an i.i.d. sampling of the associated sequence and a sequential Monte Carlo (SMC) version of the MLMC method is introduced to deal with this problem. It is shown that under appropriate assumptions, the attractive property of a reduction of the amount of computational effort to estimate expectations, for a given level of error, can be maintained within the SMC context. That is, relative to exact sampling and Monte Carlo for the distribution at the finest level . hL. The approach is numerically illustrated on a Bayesian inverse problem. © 2016 Elsevier B.V.

Error Estimates for the Approximation of the Effective Hamiltonian

International Nuclear Information System (INIS)

Camilli, Fabio; Capuzzo Dolcetta, Italo; Gomes, Diogo A.

2008-01-01

We study approximation schemes for the cell problem arising in homogenization of Hamilton-Jacobi equations. We prove several error estimates concerning the rate of convergence of the approximation scheme to the effective Hamiltonian, both in the optimal control setting and as well as in the calculus of variations setting

Sequential Combination of Electro-Fenton and Electrochemical Chlorination Processes for the Treatment of Anaerobically-Digested Food Wastewater.

Science.gov (United States)

Shin, Yong-Uk; Yoo, Ha-Young; Kim, Seonghun; Chung, Kyung-Mi; Park, Yong-Gyun; Hwang, Kwang-Hyun; Hong, Seok Won; Park, Hyunwoong; Cho, Kangwoo; Lee, Jaesang

2017-09-19

A two-stage sequential electro-Fenton (E-Fenton) oxidation followed by electrochemical chlorination (EC) was demonstrated to concomitantly treat high concentrations of organic carbon and ammonium nitrogen (NH 4 + -N) in real anaerobically digested food wastewater (ADFW). The anodic Fenton process caused the rapid mineralization of phenol as a model substrate through the production of hydroxyl radical as the main oxidant. The electrochemical oxidation of NH 4 + by a dimensionally stable anode (DSA) resulted in temporal concentration profiles of combined and free chlorine species that were analogous to those during the conventional breakpoint chlorination of NH 4 + . Together with the minimal production of nitrate, this confirmed that the conversion of NH 4 + to nitrogen gas was electrochemically achievable. The monitoring of treatment performance with varying key parameters (e.g., current density, H 2 O 2 feeding rate, pH, NaCl loading, and DSA type) led to the optimization of two component systems. The comparative evaluation of two sequentially combined systems (i.e., the E-Fenton-EC system versus the EC-E-Fenton system) using the mixture of phenol and NH 4 + under the predetermined optimal conditions suggested the superiority of the E-Fenton-EC system in terms of treatment efficiency and energy consumption. Finally, the sequential E-Fenton-EC process effectively mineralized organic carbon and decomposed NH 4 + -N in the real ADFW without external supply of NaCl.

Sequential Generalized Transforms on Function Space

Directory of Open Access Journals (Sweden)

Jae Gil Choi

2013-01-01

Full Text Available We define two sequential transforms on a function space Ca,b[0,T] induced by generalized Brownian motion process. We then establish the existence of the sequential transforms for functionals in a Banach algebra of functionals on Ca,b[0,T]. We also establish that any one of these transforms acts like an inverse transform of the other transform. Finally, we give some remarks about certain relations between our sequential transforms and other well-known transforms on Ca,b[0,T].

Methods of Fourier analysis and approximation theory

CERN Document Server

Tikhonov, Sergey

2016-01-01

Different facets of interplay between harmonic analysis and approximation theory are covered in this volume. The topics included are Fourier analysis, function spaces, optimization theory, partial differential equations, and their links to modern developments in the approximation theory. The articles of this collection were originated from two events. The first event took place during the 9th ISAAC Congress in Krakow, Poland, 5th-9th August 2013, at the section “Approximation Theory and Fourier Analysis”. The second event was the conference on Fourier Analysis and Approximation Theory in the Centre de Recerca Matemàtica (CRM), Barcelona, during 4th-8th November 2013, organized by the editors of this volume. All articles selected to be part of this collection were carefully reviewed.

Forced Sequence Sequential Decoding

DEFF Research Database (Denmark)

Jensen, Ole Riis; Paaske, Erik

1998-01-01

We describe a new concatenated decoding scheme based on iterations between an inner sequentially decoded convolutional code of rate R=1/4 and memory M=23, and block interleaved outer Reed-Solomon (RS) codes with nonuniform profile. With this scheme decoding with good performance is possible as low...... as Eb/N0=0.6 dB, which is about 1.25 dB below the signal-to-noise ratio (SNR) that marks the cutoff rate for the full system. Accounting for about 0.45 dB due to the outer codes, sequential decoding takes place at about 1.7 dB below the SNR cutoff rate for the convolutional code. This is possible since...... the iteration process provides the sequential decoders with side information that allows a smaller average load and minimizes the probability of computational overflow. Analytical results for the probability that the first RS word is decoded after C computations are presented. These results are supported...

Reliability-Based Topology Optimization Using Stochastic Response Surface Method with Sparse Grid Design

Directory of Open Access Journals (Sweden)

Qinghai Zhao

2015-01-01

Full Text Available A mathematical framework is developed which integrates the reliability concept into topology optimization to solve reliability-based topology optimization (RBTO problems under uncertainty. Two typical methodologies have been presented and implemented, including the performance measure approach (PMA and the sequential optimization and reliability assessment (SORA. To enhance the computational efficiency of reliability analysis, stochastic response surface method (SRSM is applied to approximate the true limit state function with respect to the normalized random variables, combined with the reasonable design of experiments generated by sparse grid design, which was proven to be an effective and special discretization technique. The uncertainties such as material property and external loads are considered on three numerical examples: a cantilever beam, a loaded knee structure, and a heat conduction problem. Monte-Carlo simulations are also performed to verify the accuracy of the failure probabilities computed by the proposed approach. Based on the results, it is demonstrated that application of SRSM with SGD can produce an efficient reliability analysis in RBTO which enables a more reliable design than that obtained by DTO. It is also found that, under identical accuracy, SORA is superior to PMA in view of computational efficiency.

Finite approximations in discrete-time stochastic control quantized models and asymptotic optimality

CERN Document Server

Saldi, Naci; Yüksel, Serdar

2018-01-01

In a unified form, this monograph presents fundamental results on the approximation of centralized and decentralized stochastic control problems, with uncountable state, measurement, and action spaces. It demonstrates how quantization provides a system-independent and constructive method for the reduction of a system with Borel spaces to one with finite state, measurement, and action spaces. In addition to this constructive view, the book considers both the information transmission approach for discretization of actions, and the computational approach for discretization of states and actions. Part I of the text discusses Markov decision processes and their finite-state or finite-action approximations, while Part II builds from there to finite approximations in decentralized stochastic control problems. This volume is perfect for researchers and graduate students interested in stochastic controls. With the tools presented, readers will be able to establish the convergence of approximation models to original mo...

Sequential probability ratio controllers for safeguards radiation monitors

International Nuclear Information System (INIS)

Fehlau, P.E.; Coop, K.L.; Nixon, K.V.

1984-01-01

Sequential hypothesis tests applied to nuclear safeguards accounting methods make the methods more sensitive to detecting diversion. The sequential tests also improve transient signal detection in safeguards radiation monitors. This paper describes three microprocessor control units with sequential probability-ratio tests for detecting transient increases in radiation intensity. The control units are designed for three specific applications: low-intensity monitoring with Poisson probability ratios, higher intensity gamma-ray monitoring where fixed counting intervals are shortened by sequential testing, and monitoring moving traffic where the sequential technique responds to variable-duration signals. The fixed-interval controller shortens a customary 50-s monitoring time to an average of 18 s, making the monitoring delay less bothersome. The controller for monitoring moving vehicles benefits from the sequential technique by maintaining more than half its sensitivity when the normal passage speed doubles

Large-grain polycrystalline silicon film by sequential lateral solidification on a plastic substrate

International Nuclear Information System (INIS)

Kim, Yong-Hae; Chung, Choong-Heui; Yun, Sun Jin; Moon, Jaehyun; Park, Dong-Jin; Kim, Dae-Won; Lim, Jung Wook; Song, Yoon-Ho; Lee, Jin Ho

2005-01-01

A large-grain polycrystalline silicon film was obtained on a plastic substrate by sequential lateral solidification. With various combinations of sputtering powers and Ar working gas pressures, the conditions for producing dense amorphous silicon (a-Si) and SiO 2 films were optimized. The successful crystallization of the a-Si film is attributed to the production of a dense a-Si film that has low argon content and can endure high-intensity laser irradiation

Double ionization in Helium. Ab initio calculations beyond the one dimensional approximation

International Nuclear Information System (INIS)

Camilo Ruiz; Luis Plaja; Luis Roso; Andreas Becker

2006-01-01

Complete test of publication follows. We present ab-initio computations of the ionization of two-electron atoms by short pulses of coherent radiation beyond the one-dimensional approximation. In the model the electron correlation is included in its full dimensionality, while the center-of-mass motion is restricted along the polarization axis. We show some result for Non Sequential Double Ionization (NSDI) as well as for SDI for high intensity low IR frequency. Some recent applications for this correlated system is also presented.

Sequential biventricular pacing improves regional contractility, longitudinal function and dyssynchrony in patients with heart failure and prolonged QRS

Directory of Open Access Journals (Sweden)

Ring Margareta

2010-04-01

Full Text Available Abstract Aims Biventricular pacing (BiP is an effective treatment in systolic heart failure (HF patients with prolonged QRS. However, approximately 35% of the patients receiving BiP are classified as non-responders. The aim of this study is to evaluate the acute effects of VV-optimization on systolic heart function. Methods Twenty-one HF patients aged 72 (46-88 years, QRS 154 (120-190 ms, were studied with echocardiography, Tissue Doppler Imaging (TDI and 3D-echo the first day after receiving a BiP device. TDI was performed; during simultaneous pacing (LV-lead pacing 4 ms before the RV-lead and during sequential pacing (LV 20 and 40 ms before RV and RV 20 and 40 ms before LV-lead pacing. Systolic heart function was studied by tissue tracking (TT for longitudinal function and systolic maximal velocity (SMV for regional contractility and signs of dyssynchrony assessed by time-delays standard deviation of aortic valve opening to SMV, AVO-SMV/SD and tissue synchronization imaging (TSI. Results The TT mean value preoperatively was 4,2 ± 1,5 and increased at simultaneous pacing to 5,0 ± 1,2 mm (p Conclusions VV-optimization in the acute phase improves systolic heart function more than simultaneous BiP pacing. Long-term effects should be evaluated in prospective randomized trials.

Biased lineups: sequential presentation reduces the problem.

Science.gov (United States)

Lindsay, R C; Lea, J A; Nosworthy, G J; Fulford, J A; Hector, J; LeVan, V; Seabrook, C

1991-12-01

Biased lineups have been shown to increase significantly false, but not correct, identification rates (Lindsay, Wallbridge, & Drennan, 1987; Lindsay & Wells, 1980; Malpass & Devine, 1981). Lindsay and Wells (1985) found that sequential lineup presentation reduced false identification rates, presumably by reducing reliance on relative judgment processes. Five staged-crime experiments were conducted to examine the effect of lineup biases and sequential presentation on eyewitness recognition accuracy. Sequential lineup presentation significantly reduced false identification rates from fair lineups as well as from lineups biased with regard to foil similarity, instructions, or witness attire, and from lineups biased in all of these ways. The results support recommendations that police present lineups sequentially.

Footprints of Optimal Protein Assembly Strategies in the Operonic Structure of Prokaryotes

Directory of Open Access Journals (Sweden)

Jan Ewald

2015-04-01

Full Text Available In this work, we investigate optimality principles behind synthesis strategies for protein complexes using a dynamic optimization approach. We show that the cellular capacity of protein synthesis has a strong influence on optimal synthesis strategies reaching from a simultaneous to a sequential synthesis of the subunits of a protein complex. Sequential synthesis is preferred if protein synthesis is strongly limited, whereas a simultaneous synthesis is optimal in situations with a high protein synthesis capacity. We confirm the predictions of our optimization approach through the analysis of the operonic organization of protein complexes in several hundred prokaryotes. Thereby, we are able to show that cellular protein synthesis capacity is a driving force in the dissolution of operons comprising the subunits of a protein complex. Thus, we also provide a tested hypothesis explaining why the subunits of many prokaryotic protein complexes are distributed across several operons despite the presumably less precise co-regulation.

A Numerical Approximation Framework for the Stochastic Linear Quadratic Regulator on Hilbert Spaces

Energy Technology Data Exchange (ETDEWEB)

Levajković, Tijana, E-mail: tijana.levajkovic@uibk.ac.at, E-mail: t.levajkovic@sf.bg.ac.rs; Mena, Hermann, E-mail: hermann.mena@uibk.ac.at [University of Innsbruck, Department of Mathematics (Austria); Tuffaha, Amjad, E-mail: atufaha@aus.edu [American University of Sharjah, Department of Mathematics (United Arab Emirates)

2017-06-15

We present an approximation framework for computing the solution of the stochastic linear quadratic control problem on Hilbert spaces. We focus on the finite horizon case and the related differential Riccati equations (DREs). Our approximation framework is concerned with the so-called “singular estimate control systems” (Lasiecka in Optimal control problems and Riccati equations for systems with unbounded controls and partially analytic generators: applications to boundary and point control problems, 2004) which model certain coupled systems of parabolic/hyperbolic mixed partial differential equations with boundary or point control. We prove that the solutions of the approximate finite-dimensional DREs converge to the solution of the infinite-dimensional DRE. In addition, we prove that the optimal state and control of the approximate finite-dimensional problem converge to the optimal state and control of the corresponding infinite-dimensional problem.

Lineup composition, suspect position, and the sequential lineup advantage.

Science.gov (United States)

Carlson, Curt A; Gronlund, Scott D; Clark, Steven E

2008-06-01

N. M. Steblay, J. Dysart, S. Fulero, and R. C. L. Lindsay (2001) argued that sequential lineups reduce the likelihood of mistaken eyewitness identification. Experiment 1 replicated the design of R. C. L. Lindsay and G. L. Wells (1985), the first study to show the sequential lineup advantage. However, the innocent suspect was chosen at a lower rate in the simultaneous lineup, and no sequential lineup advantage was found. This led the authors to hypothesize that protection from a sequential lineup might emerge only when an innocent suspect stands out from the other lineup members. In Experiment 2, participants viewed a simultaneous or sequential lineup with either the guilty suspect or 1 of 3 innocent suspects. Lineup fairness was varied to influence the degree to which a suspect stood out. A sequential lineup advantage was found only for the unfair lineups. Additional analyses of suspect position in the sequential lineups showed an increase in the diagnosticity of suspect identifications as the suspect was placed later in the sequential lineup. These results suggest that the sequential lineup advantage is dependent on lineup composition and suspect position. (c) 2008 APA, all rights reserved

Sequential inference as a mode of cognition and its correlates in fronto-parietal and hippocampal brain regions.

Directory of Open Access Journals (Sweden)

Thomas H B FitzGerald

2017-05-01

Full Text Available Normative models of human cognition often appeal to Bayesian filtering, which provides optimal online estimates of unknown or hidden states of the world, based on previous observations. However, in many cases it is necessary to optimise beliefs about sequences of states rather than just the current state. Importantly, Bayesian filtering and sequential inference strategies make different predictions about beliefs and subsequent choices, rendering them behaviourally dissociable. Taking data from a probabilistic reversal task we show that subjects' choices provide strong evidence that they are representing short sequences of states. Between-subject measures of this implicit sequential inference strategy had a neurobiological underpinning and correlated with grey matter density in prefrontal and parietal cortex, as well as the hippocampus. Our findings provide, to our knowledge, the first evidence for sequential inference in human cognition, and by exploiting between-subject variation in this measure we provide pointers to its neuronal substrates.

Sequential Optimization of Paths in Directed Graphs Relative to Different Cost Functions

KAUST Repository

Mahayni, Malek A.

2011-01-01

developed to solve the optimal paths problem with different kinds of graphs. An algorithm that solves the problem of paths’ optimization in directed graphs relative to different cost functions is described in [1]. It follows an approach extended from

Comparison of Sequential and Variational Data Assimilation

Science.gov (United States)

Alvarado Montero, Rodolfo; Schwanenberg, Dirk; Weerts, Albrecht

2017-04-01

Data assimilation is a valuable tool to improve model state estimates by combining measured observations with model simulations. It has recently gained significant attention due to its potential in using remote sensing products to improve operational hydrological forecasts and for reanalysis purposes. This has been supported by the application of sequential techniques such as the Ensemble Kalman Filter which require no additional features within the modeling process, i.e. it can use arbitrary black-box models. Alternatively, variational techniques rely on optimization algorithms to minimize a pre-defined objective function. This function describes the trade-off between the amount of noise introduced into the system and the mismatch between simulated and observed variables. While sequential techniques have been commonly applied to hydrological processes, variational techniques are seldom used. In our believe, this is mainly attributed to the required computation of first order sensitivities by algorithmic differentiation techniques and related model enhancements, but also to lack of comparison between both techniques. We contribute to filling this gap and present the results from the assimilation of streamflow data in two basins located in Germany and Canada. The assimilation introduces noise to precipitation and temperature to produce better initial estimates of an HBV model. The results are computed for a hindcast period and assessed using lead time performance metrics. The study concludes with a discussion of the main features of each technique and their advantages/disadvantages in hydrological applications.

Sequential sampling of visual objects during sustained attention.

Directory of Open Access Journals (Sweden)

Jianrong Jia

2017-06-01

Full Text Available In a crowded visual scene, attention must be distributed efficiently and flexibly over time and space to accommodate different contexts. It is well established that selective attention enhances the corresponding neural responses, presumably implying that attention would persistently dwell on the task-relevant item. Meanwhile, recent studies, mostly in divided attentional contexts, suggest that attention does not remain stationary but samples objects alternately over time, suggesting a rhythmic view of attention. However, it remains unknown whether the dynamic mechanism essentially mediates attentional processes at a general level. Importantly, there is also a complete lack of direct neural evidence reflecting whether and how the brain rhythmically samples multiple visual objects during stimulus processing. To address these issues, in this study, we employed electroencephalography (EEG and a temporal response function (TRF approach, which can dissociate responses that exclusively represent a single object from the overall neuronal activity, to examine the spatiotemporal characteristics of attention in various attentional contexts. First, attention, which is characterized by inhibitory alpha-band (approximately 10 Hz activity in TRFs, switches between attended and unattended objects every approximately 200 ms, suggesting a sequential sampling even when attention is required to mostly stay on the attended object. Second, the attentional spatiotemporal pattern is modulated by the task context, such that alpha-mediated switching becomes increasingly prominent as the task requires a more uniform distribution of attention. Finally, the switching pattern correlates with attentional behavioral performance. Our work provides direct neural evidence supporting a generally central role of temporal organization mechanism in attention, such that multiple objects are sequentially sorted according to their priority in attentional contexts. The results suggest

Tradable permit allocations and sequential choice

Energy Technology Data Exchange (ETDEWEB)

MacKenzie, Ian A. [Centre for Economic Research, ETH Zuerich, Zurichbergstrasse 18, 8092 Zuerich (Switzerland)

2011-01-15

This paper investigates initial allocation choices in an international tradable pollution permit market. For two sovereign governments, we compare allocation choices that are either simultaneously or sequentially announced. We show sequential allocation announcements result in higher (lower) aggregate emissions when announcements are strategic substitutes (complements). Whether allocation announcements are strategic substitutes or complements depends on the relationship between the follower's damage function and governments' abatement costs. When the marginal damage function is relatively steep (flat), allocation announcements are strategic substitutes (complements). For quadratic abatement costs and damages, sequential announcements provide a higher level of aggregate emissions. (author)

On NonAsymptotic Optimal Stopping Criteria in Monte Carlo Simulations

KAUST Repository

Bayer, Christian; Hoel, Hakon; von Schwerin, Erik; Tempone, Raul

2014-01-01

We consider the setting of estimating the mean of a random variable by a sequential stopping rule Monte Carlo (MC) method. The performance of a typical second moment based sequential stopping rule MC method is shown to be unreliable in such settings both by numerical examples and through analysis. By analysis and approximations, we construct a higher moment based stopping rule which is shown in numerical examples to perform more reliably and only slightly less efficiently than the second moment based stopping rule.

Smooth function approximation using neural networks.

Science.gov (United States)

Ferrari, Silvia; Stengel, Robert F

2005-01-01

An algebraic approach for representing multidimensional nonlinear functions by feedforward neural networks is presented. In this paper, the approach is implemented for the approximation of smooth batch data containing the function's input, output, and possibly, gradient information. The training set is associated to the network adjustable parameters by nonlinear weight equations. The cascade structure of these equations reveals that they can be treated as sets of linear systems. Hence, the training process and the network approximation properties can be investigated via linear algebra. Four algorithms are developed to achieve exact or approximate matching of input-output and/or gradient-based training sets. Their application to the design of forward and feedback neurocontrollers shows that algebraic training is characterized by faster execution speeds and better generalization properties than contemporary optimization techniques.

Classical and sequential limit analysis revisited

Science.gov (United States)

Leblond, Jean-Baptiste; Kondo, Djimédo; Morin, Léo; Remmal, Almahdi

2018-04-01

Classical limit analysis applies to ideal plastic materials, and within a linearized geometrical framework implying small displacements and strains. Sequential limit analysis was proposed as a heuristic extension to materials exhibiting strain hardening, and within a fully general geometrical framework involving large displacements and strains. The purpose of this paper is to study and clearly state the precise conditions permitting such an extension. This is done by comparing the evolution equations of the full elastic-plastic problem, the equations of classical limit analysis, and those of sequential limit analysis. The main conclusion is that, whereas classical limit analysis applies to materials exhibiting elasticity - in the absence of hardening and within a linearized geometrical framework -, sequential limit analysis, to be applicable, strictly prohibits the presence of elasticity - although it tolerates strain hardening and large displacements and strains. For a given mechanical situation, the relevance of sequential limit analysis therefore essentially depends upon the importance of the elastic-plastic coupling in the specific case considered.

Well Field Management Using Multi-Objective Optimization

DEFF Research Database (Denmark)

Hansen, Annette Kirstine; Hendricks Franssen, H. J.; Bauer-Gottwein, Peter

2013-01-01

with infiltration basins, injection wells and abstraction wells. The two management objectives are to minimize the amount of water needed for infiltration and to minimize the risk of getting contaminated water into the drinking water wells. The management is subject to a daily demand fulfilment constraint. Two...... different optimization methods are tested. Constant scheduling where decision variables are held constant during the time of optimization, and sequential scheduling where the optimization is performed stepwise for daily time steps. The latter is developed to work in a real-time situation. Case study...

Sequential decisions: a computational comparison of observational and reinforcement accounts.

Directory of Open Access Journals (Sweden)

Nazanin Mohammadi Sepahvand

Full Text Available Right brain damaged patients show impairments in sequential decision making tasks for which healthy people do not show any difficulty. We hypothesized that this difficulty could be due to the failure of right brain damage patients to develop well-matched models of the world. Our motivation is the idea that to navigate uncertainty, humans use models of the world to direct the decisions they make when interacting with their environment. The better the model is, the better their decisions are. To explore the model building and updating process in humans and the basis for impairment after brain injury, we used a computational model of non-stationary sequence learning. RELPH (Reinforcement and Entropy Learned Pruned Hypothesis space was able to qualitatively and quantitatively reproduce the results of left and right brain damaged patient groups and healthy controls playing a sequential version of Rock, Paper, Scissors. Our results suggests that, in general, humans employ a sub-optimal reinforcement based learning method rather than an objectively better statistical learning approach, and that differences between right brain damaged and healthy control groups can be explained by different exploration policies, rather than qualitatively different learning mechanisms.

An Approximate Proximal Bundle Method to Minimize a Class of Maximum Eigenvalue Functions

Directory of Open Access Journals (Sweden)

Wei Wang

2014-01-01

Full Text Available We present an approximate nonsmooth algorithm to solve a minimization problem, in which the objective function is the sum of a maximum eigenvalue function of matrices and a convex function. The essential idea to solve the optimization problem in this paper is similar to the thought of proximal bundle method, but the difference is that we choose approximate subgradient and function value to construct approximate cutting-plane model to solve the above mentioned problem. An important advantage of the approximate cutting-plane model for objective function is that it is more stable than cutting-plane model. In addition, the approximate proximal bundle method algorithm can be given. Furthermore, the sequences generated by the algorithm converge to the optimal solution of the original problem.

Comparison of direct machine parameter optimization versus fluence optimization with sequential sequencing in IMRT of hypopharyngeal carcinoma

International Nuclear Information System (INIS)

Dobler, Barbara; Pohl, Fabian; Bogner, Ludwig; Koelbl, Oliver

2007-01-01

To evaluate the effects of direct machine parameter optimization in the treatment planning of intensity-modulated radiation therapy (IMRT) for hypopharyngeal cancer as compared to subsequent leaf sequencing in Oncentra Masterplan v1.5. For 10 hypopharyngeal cancer patients IMRT plans were generated in Oncentra Masterplan v1.5 (Nucletron BV, Veenendal, the Netherlands) for a Siemens Primus linear accelerator. For optimization the dose volume objectives (DVO) for the planning target volume (PTV) were set to 53 Gy minimum dose and 59 Gy maximum dose, in order to reach a dose of 56 Gy to the average of the PTV. For the parotids a median dose of 22 Gy was allowed and for the spinal cord a maximum dose of 35 Gy. The maximum DVO to the external contour of the patient was set to 59 Gy. The treatment plans were optimized with the direct machine parameter optimization ('Direct Step & Shoot', DSS, Raysearch Laboratories, Sweden) newly implemented in Masterplan v1.5 and the fluence modulation technique ('Intensity Modulation', IM) which was available in previous versions of Masterplan already. The two techniques were compared with regard to compliance to the DVO, plan quality, and number of monitor units (MU) required per fraction dose. The plans optimized with the DSS technique met the DVO for the PTV significantly better than the plans optimized with IM (p = 0.007 for the min DVO and p < 0.0005 for the max DVO). No significant difference could be observed for compliance to the DVO for the organs at risk (OAR) (p > 0.05). Plan quality, target coverage and dose homogeneity inside the PTV were superior for the plans optimized with DSS for similar dose to the spinal cord and lower dose to the normal tissue. The mean dose to the parotids was lower for the plans optimized with IM. Treatment plan efficiency was higher for the DSS plans with (901 ± 160) MU compared to (1151 ± 157) MU for IM (p-value < 0.05). Renormalization of the IM plans to the mean of the

Kullback-Leibler divergence and the Pareto-Exponential approximation.

Science.gov (United States)

Weinberg, G V

2016-01-01

Recent radar research interests in the Pareto distribution as a model for X-band maritime surveillance radar clutter returns have resulted in analysis of the asymptotic behaviour of this clutter model. In particular, it is of interest to understand when the Pareto distribution is well approximated by an Exponential distribution. The justification for this is that under the latter clutter model assumption, simpler radar detection schemes can be applied. An information theory approach is introduced to investigate the Pareto-Exponential approximation. By analysing the Kullback-Leibler divergence between the two distributions it is possible to not only assess when the approximation is valid, but to determine, for a given Pareto model, the optimal Exponential approximation.

Reliability Based Optimization of Structural Systems

DEFF Research Database (Denmark)

Sørensen, John Dalsgaard

1987-01-01

The optimization problem to design structural systems such that the reliability is satisfactory during the whole lifetime of the structure is considered in this paper. Some of the quantities modelling the loads and the strength of the structure are modelled as random variables. The reliability...... is estimated using first. order reliability methods ( FORM ). The design problem is formulated as the optimization problem to minimize a given cost function such that the reliability of the single elements satisfies given requirements or such that the systems reliability satisfies a given requirement....... For these optimization problems it is described how a sensitivity analysis can be performed. Next, new optimization procedures to solve the optimization problems are presented. Two of these procedures solve the system reliability based optimization problem sequentially using quasi-analytical derivatives. Finally...

Simultaneous versus sequential penetrating keratoplasty and cataract surgery.

Science.gov (United States)

Hayashi, Ken; Hayashi, Hideyuki

2006-10-01

To compare the surgical outcomes of simultaneous penetrating keratoplasty and cataract surgery with those of sequential surgery. Thirty-nine eyes of 39 patients scheduled for simultaneous keratoplasty and cataract surgery and 23 eyes of 23 patients scheduled for sequential keratoplasty and secondary phacoemulsification surgery were recruited. Refractive error, regular and irregular corneal astigmatism determined by Fourier analysis, and endothelial cell loss were studied at 1 week and 3, 6, and 12 months after combined surgery in the simultaneous surgery group or after subsequent phacoemulsification surgery in the sequential surgery group. At 3 and more months after surgery, mean refractive error was significantly greater in the simultaneous surgery group than in the sequential surgery group, although no difference was seen at 1 week. The refractive error at 12 months was within 2 D of that targeted in 15 eyes (39%) in the simultaneous surgery group and within 2 D in 16 eyes (70%) in the sequential surgery group; the incidence was significantly greater in the sequential group (P = 0.0344). The regular and irregular astigmatism was not significantly different between the groups at 3 and more months after surgery. No significant difference was also found in the percentage of endothelial cell loss between the groups. Although corneal astigmatism and endothelial cell loss were not different, refractive error from target refraction was greater after simultaneous keratoplasty and cataract surgery than after sequential surgery, indicating a better outcome after sequential surgery than after simultaneous surgery.

Adaptive x-ray threat detection using sequential hypotheses testing with fan-beam experimental data (Conference Presentation)

Science.gov (United States)

Thamvichai, Ratchaneekorn; Huang, Liang-Chih; Ashok, Amit; Gong, Qian; Coccarelli, David; Greenberg, Joel A.; Gehm, Michael E.; Neifeld, Mark A.

2017-05-01

We employ an adaptive measurement system, based on sequential hypotheses testing (SHT) framework, for detecting material-based threats using experimental data acquired on an X-ray experimental testbed system. This testbed employs 45-degree fan-beam geometry and 15 views over a 180-degree span to generate energy sensitive X-ray projection data. Using this testbed system, we acquire multiple view projection data for 200 bags. We consider an adaptive measurement design where the X-ray projection measurements are acquired in a sequential manner and the adaptation occurs through the choice of the optimal "next" source/view system parameter. Our analysis of such an adaptive measurement design using the experimental data demonstrates a 3x-7x reduction in the probability of error relative to a static measurement design. Here the static measurement design refers to the operational system baseline that corresponds to a sequential measurement using all the available sources/views. We also show that by using adaptive measurements it is possible to reduce the number of sources/views by nearly 50% compared a system that relies on static measurements.

Dynamic Programming Approach for Exact Decision Rule Optimization

KAUST Repository

Amin, Talha M.; Chikalov, Igor; Moshkov, Mikhail; Zielosko, Beata

2013-01-01

This chapter is devoted to the study of an extension of dynamic programming approach that allows sequential optimization of exact decision rules relative to the length and coverage. It contains also results of experiments with decision tables from

Optimal control of Navier-Stokes equations by Oseen approximation

Czech Academy of Sciences Publication Activity Database

Pošta, M.; Roubíček, Tomáš

2007-01-01

Roč. 53, 3/4 (2007), s. 569-581 ISSN 0898-1221 R&D Projects: GA AV ČR IAA1075402 Grant - others:GA MŠk(CZ) LC06052 Program:LC Institutional research plan: CEZ:AV0Z10750506 Keywords : optimal control * steady flow * incompressible fluids Subject RIV: BA - General Mathematics Impact factor: 0.720, year: 2007

Particle connectedness and cluster formation in sequential depositions of particles: integral-equation theory.

Science.gov (United States)

Danwanichakul, Panu; Glandt, Eduardo D

2004-11-15

We applied the integral-equation theory to the connectedness problem. The method originally applied to the study of continuum percolation in various equilibrium systems was modified for our sequential quenching model, a particular limit of an irreversible adsorption. The development of the theory based on the (quenched-annealed) binary-mixture approximation includes the Ornstein-Zernike equation, the Percus-Yevick closure, and an additional term involving the three-body connectedness function. This function is simplified by introducing a Kirkwood-like superposition approximation. We studied the three-dimensional (3D) system of randomly placed spheres and 2D systems of square-well particles, both with a narrow and with a wide well. The results from our integral-equation theory are in good accordance with simulation results within a certain range of densities.

The impact of uncertainty on optimal emission policies

Science.gov (United States)

Botta, Nicola; Jansson, Patrik; Ionescu, Cezar

2018-05-01

We apply a computational framework for specifying and solving sequential decision problems to study the impact of three kinds of uncertainties on optimal emission policies in a stylized sequential emission problem.We find that uncertainties about the implementability of decisions on emission reductions (or increases) have a greater impact on optimal policies than uncertainties about the availability of effective emission reduction technologies and uncertainties about the implications of trespassing critical cumulated emission thresholds. The results show that uncertainties about the implementability of decisions on emission reductions (or increases) call for more precautionary policies. In other words, delaying emission reductions to the point in time when effective technologies will become available is suboptimal when these uncertainties are accounted for rigorously. By contrast, uncertainties about the implications of exceeding critical cumulated emission thresholds tend to make early emission reductions less rewarding.

A new moving strategy for the sequential Monte Carlo approach in optimizing the hydrological model parameters

Science.gov (United States)

Zhu, Gaofeng; Li, Xin; Ma, Jinzhu; Wang, Yunquan; Liu, Shaomin; Huang, Chunlin; Zhang, Kun; Hu, Xiaoli

2018-04-01

Sequential Monte Carlo (SMC) samplers have become increasing popular for estimating the posterior parameter distribution with the non-linear dependency structures and multiple modes often present in hydrological models. However, the explorative capabilities and efficiency of the sampler depends strongly on the efficiency in the move step of SMC sampler. In this paper we presented a new SMC sampler entitled the Particle Evolution Metropolis Sequential Monte Carlo (PEM-SMC) algorithm, which is well suited to handle unknown static parameters of hydrologic model. The PEM-SMC sampler is inspired by the works of Liang and Wong (2001) and operates by incorporating the strengths of the genetic algorithm, differential evolution algorithm and Metropolis-Hasting algorithm into the framework of SMC. We also prove that the sampler admits the target distribution to be a stationary distribution. Two case studies including a multi-dimensional bimodal normal distribution and a conceptual rainfall-runoff hydrologic model by only considering parameter uncertainty and simultaneously considering parameter and input uncertainty show that PEM-SMC sampler is generally superior to other popular SMC algorithms in handling the high dimensional problems. The study also indicated that it may be important to account for model structural uncertainty by using multiplier different hydrological models in the SMC framework in future study.

Non-euclidean simplex optimization. [Application to potentiometric titration of Pu

Energy Technology Data Exchange (ETDEWEB)

Silver, G.L.

1977-08-15

Geometric optimization techniques useful for studying chemical equilibrium traditionally rely upon principles of euclidean geometry, but such algorithms may also be based upon principles of a non-euclidean geometry. The sequential simplex method is adapted to the hyperbolic plane, and application of optimization to problems such as the potentiometric titration of plutonium is suggested.

Trial Sequential Methods for Meta-Analysis

Science.gov (United States)

Kulinskaya, Elena; Wood, John

2014-01-01

Statistical methods for sequential meta-analysis have applications also for the design of new trials. Existing methods are based on group sequential methods developed for single trials and start with the calculation of a required information size. This works satisfactorily within the framework of fixed effects meta-analysis, but conceptual…

Sequentially pulsed traveling wave accelerator

Science.gov (United States)

Caporaso, George J [Livermore, CA; Nelson, Scott D [Patterson, CA; Poole, Brian R [Tracy, CA

2009-08-18

A sequentially pulsed traveling wave compact accelerator having two or more pulse forming lines each with a switch for producing a short acceleration pulse along a short length of a beam tube, and a trigger mechanism for sequentially triggering the switches so that a traveling axial electric field is produced along the beam tube in synchronism with an axially traversing pulsed beam of charged particles to serially impart energy to the particle beam.

Optimal approximations for risk measures of sums of lognormals based on conditional expectations

Science.gov (United States)

Vanduffel, S.; Chen, X.; Dhaene, J.; Goovaerts, M.; Henrard, L.; Kaas, R.

2008-11-01

In this paper we investigate the approximations for the distribution function of a sum S of lognormal random variables. These approximations are obtained by considering the conditional expectation E[S|[Lambda

Short-Range Temporal Interactions in Sleep; Hippocampal Spike Avalanches Support a Large Milieu of Sequential Activity Including Replay.

Directory of Open Access Journals (Sweden)

J Matthew Mahoney

Full Text Available Hippocampal neural systems consolidate multiple complex behaviors into memory. However, the temporal structure of neural firing supporting complex memory consolidation is unknown. Replay of hippocampal place cells during sleep supports the view that a simple repetitive behavior modifies sleep firing dynamics, but does not explain how multiple episodes could be integrated into associative networks for recollection during future cognition. Here we decode sequential firing structure within spike avalanches of all pyramidal cells recorded in sleeping rats after running in a circular track. We find that short sequences that combine into multiple long sequences capture the majority of the sequential structure during sleep, including replay of hippocampal place cells. The ensemble, however, is not optimized for maximally producing the behavior-enriched episode. Thus behavioral programming of sequential correlations occurs at the level of short-range interactions, not whole behavioral sequences and these short sequences are assembled into a large and complex milieu that could support complex memory consolidation.

Sequential experimental design based generalised ANOVA

Energy Technology Data Exchange (ETDEWEB)

Chakraborty, Souvik, E-mail: csouvik41@gmail.com; Chowdhury, Rajib, E-mail: rajibfce@iitr.ac.in

2016-07-15

Over the last decade, surrogate modelling technique has gained wide popularity in the field of uncertainty quantification, optimization, model exploration and sensitivity analysis. This approach relies on experimental design to generate training points and regression/interpolation for generating the surrogate. In this work, it is argued that conventional experimental design may render a surrogate model inefficient. In order to address this issue, this paper presents a novel distribution adaptive sequential experimental design (DA-SED). The proposed DA-SED has been coupled with a variant of generalised analysis of variance (G-ANOVA), developed by representing the component function using the generalised polynomial chaos expansion. Moreover, generalised analytical expressions for calculating the first two statistical moments of the response, which are utilized in predicting the probability of failure, have also been developed. The proposed approach has been utilized in predicting probability of failure of three structural mechanics problems. It is observed that the proposed approach yields accurate and computationally efficient estimate of the failure probability.

Sequential bidding in day-ahead auctions for spot energy and power systems reserve

International Nuclear Information System (INIS)

Swider, Derk J.

2005-01-01

In this paper a novel approach for sequential bidding on day-ahead auction markets for spot energy and power systems reserve is presented. For the spot market a relatively simple method is considered as a competitive market is assumed. For the reserve market one bidder is assumed to behave strategically and the behavior of the competitors is summarized in a probability distribution of the market price. This results in a method for sequential bidding, where the bidding prices and capacities on the spot and reserve markets are calculated by maximizing a stochastic non-linear objective function of expected profit. With an exemplary application is shown that the trading sequence leads to increasing bidding capacities and prices in the reverse rank number of the markets. Hence, the consideration of a defined trading sequence greatly influences the mathematical representation of the optimal bidding behavior under price uncertainty in day-ahead auctions for spot energy and power systems reserve. (Author)

Articulated Human Motion Tracking Using Sequential Immune Genetic Algorithm

Directory of Open Access Journals (Sweden)

Yi Li

2013-01-01

Full Text Available We formulate human motion tracking as a high-dimensional constrained optimization problem. A novel generative method is proposed for human motion tracking in the framework of evolutionary computation. The main contribution is that we introduce immune genetic algorithm (IGA for pose optimization in latent space of human motion. Firstly, we perform human motion analysis in the learnt latent space of human motion. As the latent space is low dimensional and contents the prior knowledge of human motion, it makes pose analysis more efficient and accurate. Then, in the search strategy, we apply IGA for pose optimization. Compared with genetic algorithm and other evolutionary methods, its main advantage is the ability to use the prior knowledge of human motion. We design an IGA-based method to estimate human pose from static images for initialization of motion tracking. And we propose a sequential IGA (S-IGA algorithm for motion tracking by incorporating the temporal continuity information into the traditional IGA. Experimental results on different videos of different motion types show that our IGA-based pose estimation method can be used for initialization of motion tracking. The S-IGA-based motion tracking method can achieve accurate and stable tracking of 3D human motion.

Over production of fermentable sugar for bioethanol production from carbohydrate-rich Malaysian food waste via sequential acid-enzymatic hydrolysis pretreatment.

Science.gov (United States)

Hafid, Halimatun Saadiah; Nor 'Aini, Abdul Rahman; Mokhtar, Mohd Noriznan; Talib, Ahmad Tarmezee; Baharuddin, Azhari Samsu; Umi Kalsom, Md Shah

2017-09-01

In Malaysia, the amount of food waste produced is estimated at approximately 70% of total municipal solid waste generated and characterised by high amount of carbohydrate polymers such as starch, cellulose, and sugars. Considering the beneficial organic fraction contained, its utilization as an alternative substrate specifically for bioethanol production has receiving more attention. However, the sustainable production of bioethanol from food waste is linked to the efficient pretreatment needed for higher production of fermentable sugar prior to fermentation. In this work, a modified sequential acid-enzymatic hydrolysis process has been developed to produce high concentration of fermentable sugars; glucose, sucrose, fructose and maltose. The process started with hydrothermal and dilute acid pretreatment by hydrochloric acid (HCl) and sulphuric acid (H 2 SO 4 ) which aim to degrade larger molecules of polysaccharide before accessible for further steps of enzymatic hydrolysis by glucoamylase. A kinetic model is proposed to perform an optimal hydrolysis for obtaining high fermentable sugars. The results suggested that a significant increase in fermentable sugar production (2.04-folds) with conversion efficiency of 86.8% was observed via sequential acid-enzymatic pretreatment as compared to dilute acid pretreatment (∼42.4% conversion efficiency). The bioethanol production by Saccharomyces cerevisiae utilizing fermentable sugar obtained shows ethanol yield of 0.42g/g with conversion efficiency of 85.38% based on the theoretical yield was achieved. The finding indicates that food waste can be considered as a promising substrate for bioethanol production. Copyright © 2017. Published by Elsevier Ltd.

An Efficient System Based On Closed Sequential Patterns for Web Recommendations

OpenAIRE

Utpala Niranjan; R.B.V. Subramanyam; V-Khana

2010-01-01

Sequential pattern mining, since its introduction has received considerable attention among the researchers with broad applications. The sequential pattern algorithms generally face problems when mining long sequential patterns or while using very low support threshold. One possible solution of such problems is by mining the closed sequential patterns, which is a condensed representation of sequential patterns. Recently, several researchers have utilized the sequential pattern discovery for d...

An improved saddlepoint approximation.

Science.gov (United States)

Gillespie, Colin S; Renshaw, Eric

2007-08-01

Given a set of third- or higher-order moments, not only is the saddlepoint approximation the only realistic 'family-free' technique available for constructing an associated probability distribution, but it is 'optimal' in the sense that it is based on the highly efficient numerical method of steepest descents. However, it suffers from the problem of not always yielding full support, and whilst [S. Wang, General saddlepoint approximations in the bootstrap, Prob. Stat. Lett. 27 (1992) 61.] neat scaling approach provides a solution to this hurdle, it leads to potentially inaccurate and aberrant results. We therefore propose several new ways of surmounting such difficulties, including: extending the inversion of the cumulant generating function to second-order; selecting an appropriate probability structure for higher-order cumulants (the standard moment closure procedure takes them to be zero); and, making subtle changes to the target cumulants and then optimising via the simplex algorithm.

Optimization of Multiple Related Negotiation through Multi-Negotiation Network

Science.gov (United States)

Ren, Fenghui; Zhang, Minjie; Miao, Chunyan; Shen, Zhiqi

In this paper, a Multi-Negotiation Network (MNN) and a Multi- Negotiation Influence Diagram (MNID) are proposed to optimally handle Multiple Related Negotiations (MRN) in a multi-agent system. Most popular, state-of-the-art approaches perform MRN sequentially. However, a sequential procedure may not optimally execute MRN in terms of maximizing the global outcome, and may even lead to unnecessary losses in some situations. The motivation of this research is to use a MNN to handle MRN concurrently so as to maximize the expected utility of MRN. Firstly, both the joint success rate and the joint utility by considering all related negotiations are dynamically calculated based on a MNN. Secondly, by employing a MNID, an agent's possible decision on each related negotiation is reflected by the value of expected utility. Lastly, through comparing expected utilities between all possible policies to conduct MRN, an optimal policy is generated to optimize the global outcome of MRN. The experimental results indicate that the proposed approach can improve the global outcome of MRN in a successful end scenario, and avoid unnecessary losses in an unsuccessful end scenario.

Selectivity assessment of an arsenic sequential extraction procedure for evaluating mobility in mine wastes

International Nuclear Information System (INIS)

Drahota, Petr; Grösslová, Zuzana; Kindlová, Helena

2014-01-01

Highlights: • Extraction efficiency and selectivity of phosphate and oxalate were tested. • Pure As-bearing mineral phases and mine wastes were used. • The reagents were found to be specific and selective for most major forms of As. • An optimized sequential extraction scheme for mine wastes has been developed. • It has been tested over a model mineral mixtures and natural mine waste materials. - Abstract: An optimized sequential extraction (SE) scheme for mine waste materials has been developed and tested for As partitioning over a range of pure As-bearing mineral phases, their model mixtures, and natural mine waste materials. This optimized SE procedure employs five extraction steps: (1) nitrogen-purged deionized water, 10 h; (2) 0.01 M NH 4 H 2 PO 4 , 16 h; (3) 0.2 M NH 4 -oxalate in the dark, pH3, 2 h; (4) 0.2 M NH 4 -oxalate, pH3/80 °C, 4 h; (5) KClO 3 /HCl/HNO 3 digestion. Selectivity and specificity tests on natural mine wastes and major pure As-bearing mineral phases showed that these As fractions appear to be primarily associated with: (1) readily soluble; (2) adsorbed; (3) amorphous and poorly-crystalline arsenates, oxides and hydroxosulfates of Fe; (4) well-crystalline arsenates, oxides, and hydroxosulfates of Fe; as well as (5) sulfides and arsenides. The specificity and selectivity of extractants, and the reproducibility of the optimized SE procedure were further verified by artificial model mineral mixtures and different natural mine waste materials. Partitioning data for extraction steps 3, 4, and 5 showed good agreement with those calculated in the model mineral mixtures (<15% difference), as well as that expected in different natural mine waste materials. The sum of the As recovered in the different extractant pools was not significantly different (89–112%) than the results for acid digestion. This suggests that the optimized SE scheme can reliably be employed for As partitioning in mine waste materials

Sequential use of simulation and optimization in analysis and planning

Science.gov (United States)

Hans R. Zuuring; Jimmie D. Chew; J. Greg Jones

2000-01-01

Management activities are analyzed at landscape scales employing both simulation and optimization. SIMPPLLE, a stochastic simulation modeling system, is initially applied to assess the risks associated with a specific natural process occurring on the current landscape without management treatments, but with fire suppression. These simulation results are input into...

Higher-order convex approximations of Young measures in optimal control

Czech Academy of Sciences Publication Activity Database

Matache, A. M.; Roubíček, Tomáš; Schwab, Ch.

2003-01-01

Roč. 19, č. 1 (2003), s. 73-97 ISSN 1019-7168 R&D Projects: GA ČR GA201/00/0768; GA AV ČR IAA1075005 Institutional research plan: CEZ:AV0Z1075907 Keywords : Young measures * approximation * error estimation Subject RIV: BA - General Mathematics Impact factor: 0.926, year: 2003

Systolic array processing of the sequential decoding algorithm

Science.gov (United States)

Chang, C. Y.; Yao, K.

1989-01-01

A systolic array processing technique is applied to implementing the stack algorithm form of the sequential decoding algorithm. It is shown that sorting, a key function in the stack algorithm, can be efficiently realized by a special type of systolic arrays known as systolic priority queues. Compared to the stack-bucket algorithm, this approach is shown to have the advantages that the decoding always moves along the optimal path, that it has a fast and constant decoding speed and that its simple and regular hardware architecture is suitable for VLSI implementation. Three types of systolic priority queues are discussed: random access scheme, shift register scheme and ripple register scheme. The property of the entries stored in the systolic priority queue is also investigated. The results are applicable to many other basic sorting type problems.

Reliability analysis of large scaled structures by optimization technique

International Nuclear Information System (INIS)

Ishikawa, N.; Mihara, T.; Iizuka, M.

1987-01-01

This paper presents a reliability analysis based on the optimization technique using PNET (Probabilistic Network Evaluation Technique) method for the highly redundant structures having a large number of collapse modes. This approach makes the best use of the merit of the optimization technique in which the idea of PNET method is used. The analytical process involves the minimization of safety index of the representative mode, subjected to satisfaction of the mechanism condition and of the positive external work. The procedure entails the sequential performance of a series of the NLP (Nonlinear Programming) problems, where the correlation condition as the idea of PNET method pertaining to the representative mode is taken as an additional constraint to the next analysis. Upon succeeding iterations, the final analysis is achieved when a collapse probability at the subsequent mode is extremely less than the value at the 1st mode. The approximate collapse probability of the structure is defined as the sum of the collapse probabilities of the representative modes classified by the extent of correlation. Then, in order to confirm the validity of the proposed method, the conventional Monte Carlo simulation is also revised by using the collapse load analysis. Finally, two fairly large structures were analyzed to illustrate the scope and application of the approach. (orig./HP)

A new optimization algotithm with application to nonlinear MPC

Directory of Open Access Journals (Sweden)

Frode Martinsen

2005-01-01

Full Text Available This paper investigates application of SQP optimization algorithm to nonlinear model predictive control. It considers feasible vs. infeasible path methods, sequential vs. simultaneous methods and reduced vs full space methods. A new optimization algorithm coined rFOPT which remains feasibile with respect to inequality constraints is introduced. The suitable choices between these various strategies are assessed informally through a small CSTR case study. The case study also considers the effect various discretization methods have on the optimization problem.

Optimization of practical trusses with constraints on eigenfrequencies, displacements, stresses, and buckling

DEFF Research Database (Denmark)

Pedersen, Niels Leergaard; Nielsen, A.

2004-01-01

In this paper we consider the optimization of general 3D truss structures. The design variables are the cross-sections of the truss bars together with the joint coordinates, and are considered to be continuous variables. Using these design variables we simultaneously carry out size optimization...... are imposed in correlation with industrial standards, to make the optimized designs valuable from a practical point of view. The optimization problem is solved using SLP (Sequential Linear Programming)....

Risk-Constrained Dynamic Programming for Optimal Mars Entry, Descent, and Landing

Science.gov (United States)

Ono, Masahiro; Kuwata, Yoshiaki

2013-01-01

A chance-constrained dynamic programming algorithm was developed that is capable of making optimal sequential decisions within a user-specified risk bound. This work handles stochastic uncertainties over multiple stages in the CEMAT (Combined EDL-Mobility Analyses Tool) framework. It was demonstrated by a simulation of Mars entry, descent, and landing (EDL) using real landscape data obtained from the Mars Reconnaissance Orbiter. Although standard dynamic programming (DP) provides a general framework for optimal sequential decisionmaking under uncertainty, it typically achieves risk aversion by imposing an arbitrary penalty on failure states. Such a penalty-based approach cannot explicitly bound the probability of mission failure. A key idea behind the new approach is called risk allocation, which decomposes a joint chance constraint into a set of individual chance constraints and distributes risk over them. The joint chance constraint was reformulated into a constraint on an expectation over a sum of an indicator function, which can be incorporated into the cost function by dualizing the optimization problem. As a result, the chance-constraint optimization problem can be turned into an unconstrained optimization over a Lagrangian, which can be solved efficiently using a standard DP approach.

Discrimination between sequential and simultaneous virtual channels with electrical hearing.

Science.gov (United States)

Landsberger, David; Galvin, John J

2011-09-01

In cochlear implants (CIs), simultaneous or sequential stimulation of adjacent electrodes can produce intermediate pitch percepts between those of the component electrodes. However, it is unclear whether simultaneous and sequential virtual channels (VCs) can be discriminated. In this study, CI users were asked to discriminate simultaneous and sequential VCs; discrimination was measured for monopolar (MP) and bipolar + 1 stimulation (BP + 1), i.e., relatively broad and focused stimulation modes. For sequential VCs, the interpulse interval (IPI) varied between 0.0 and 1.8 ms. All stimuli were presented at comfortably loud, loudness-balanced levels at a 250 pulse per second per electrode (ppse) stimulation rate. On average, CI subjects were able to reliably discriminate between sequential and simultaneous VCs. While there was no significant effect of IPI or stimulation mode on VC discrimination, some subjects exhibited better VC discrimination with BP + 1 stimulation. Subjects' discrimination between sequential and simultaneous VCs was correlated with electrode discrimination, suggesting that spatial selectivity may influence perception of sequential VCs. To maintain equal loudness, sequential VC amplitudes were nearly double those of simultaneous VCs, presumably resulting in a broader spread of excitation. These results suggest that perceptual differences between simultaneous and sequential VCs might be explained by differences in the spread of excitation. © 2011 Acoustical Society of America

Rational approximation of vertical segments

Science.gov (United States)

Salazar Celis, Oliver; Cuyt, Annie; Verdonk, Brigitte

2007-08-01

In many applications, observations are prone to imprecise measurements. When constructing a model based on such data, an approximation rather than an interpolation approach is needed. Very often a least squares approximation is used. Here we follow a different approach. A natural way for dealing with uncertainty in the data is by means of an uncertainty interval. We assume that the uncertainty in the independent variables is negligible and that for each observation an uncertainty interval can be given which contains the (unknown) exact value. To approximate such data we look for functions which intersect all uncertainty intervals. In the past this problem has been studied for polynomials, or more generally for functions which are linear in the unknown coefficients. Here we study the problem for a particular class of functions which are nonlinear in the unknown coefficients, namely rational functions. We show how to reduce the problem to a quadratic programming problem with a strictly convex objective function, yielding a unique rational function which intersects all uncertainty intervals and satisfies some additional properties. Compared to rational least squares approximation which reduces to a nonlinear optimization problem where the objective function may have many local minima, this makes the new approach attractive.

Sequential versus simultaneous market delineation

DEFF Research Database (Denmark)

Haldrup, Niels; Møllgaard, Peter; Kastberg Nielsen, Claus

2005-01-01

and geographical markets. Using a unique data setfor prices of Norwegian and Scottish salmon, we propose a methodologyfor simultaneous market delineation and we demonstrate that comparedto a sequential approach conclusions will be reversed.JEL: C3, K21, L41, Q22Keywords: Relevant market, econometric delineation......Delineation of the relevant market forms a pivotal part of most antitrustcases. The standard approach is sequential. First the product marketis delineated, then the geographical market is defined. Demand andsupply substitution in both the product dimension and the geographicaldimension...

Efficient approximation of the Struve functions Hn occurring in the calculation of sound radiation quantities.

Science.gov (United States)

Aarts, Ronald M; Janssen, Augustus J E M

2016-12-01

The Struve functions H n (z), n=0, 1, ...  are approximated in a simple, accurate form that is valid for all z≥0. The authors previously treated the case n = 1 that arises in impedance calculations for the rigid-piston circular radiator mounted in an infinite planar baffle [Aarts and Janssen, J. Acoust. Soc. Am. 113, 2635-2637 (2003)]. The more general Struve functions occur when other acoustical quantities and/or non-rigid pistons are considered. The key step in the paper just cited is to express H 1 (z) as (2/π)-J 0 (z)+(2/π) I(z), where J 0 is the Bessel function of order zero and the first kind and I(z) is the Fourier cosine transform of [(1-t)/(1+t)] 1/2 , 0≤t≤1. The square-root function is optimally approximated by a linear function ĉt+d̂, 0≤t≤1, and the resulting approximated Fourier integral is readily computed explicitly in terms of sin z/z and (1-cos z)/z 2 . The same approach has been used by Maurel, Pagneux, Barra, and Lund [Phys. Rev. B 75, 224112 (2007)] to approximate H 0 (z) for all z≥0. In the present paper, the square-root function is optimally approximated by a piecewise linear function consisting of two linear functions supported by [0,t̂ 0 ] and [t̂ 0 ,1] with t̂ 0 the optimal take-over point. It is shown that the optimal two-piece linear function is actually continuous at the take-over point, causing a reduction of the additional complexity in the resulting approximations of H 0 and H 1 . Furthermore, this allows analytic computation of the optimal two-piece linear function. By using the two-piece instead of the one-piece linear approximation, the root mean square approximation error is reduced by roughly a factor of 3 while the maximum approximation error is reduced by a factor of 4.5 for H 0 and of 2.6 for H 1 . Recursion relations satisfied by Struve functions, initialized with the approximations of H 0 and H 1 , yield approximations for higher order Struve functions.

Entropy-Based Experimental Design for Optimal Model Discrimination in the Geosciences

Directory of Open Access Journals (Sweden)

Wolfgang Nowak

2016-11-01

Full Text Available Choosing between competing models lies at the heart of scientific work, and is a frequent motivation for experimentation. Optimal experimental design (OD methods maximize the benefit of experiments towards a specified goal. We advance and demonstrate an OD approach to maximize the information gained towards model selection. We make use of so-called model choice indicators, which are random variables with an expected value equal to Bayesian model weights. Their uncertainty can be measured with Shannon entropy. Since the experimental data are still random variables in the planning phase of an experiment, we use mutual information (the expected reduction in Shannon entropy to quantify the information gained from a proposed experimental design. For implementation, we use the Preposterior Data Impact Assessor framework (PreDIA, because it is free of the lower-order approximations of mutual information often found in the geosciences. In comparison to other studies in statistics, our framework is not restricted to sequential design or to discrete-valued data, and it can handle measurement errors. As an application example, we optimize an experiment about the transport of contaminants in clay, featuring the problem of choosing between competing isotherms to describe sorption. We compare the results of optimizing towards maximum model discrimination with an alternative OD approach that minimizes the overall predictive uncertainty under model choice uncertainty.

Production of alkyl esters from macaw palm oil by a sequential hydrolysis/esterification process using heterogeneous biocatalysts: optimization by response surface methodology.

Science.gov (United States)

Bressani, Ana Paula P; Garcia, Karen C A; Hirata, Daniela B; Mendes, Adriano A

2015-02-01

The present study deals with the enzymatic synthesis of alkyl esters with emollient properties by a sequential hydrolysis/esterification process (hydroesterification) using unrefined macaw palm oil from pulp seeds (MPPO) as feedstock. Crude enzymatic extract from dormant castor bean seeds was used as biocatalyst in the production of free fatty acids (FFA) by hydrolysis of MPPO. Esterification of purified FFA with several alcohols in heptane medium was catalyzed by immobilized Thermomyces lanuginosus lipase (TLL) on poly-hydroxybutyrate (PHB) particles. Under optimal experimental conditions (mass ratio oil:buffer of 35% m/m, reaction temperature of 35 °C, biocatalyst concentration of 6% m/m, and stirring speed of 1,000 rpm), complete hydrolysis of MPPO was reached after 110 min of reaction. Maximum ester conversion percentage of 92.4 ± 0.4% was reached using hexanol as acyl acceptor at 750 mM of each reactant after 15 min of reaction. The biocatalyst retained full activity after eight successive cycles of esterification reaction. These results show that the proposed process is a promising strategy for the synthesis of alkyl esters of industrial interest from macaw palm oil, an attractive option for the Brazilian oleochemical industry.

Boundary Control of Linear Uncertain 1-D Parabolic PDE Using Approximate Dynamic Programming.

Science.gov (United States)

Talaei, Behzad; Jagannathan, Sarangapani; Singler, John

2018-04-01

This paper develops a near optimal boundary control method for distributed parameter systems governed by uncertain linear 1-D parabolic partial differential equations (PDE) by using approximate dynamic programming. A quadratic surface integral is proposed to express the optimal cost functional for the infinite-dimensional state space. Accordingly, the Hamilton-Jacobi-Bellman (HJB) equation is formulated in the infinite-dimensional domain without using any model reduction. Subsequently, a neural network identifier is developed to estimate the unknown spatially varying coefficient in PDE dynamics. Novel tuning law is proposed to guarantee the boundedness of identifier approximation error in the PDE domain. A radial basis network (RBN) is subsequently proposed to generate an approximate solution for the optimal surface kernel function online. The tuning law for near optimal RBN weights is created, such that the HJB equation error is minimized while the dynamics are identified and closed-loop system remains stable. Ultimate boundedness (UB) of the closed-loop system is verified by using the Lyapunov theory. The performance of the proposed controller is successfully confirmed by simulation on an unstable diffusion-reaction process.

Congruence Approximations for Entrophy Endowed Hyperbolic Systems

Science.gov (United States)

Barth, Timothy J.; Saini, Subhash (Technical Monitor)

1998-01-01

Building upon the standard symmetrization theory for hyperbolic systems of conservation laws, congruence properties of the symmetrized system are explored. These congruence properties suggest variants of several stabilized numerical discretization procedures for hyperbolic equations (upwind finite-volume, Galerkin least-squares, discontinuous Galerkin) that benefit computationally from congruence approximation. Specifically, it becomes straightforward to construct the spatial discretization and Jacobian linearization for these schemes (given a small amount of derivative information) for possible use in Newton's method, discrete optimization, homotopy algorithms, etc. Some examples will be given for the compressible Euler equations and the nonrelativistic MHD equations using linear and quadratic spatial approximation.

Macroscopic Dynamic Modeling of Sequential Batch Cultures of Hybridoma Cells: An Experimental Validation

Directory of Open Access Journals (Sweden)

Laurent Dewasme

2017-02-01

Full Text Available Hybridoma cells are commonly grown for the production of monoclonal antibodies (MAb. For monitoring and control purposes of the bioreactors, dynamic models of the cultures are required. However these models are difficult to infer from the usually limited amount of available experimental data and do not focus on target protein production optimization. This paper explores an experimental case study where hybridoma cells are grown in a sequential batch reactor. The simplest macroscopic reaction scheme translating the data is first derived using a maximum likelihood principal component analysis. Subsequently, nonlinear least-squares estimation is used to determine the kinetic laws. The resulting dynamic model reproduces quite satisfactorily the experimental data, as evidenced in direct and cross-validation tests. Furthermore, model predictions can also be used to predict optimal medium renewal time and composition.

Fast wavelet based sparse approximate inverse preconditioner

Energy Technology Data Exchange (ETDEWEB)

Wan, W.L. [Univ. of California, Los Angeles, CA (United States)

1996-12-31

Incomplete LU factorization is a robust preconditioner for both general and PDE problems but unfortunately not easy to parallelize. Recent study of Huckle and Grote and Chow and Saad showed that sparse approximate inverse could be a potential alternative while readily parallelizable. However, for special class of matrix A that comes from elliptic PDE problems, their preconditioners are not optimal in the sense that independent of mesh size. A reason may be that no good sparse approximate inverse exists for the dense inverse matrix. Our observation is that for this kind of matrices, its inverse entries typically have piecewise smooth changes. We can take advantage of this fact and use wavelet compression techniques to construct a better sparse approximate inverse preconditioner. We shall show numerically that our approach is effective for this kind of matrices.

Group-sequential analysis may allow for early trial termination

DEFF Research Database (Denmark)

Gerke, Oke; Vilstrup, Mie H; Halekoh, Ulrich

2017-01-01

BACKGROUND: Group-sequential testing is widely used in pivotal therapeutic, but rarely in diagnostic research, although it may save studies, time, and costs. The purpose of this paper was to demonstrate a group-sequential analysis strategy in an intra-observer study on quantitative FDG-PET/CT mea......BACKGROUND: Group-sequential testing is widely used in pivotal therapeutic, but rarely in diagnostic research, although it may save studies, time, and costs. The purpose of this paper was to demonstrate a group-sequential analysis strategy in an intra-observer study on quantitative FDG...

Sequential logic analysis and synthesis

CERN Document Server

Cavanagh, Joseph

2007-01-01

Until now, there was no single resource for actual digital system design. Using both basic and advanced concepts, Sequential Logic: Analysis and Synthesis offers a thorough exposition of the analysis and synthesis of both synchronous and asynchronous sequential machines. With 25 years of experience in designing computing equipment, the author stresses the practical design of state machines. He clearly delineates each step of the structured and rigorous design principles that can be applied to practical applications. The book begins by reviewing the analysis of combinatorial logic and Boolean a

Bounds on Rates of Variable-Basis and Neural-Network Approximation

Czech Academy of Sciences Publication Activity Database

Kůrková, Věra; Sanguineti, M.

2001-01-01

Roč. 47, č. 6 (2001), s. 2659-2665 ISSN 0018-9448 R&D Projects: GA ČR GA201/00/1482 Institutional research plan: AV0Z1030915 Keywords : approximation by variable-basis functions * bounds on rates of approximation * complexity of neural networks * high-dimensional optimal decision problems Subject RIV: BA - General Mathematics Impact factor: 2.077, year: 2001

Applicability of point-dipoles approximation to all-dielectric metamaterials

DEFF Research Database (Denmark)

Kuznetsova, S. M.; Andryieuski, Andrei; Lavrinenko, Andrei

2015-01-01

All-dielectric metamaterials consisting of high-dielectric inclusions in a low-dielectric matrix are considered as a low-loss alternative to resonant metal-based metamaterials. In this paper we investigate the applicability of the point electric and magnetic dipoles approximation to dielectric meta......-atoms on the example of a dielectric ring metamaterial. Despite the large electrical size of high-dielectric meta-atoms, the dipole approximation allows for accurate prediction of the metamaterials properties for the rings with diameters up to approximate to 0.8 of the lattice constant. The results provide important...... guidelines for design and optimization of all-dielectric metamaterials....

DQM: Decentralized Quadratically Approximated Alternating Direction Method of Multipliers

Science.gov (United States)

Mokhtari, Aryan; Shi, Wei; Ling, Qing; Ribeiro, Alejandro

2016-10-01

This paper considers decentralized consensus optimization problems where nodes of a network have access to different summands of a global objective function. Nodes cooperate to minimize the global objective by exchanging information with neighbors only. A decentralized version of the alternating directions method of multipliers (DADMM) is a common method for solving this category of problems. DADMM exhibits linear convergence rate to the optimal objective but its implementation requires solving a convex optimization problem at each iteration. This can be computationally costly and may result in large overall convergence times. The decentralized quadratically approximated ADMM algorithm (DQM), which minimizes a quadratic approximation of the objective function that DADMM minimizes at each iteration, is proposed here. The consequent reduction in computational time is shown to have minimal effect on convergence properties. Convergence still proceeds at a linear rate with a guaranteed constant that is asymptotically equivalent to the DADMM linear convergence rate constant. Numerical results demonstrate advantages of DQM relative to DADMM and other alternatives in a logistic regression problem.

Structural Consistency, Consistency, and Sequential Rationality.

OpenAIRE

Kreps, David M; Ramey, Garey

1987-01-01

Sequential equilibria comprise consistent beliefs and a sequentially ra tional strategy profile. Consistent beliefs are limits of Bayes ratio nal beliefs for sequences of strategies that approach the equilibrium strategy. Beliefs are structurally consistent if they are rationaliz ed by some single conjecture concerning opponents' strategies. Consis tent beliefs are not necessarily structurally consistent, notwithstan ding a claim by Kreps and Robert Wilson (1982). Moreover, the spirit of stru...

Multi-arm group sequential designs with a simultaneous stopping rule.

Science.gov (United States)

Urach, S; Posch, M

2016-12-30

Multi-arm group sequential clinical trials are efficient designs to compare multiple treatments to a control. They allow one to test for treatment effects already in interim analyses and can have a lower average sample number than fixed sample designs. Their operating characteristics depend on the stopping rule: We consider simultaneous stopping, where the whole trial is stopped as soon as for any of the arms the null hypothesis of no treatment effect can be rejected, and separate stopping, where only recruitment to arms for which a significant treatment effect could be demonstrated is stopped, but the other arms are continued. For both stopping rules, the family-wise error rate can be controlled by the closed testing procedure applied to group sequential tests of intersection and elementary hypotheses. The group sequential boundaries for the separate stopping rule also control the family-wise error rate if the simultaneous stopping rule is applied. However, we show that for the simultaneous stopping rule, one can apply improved, less conservative stopping boundaries for local tests of elementary hypotheses. We derive corresponding improved Pocock and O'Brien type boundaries as well as optimized boundaries to maximize the power or average sample number and investigate the operating characteristics and small sample properties of the resulting designs. To control the power to reject at least one null hypothesis, the simultaneous stopping rule requires a lower average sample number than the separate stopping rule. This comes at the cost of a lower power to reject all null hypotheses. Some of this loss in power can be regained by applying the improved stopping boundaries for the simultaneous stopping rule. The procedures are illustrated with clinical trials in systemic sclerosis and narcolepsy. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Applying Sequential Particle Swarm Optimization Algorithm to Improve Power Generation Quality

Directory of Open Access Journals (Sweden)

Abdulhafid Sallama

2014-10-01

Full Text Available Swarm Optimization approach is a heuristic search method whose mechanics are inspired by the swarming or collaborative behaviour of biological populations. It is used to solve constrained, unconstrained, continuous and discrete problems. Swarm intelligence systems are widely used and very effective in solving standard and large-scale optimization, provided that the problem does not require multi solutions. In this paper, particle swarm optimisation technique is used to optimise fuzzy logic controller (FLC for stabilising a power generation and distribution network that consists of four generators. The system is subject to different types of faults (single and multi-phase. Simulation studies show that the optimised FLC performs well in stabilising the network after it recovers from a fault. The controller is compared to multi-band and standard controllers.

Joint optimization of collimator and reconstruction parameters in SPECT imaging for lesion quantification

International Nuclear Information System (INIS)

McQuaid, Sarah J; Southekal, Sudeepti; Kijewski, Marie Foley; Moore, Stephen C

2011-01-01

Obtaining the best possible task performance using reconstructed SPECT images requires optimization of both the collimator and reconstruction parameters. The goal of this study is to determine how to perform this optimization, namely whether the collimator parameters can be optimized solely from projection data, or whether reconstruction parameters should also be considered. In order to answer this question, and to determine the optimal collimation, a digital phantom representing a human torso with 16 mm diameter hot lesions (activity ratio 8:1) was generated and used to simulate clinical SPECT studies with parallel-hole collimation. Two approaches to optimizing the SPECT system were then compared in a lesion quantification task: sequential optimization, where collimation was optimized on projection data using the Cramer–Rao bound, and joint optimization, which simultaneously optimized collimator and reconstruction parameters. For every condition, quantification performance in reconstructed images was evaluated using the root-mean-squared-error of 400 estimates of lesion activity. Compared to the joint-optimization approach, the sequential-optimization approach favoured a poorer resolution collimator, which, under some conditions, resulted in sub-optimal estimation performance. This implies that inclusion of the reconstruction parameters in the optimization procedure is important in obtaining the best possible task performance; in this study, this was achieved with a collimator resolution similar to that of a general-purpose (LEGP) collimator. This collimator was found to outperform the more commonly used high-resolution (LEHR) collimator, in agreement with other task-based studies, using both quantification and detection tasks.

Methods of Approximation Theory in Complex Analysis and Mathematical Physics

CERN Document Server

Saff, Edward

1993-01-01

The book incorporates research papers and surveys written by participants ofan International Scientific Programme on Approximation Theory jointly supervised by Institute for Constructive Mathematics of University of South Florida at Tampa, USA and the Euler International Mathematical Instituteat St. Petersburg, Russia. The aim of the Programme was to present new developments in Constructive Approximation Theory. The topics of the papers are: asymptotic behaviour of orthogonal polynomials, rational approximation of classical functions, quadrature formulas, theory of n-widths, nonlinear approximation in Hardy algebras,numerical results on best polynomial approximations, wavelet analysis. FROM THE CONTENTS: E.A. Rakhmanov: Strong asymptotics for orthogonal polynomials associated with exponential weights on R.- A.L. Levin, E.B. Saff: Exact Convergence Rates for Best Lp Rational Approximation to the Signum Function and for Optimal Quadrature in Hp.- H. Stahl: Uniform Rational Approximation of x .- M. Rahman, S.K. ...

Hierarchical matrix approximation of large covariance matrices

KAUST Repository

Litvinenko, Alexander; Genton, Marc G.; Sun, Ying

2015-01-01

We approximate large non-structured Matérn covariance matrices of size n×n in the H-matrix format with a log-linear computational cost and storage O(kn log n), where rank k ≪ n is a small integer. Applications are: spatial statistics, machine learning and image analysis, kriging and optimal design.

Hierarchical matrix approximation of large covariance matrices

KAUST Repository

Litvinenko, Alexander

2015-11-30

We approximate large non-structured Matérn covariance matrices of size n×n in the H-matrix format with a log-linear computational cost and storage O(kn log n), where rank k ≪ n is a small integer. Applications are: spatial statistics, machine learning and image analysis, kriging and optimal design.

MVMO-based approach for optimal placement and tuning of ...

African Journals Online (AJOL)

DR OKE

differential evolution DE algorithm with adaptive crossover operator, .... x are assigned by using a sequential scheme which accounts for mean and ... the representative scenarios from probabilistic model based Monte Carlo ... Comparison of average convergence of MVMO-S with other metaheuristic optimization methods.

A rotor optimization using regression analysis

Science.gov (United States)

Giansante, N.

1984-01-01

The design and development of helicopter rotors is subject to the many design variables and their interactions that effect rotor operation. Until recently, selection of rotor design variables to achieve specified rotor operational qualities has been a costly, time consuming, repetitive task. For the past several years, Kaman Aerospace Corporation has successfully applied multiple linear regression analysis, coupled with optimization and sensitivity procedures, in the analytical design of rotor systems. It is concluded that approximating equations can be developed rapidly for a multiplicity of objective and constraint functions and optimizations can be performed in a rapid and cost effective manner; the number and/or range of design variables can be increased by expanding the data base and developing approximating functions to reflect the expanded design space; the order of the approximating equations can be expanded easily to improve correlation between analyzer results and the approximating equations; gradients of the approximating equations can be calculated easily and these gradients are smooth functions reducing the risk of numerical problems in the optimization; the use of approximating functions allows the problem to be started easily and rapidly from various initial designs to enhance the probability of finding a global optimum; and the approximating equations are independent of the analysis or optimization codes used.

Diophantine approximation and badly approximable sets

DEFF Research Database (Denmark)

Kristensen, S.; Thorn, R.; Velani, S.

2006-01-01

. The classical set Bad of `badly approximable' numbers in the theory of Diophantine approximation falls within our framework as do the sets Bad(i,j) of simultaneously badly approximable numbers. Under various natural conditions we prove that the badly approximable subsets of Omega have full Hausdorff dimension...

Detecting changes in real-time data: a user's guide to optimal detection.

Science.gov (United States)

Johnson, P; Moriarty, J; Peskir, G

2017-08-13

The real-time detection of changes in a noisily observed signal is an important problem in applied science and engineering. The study of parametric optimal detection theory began in the 1930s, motivated by applications in production and defence. Today this theory, which aims to minimize a given measure of detection delay under accuracy constraints, finds applications in domains including radar, sonar, seismic activity, global positioning, psychological testing, quality control, communications and power systems engineering. This paper reviews developments in optimal detection theory and sequential analysis, including sequential hypothesis testing and change-point detection, in both Bayesian and classical (non-Bayesian) settings. For clarity of exposition, we work in discrete time and provide a brief discussion of the continuous time setting, including recent developments using stochastic calculus. Different measures of detection delay are presented, together with the corresponding optimal solutions. We emphasize the important role of the signal-to-noise ratio and discuss both the underlying assumptions and some typical applications for each formulation.This article is part of the themed issue 'Energy management: flexibility, risk and optimization'. © 2017 The Author(s).

Optimal allocation of resources in systems

International Nuclear Information System (INIS)

Derman, C.; Lieberman, G.J.; Ross, S.M.

1975-01-01

In the design of a new system, or the maintenance of an old system, allocation of resources is of prime consideration. In allocating resources it is often beneficial to develop a solution that yields an optimal value of the system measure of desirability. In the context of the problems considered in this paper the resources to be allocated are components already produced (assembly problems) and money (allocation in the construction or repair of systems). The measure of desirability for system assembly will usually be maximizing the expected number of systems that perform satisfactorily and the measure in the allocation context will be maximizing the system reliability. Results are presented for these two types of general problems in both a sequential (when appropriate) and non-sequential context

Discrete-time optimal control and games on large intervals

CERN Document Server

Zaslavski, Alexander J

2017-01-01

Devoted to the structure of approximate solutions of discrete-time optimal control problems and approximate solutions of dynamic discrete-time two-player zero-sum games, this book presents results on properties of approximate solutions in an interval that is independent lengthwise, for all sufficiently large intervals. Results concerning the so-called turnpike property of optimal control problems and zero-sum games in the regions close to the endpoints of the time intervals are the main focus of this book. The description of the structure of approximate solutions on sufficiently large intervals and its stability will interest graduate students and mathematicians in optimal control and game theory, engineering, and economics. This book begins with a brief overview and moves on to analyze the structure of approximate solutions of autonomous nonconcave discrete-time optimal control Lagrange problems.Next the structures of approximate solutions of autonomous discrete-time optimal control problems that are discret...

Sequentially administrated of pemetrexed with icotinib/erlotinib in lung adenocarcinoma cell lines in vitro.

Science.gov (United States)

Feng, Xiuli; Zhang, Yan; Li, Tao; Li, Yu

2017-12-26

Combination of chemotherapy and epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) had been proved to be a potent anti-drug for the treatment of tumors. However, survival time was not extended for the patients with lung adenocarcinoma (AdC) compared with first-line chemotherapy. In the present study, we attempt to assess the optimal schedule of the combined administration of pemetrexed and icotinib/erlotinib in AdC cell lines. Human lung AdC cell lines with wild-type (A549), EGFR T790M (H1975) and activating EGFR mutation (HCC827) were applied in vitro to assess the differential efficacy of various sequential regimens on cell viability, cell apoptosis and cell cycle distribution. The results suggested that the antiproliferative effect of the sequence of pemetrexed followed by icotinib/erlotinib was more effective than that of icotinib/erlotinib followed by pemetrexed. Additionally, a reduction of G1 phase and increased S phase in sequence of pemetrexed followed by icotinib/erlotinib was also observed, promoting cell apoptosis. Thus, the sequential administration of pemetrexed followed by icotinib/erlotinib exerted a synergistic effect on HCC827 and H1975 cell lines compared with the reverse sequence. The sequential treatment of pemetrexed followed by icotinib/erlotinib has been demonstrated promising results. This treatment strategy warrants further confirmation in patients with advanced lung AdC.

Sequential growth factor application in bone marrow stromal cell ligament engineering.

Science.gov (United States)

Moreau, Jodie E; Chen, Jingsong; Horan, Rebecca L; Kaplan, David L; Altman, Gregory H

2005-01-01

In vitro bone marrow stromal cell (BMSC) growth may be enhanced through culture medium supplementation, mimicking the biochemical environment in which cells optimally proliferate and differentiate. We hypothesize that the sequential administration of growth factors to first proliferate and then differentiate BMSCs cultured on silk fiber matrices will support the enhanced development of ligament tissue in vitro. Confluent second passage (P2) BMSCs obtained from purified bone marrow aspirates were seeded on RGD-modified silk matrices. Seeded matrices were divided into three groups for 5 days of static culture, with medium supplement of basic fibroblast growth factor (B) (1 ng/mL), epidermal growth factor (E; 1 ng/mL), or growth factor-free control (C). After day 5, medium supplementation was changed to transforming growth factor-beta1 (T; 5 ng/mL) or C for an additional 9 days of culture. Real-time RT-PCR, SEM, MTT, histology, and ELISA for collagen type I of all sample groups were performed. Results indicated that BT supported the greatest cell ingrowth after 14 days of culture in addition to the greatest cumulative collagen type I expression measured by ELISA. Sequential growth factor application promoted significant increases in collagen type I transcript expression from day 5 of culture to day 14, for five of six groups tested. All T-supplemented samples surpassed their respective control samples in both cell ingrowth and collagen deposition. All samples supported spindle-shaped, fibroblast cell morphology, aligning with the direction of silk fibers. These findings indicate significant in vitro ligament development after only 14 days of culture when using a sequential growth factor approach.

Generalized infimum and sequential product of quantum effects

International Nuclear Information System (INIS)

Li Yuan; Sun Xiuhong; Chen Zhengli

2007-01-01

The quantum effects for a physical system can be described by the set E(H) of positive operators on a complex Hilbert space H that are bounded above by the identity operator I. For A, B(set-membership sign)E(H), the operation of sequential product A(convolution sign)B=A 1/2 BA 1/2 was proposed as a model for sequential quantum measurements. A nice investigation of properties of the sequential product has been carried over [Gudder, S. and Nagy, G., 'Sequential quantum measurements', J. Math. Phys. 42, 5212 (2001)]. In this note, we extend some results of this reference. In particular, a gap in the proof of Theorem 3.2 in this reference is overcome. In addition, some properties of generalized infimum A sqcap B are studied

Dynamic Programming Approach for Exact Decision Rule Optimization

KAUST Repository

Amin, Talha

2013-01-01

This chapter is devoted to the study of an extension of dynamic programming approach that allows sequential optimization of exact decision rules relative to the length and coverage. It contains also results of experiments with decision tables from UCI Machine Learning Repository. © Springer-Verlag Berlin Heidelberg 2013.

Optimization of fermentation medium for nisin production from ...

African Journals Online (AJOL)

Sequentially, Box-Behnken design experiments were implemented for further optimization. RSM combined with ANNGA were used for analysis of data. Specially, a RSM model was used for determining the individual effect and mutual interaction effect of tested variables on nisin titer (NT), an ANN model was used for NT ...

Accumulation of evidence during sequential decision making: the importance of top-down factors.

Science.gov (United States)

de Lange, Floris P; Jensen, Ole; Dehaene, Stanislas

2010-01-13

In the last decade, great progress has been made in characterizing the accumulation of neural information during simple unitary perceptual decisions. However, much less is known about how sequentially presented evidence is integrated over time for successful decision making. The aim of this study was to study the mechanisms of sequential decision making in humans. In a magnetoencephalography (MEG) study, we presented healthy volunteers with sequences of centrally presented arrows. Sequence length varied between one and five arrows, and the accumulated directions of the arrows informed the subject about which hand to use for a button press at the end of the sequence (e.g., LRLRR should result in a right-hand press). Mathematical modeling suggested that nonlinear accumulation was the rational strategy for performing this task in the presence of no or little noise, whereas quasilinear accumulation was optimal in the presence of substantial noise. MEG recordings showed a correlate of evidence integration over parietal and central cortex that was inversely related to the amount of accumulated evidence (i.e., when more evidence was accumulated, neural activity for new stimuli was attenuated). This modulation of activity likely reflects a top-down influence on sensory processing, effectively constraining the influence of sensory information on the decision variable over time. The results indicate that, when making decisions on the basis of sequential information, the human nervous system integrates evidence in a nonlinear manner, using the amount of previously accumulated information to constrain the accumulation of additional evidence.

Optimal Linear Responses for Markov Chains and Stochastically Perturbed Dynamical Systems

Science.gov (United States)

Antown, Fadi; Dragičević, Davor; Froyland, Gary

2018-03-01

The linear response of a dynamical system refers to changes to properties of the system when small external perturbations are applied. We consider the little-studied question of selecting an optimal perturbation so as to (i) maximise the linear response of the equilibrium distribution of the system, (ii) maximise the linear response of the expectation of a specified observable, and (iii) maximise the linear response of the rate of convergence of the system to the equilibrium distribution. We also consider the inhomogeneous, sequential, or time-dependent situation where the governing dynamics is not stationary and one wishes to select a sequence of small perturbations so as to maximise the overall linear response at some terminal time. We develop the theory for finite-state Markov chains, provide explicit solutions for some illustrative examples, and numerically apply our theory to stochastically perturbed dynamical systems, where the Markov chain is replaced by a matrix representation of an approximate annealed transfer operator for the random dynamical system.

Approximate Quantum Adders with Genetic Algorithms: An IBM Quantum Experience

Directory of Open Access Journals (Sweden)

Li Rui

2017-07-01

Full Text Available It has been proven that quantum adders are forbidden by the laws of quantum mechanics. We analyze theoretical proposals for the implementation of approximate quantum adders and optimize them by means of genetic algorithms, improving previous protocols in terms of efficiency and fidelity. Furthermore, we experimentally realize a suitable approximate quantum adder with the cloud quantum computing facilities provided by IBM Quantum Experience. The development of approximate quantum adders enhances the toolbox of quantum information protocols, paving the way for novel applications in quantum technologies.

Non-Pilot-Aided Sequential Monte Carlo Method to Joint Signal, Phase Noise, and Frequency Offset Estimation in Multicarrier Systems

Directory of Open Access Journals (Sweden)

Christelle Garnier

2008-05-01

Full Text Available We address the problem of phase noise (PHN and carrier frequency offset (CFO mitigation in multicarrier receivers. In multicarrier systems, phase distortions cause two effects: the common phase error (CPE and the intercarrier interference (ICI which severely degrade the accuracy of the symbol detection stage. Here, we propose a non-pilot-aided scheme to jointly estimate PHN, CFO, and multicarrier signal in time domain. Unlike existing methods, non-pilot-based estimation is performed without any decision-directed scheme. Our approach to the problem is based on Bayesian estimation using sequential Monte Carlo filtering commonly referred to as particle filtering. The particle filter is efficiently implemented by combining the principles of the Rao-Blackwellization technique and an approximate optimal importance function for phase distortion sampling. Moreover, in order to fully benefit from time-domain processing, we propose a multicarrier signal model which includes the redundancy information induced by the cyclic prefix, thus leading to a significant performance improvement. Simulation results are provided in terms of bit error rate (BER and mean square error (MSE to illustrate the efficiency and the robustness of the proposed algorithm.

Sequential injection analysis for automation of the Winkler methodology, with real-time SIMPLEX optimization and shipboard application

Energy Technology Data Exchange (ETDEWEB)

Horstkotte, Burkhard; Tovar Sanchez, Antonio; Duarte, Carlos M. [Department of Global Change Research, IMEDEA (CSIC-UIB) Institut Mediterrani d' Estudis Avancats, Miquel Marques 21, 07190 Esporles (Spain); Cerda, Victor, E-mail: Victor.Cerda@uib.es [University of the Balearic Islands, Department of Chemistry Carreterra de Valldemossa km 7.5, 07011 Palma de Mallorca (Spain)

2010-01-25

A multipurpose analyzer system based on sequential injection analysis (SIA) for the determination of dissolved oxygen (DO) in seawater is presented. Three operation modes were established and successfully applied onboard during a research cruise in the Southern ocean: 1st, in-line execution of the entire Winkler method including precipitation of manganese (II) hydroxide, fixation of DO, precipitate dissolution by confluent acidification, and spectrophotometric quantification of the generated iodine/tri-iodide (I{sub 2}/I{sub 3}{sup -}), 2nd, spectrophotometric quantification of I{sub 2}/I{sub 3}{sup -} in samples prepared according the classical Winkler protocol, and 3rd, accurate batch-wise titration of I{sub 2}/I{sub 3}{sup -} with thiosulfate using one syringe pump of the analyzer as automatic burette. In the first mode, the zone stacking principle was applied to achieve high dispersion of the reagent solutions in the sample zone. Spectrophotometric detection was done at the isobestic wavelength 466 nm of I{sub 2}/I{sub 3}{sup -}. Highly reduced consumption of reagents and sample compared to the classical Winkler protocol, linear response up to 16 mg L{sup -1} DO, and an injection frequency of 30 per hour were achieved. It is noteworthy that for the offline protocol, sample metering and quantification with a potentiometric titrator lasts in general over 5 min without counting sample fixation, incubation, and glassware cleaning. The modified SIMPLEX methodology was used for the simultaneous optimization of four volumetric and two chemical variables. Vertex calculation and consequent application including in-line preparation of one reagent was carried out in real-time using the software AutoAnalysis. The analytical system featured high signal stability, robustness, and a repeatability of 3% RSD (1st mode) and 0.8% (2nd mode) during shipboard application.

Sequential injection analysis for automation of the Winkler methodology, with real-time SIMPLEX optimization and shipboard application

International Nuclear Information System (INIS)

Horstkotte, Burkhard; Tovar Sanchez, Antonio; Duarte, Carlos M.; Cerda, Victor

2010-01-01

A multipurpose analyzer system based on sequential injection analysis (SIA) for the determination of dissolved oxygen (DO) in seawater is presented. Three operation modes were established and successfully applied onboard during a research cruise in the Southern ocean: 1st, in-line execution of the entire Winkler method including precipitation of manganese (II) hydroxide, fixation of DO, precipitate dissolution by confluent acidification, and spectrophotometric quantification of the generated iodine/tri-iodide (I 2 /I 3 - ), 2nd, spectrophotometric quantification of I 2 /I 3 - in samples prepared according the classical Winkler protocol, and 3rd, accurate batch-wise titration of I 2 /I 3 - with thiosulfate using one syringe pump of the analyzer as automatic burette. In the first mode, the zone stacking principle was applied to achieve high dispersion of the reagent solutions in the sample zone. Spectrophotometric detection was done at the isobestic wavelength 466 nm of I 2 /I 3 - . Highly reduced consumption of reagents and sample compared to the classical Winkler protocol, linear response up to 16 mg L -1 DO, and an injection frequency of 30 per hour were achieved. It is noteworthy that for the offline protocol, sample metering and quantification with a potentiometric titrator lasts in general over 5 min without counting sample fixation, incubation, and glassware cleaning. The modified SIMPLEX methodology was used for the simultaneous optimization of four volumetric and two chemical variables. Vertex calculation and consequent application including in-line preparation of one reagent was carried out in real-time using the software AutoAnalysis. The analytical system featured high signal stability, robustness, and a repeatability of 3% RSD (1st mode) and 0.8% (2nd mode) during shipboard application.

Distributed Algorithms for Time Optimal Reachability Analysis

DEFF Research Database (Denmark)

Zhang, Zhengkui; Nielsen, Brian; Larsen, Kim Guldstrand

2016-01-01

. We propose distributed computing to accelerate time optimal reachability analysis. We develop five distributed state exploration algorithms, implement them in \\uppaal enabling it to exploit the compute resources of a dedicated model-checking cluster. We experimentally evaluate the implemented...... algorithms with four models in terms of their ability to compute near- or proven-optimal solutions, their scalability, time and memory consumption and communication overhead. Our results show that distributed algorithms work much faster than sequential algorithms and have good speedup in general.......Time optimal reachability analysis is a novel model based technique for solving scheduling and planning problems. After modeling them as reachability problems using timed automata, a real-time model checker can compute the fastest trace to the goal states which constitutes a time optimal schedule...

An intrinsic robust rank-one-approximation approach for currencyportfolio optimization

OpenAIRE

Hongxuan Huang; Zhengjun Zhang

2018-01-01

A currency portfolio is a special kind of wealth whose value fluctuates with foreignexchange rates over time, which possesses 3Vs (volume, variety and velocity) properties of big datain the currency market. In this paper, an intrinsic robust rank one approximation (ROA) approachis proposed to maximize the value of currency portfolios over time. The main results of the paperinclude four parts: Firstly, under the assumptions about the currency market, the currency portfoliooptimization problem ...

Sequential analysis in neonatal research-systematic review.

Science.gov (United States)

Lava, Sebastiano A G; Elie, Valéry; Ha, Phuong Thi Viet; Jacqz-Aigrain, Evelyne

2018-05-01

As more new drugs are discovered, traditional designs come at their limits. Ten years after the adoption of the European Paediatric Regulation, we performed a systematic review on the US National Library of Medicine and Excerpta Medica database of sequential trials involving newborns. Out of 326 identified scientific reports, 21 trials were included. They enrolled 2832 patients, of whom 2099 were analyzed: the median number of neonates included per trial was 48 (IQR 22-87), median gestational age was 28.7 (IQR 27.9-30.9) weeks. Eighteen trials used sequential techniques to determine sample size, while 3 used continual reassessment methods for dose-finding. In 16 studies reporting sufficient data, the sequential design allowed to non-significantly reduce the number of enrolled neonates by a median of 24 (31%) patients (IQR - 4.75 to 136.5, p = 0.0674) with respect to a traditional trial. When the number of neonates finally included in the analysis was considered, the difference became significant: 35 (57%) patients (IQR 10 to 136.5, p = 0.0033). Sequential trial designs have not been frequently used in Neonatology. They might potentially be able to reduce the number of patients in drug trials, although this is not always the case. What is known: • In evaluating rare diseases in fragile populations, traditional designs come at their limits. About 20% of pediatric trials are discontinued, mainly because of recruitment problems. What is new: • Sequential trials involving newborns were infrequently used and only a few (n = 21) are available for analysis. • The sequential design allowed to non-significantly reduce the number of enrolled neonates by a median of 24 (31%) patients (IQR - 4.75 to 136.5, p = 0.0674).

Structural Design Optimization On Thermally Induced Vibration

International Nuclear Information System (INIS)

Gu, Yuanxian; Chen, Biaosong; Zhang, Hongwu; Zhao, Guozhong

2002-01-01

The numerical method of design optimization for structural thermally induced vibration is originally studied in this paper and implemented in application software JIFEX. The direct and adjoint methods of sensitivity analysis for thermal induced vibration coupled with both linear and nonlinear transient heat conduction is firstly proposed. Based on the finite element method, the structural linear dynamics is treated simultaneously with coupled linear and nonlinear transient heat structural linear dynamics is treated simultaneously with coupled linear and nonlinear transient heat conduction. In the thermal analysis model, the nonlinear heat conduction considered is result from the radiation and temperature-dependent materials. The sensitivity analysis of transient linear and nonlinear heat conduction is performed with the precise time integration method. And then, the sensitivity analysis of structural transient dynamics is performed by the Newmark method. Both the direct method and the adjoint method are employed to derive the sensitivity equations of thermal vibration, and there are two adjoint vectors of structure and heat conduction respectively. The coupling effect of heat conduction on thermal vibration in the sensitivity analysis is particularly investigated. With coupling sensitivity analysis, the optimization model is constructed and solved by the sequential linear programming or sequential quadratic programming algorithm. The methods proposed have been implemented in the application software JIFEX of structural design optimization, and numerical examples are given to illustrate the methods and usage of structural design optimization on thermally induced vibration

Group-sequential analysis may allow for early trial termination

DEFF Research Database (Denmark)

Gerke, Oke; Vilstrup, Mie H; Halekoh, Ulrich

2017-01-01

BACKGROUND: Group-sequential testing is widely used in pivotal therapeutic, but rarely in diagnostic research, although it may save studies, time, and costs. The purpose of this paper was to demonstrate a group-sequential analysis strategy in an intra-observer study on quantitative FDG-PET/CT mea......BACKGROUND: Group-sequential testing is widely used in pivotal therapeutic, but rarely in diagnostic research, although it may save studies, time, and costs. The purpose of this paper was to demonstrate a group-sequential analysis strategy in an intra-observer study on quantitative FDG...... assumed to be normally distributed, and sequential one-sided hypothesis tests on the population standard deviation of the differences against a hypothesised value of 1.5 were performed, employing an alpha spending function. The fixed-sample analysis (N = 45) was compared with the group-sequential analysis...... strategies comprising one (at N = 23), two (at N = 15, 30), or three interim analyses (at N = 11, 23, 34), respectively, which were defined post hoc. RESULTS: When performing interim analyses with one third and two thirds of patients, sufficient agreement could be concluded after the first interim analysis...

Efficient sequential and parallel algorithms for finding edit distance based motifs.

Science.gov (United States)

Pal, Soumitra; Xiao, Peng; Rajasekaran, Sanguthevar

2016-08-18

Motif search is an important step in extracting meaningful patterns from biological data. The general problem of motif search is intractable and there is a pressing need to develop efficient, exact and approximation algorithms to solve this problem. In this paper, we present several novel, exact, sequential and parallel algorithms for solving the (l,d) Edit-distance-based Motif Search (EMS) problem: given two integers l,d and n biological strings, find all strings of length l that appear in each input string with atmost d errors of types substitution, insertion and deletion. One popular technique to solve the problem is to explore for each input string the set of all possible l-mers that belong to the d-neighborhood of any substring of the input string and output those which are common for all input strings. We introduce a novel and provably efficient neighborhood exploration technique. We show that it is enough to consider the candidates in neighborhood which are at a distance exactly d. We compactly represent these candidate motifs using wildcard characters and efficiently explore them with very few repetitions. Our sequential algorithm uses a trie based data structure to efficiently store and sort the candidate motifs. Our parallel algorithm in a multi-core shared memory setting uses arrays for storing and a novel modification of radix-sort for sorting the candidate motifs. The algorithms for EMS are customarily evaluated on several challenging instances such as (8,1), (12,2), (16,3), (20,4), and so on. The best previously known algorithm, EMS1, is sequential and in estimated 3 days solves up to instance (16,3). Our sequential algorithms are more than 20 times faster on (16,3). On other hard instances such as (9,2), (11,3), (13,4), our algorithms are much faster. Our parallel algorithm has more than 600 % scaling performance while using 16 threads. Our algorithms have pushed up the state-of-the-art of EMS solvers and we believe that the techniques introduced in

Sequential decision making in computational sustainability via adaptive submodularity

Science.gov (United States)

Krause, Andreas; Golovin, Daniel; Converse, Sarah J.

2015-01-01

Many problems in computational sustainability require making a sequence of decisions in complex, uncertain environments. Such problems are generally notoriously difficult. In this article, we review the recently discovered notion of adaptive submodularity, an intuitive diminishing returns condition that generalizes the classical notion of submodular set functions to sequential decision problems. Problems exhibiting the adaptive submodularity property can be efficiently and provably near-optimally solved using simple myopic policies. We illustrate this concept in several case studies of interest in computational sustainability: First, we demonstrate how it can be used to efficiently plan for resolving uncertainty in adaptive management scenarios. Secondly, we show how it applies to dynamic conservation planning for protecting endangered species, a case study carried out in collaboration with the US Geological Survey and the US Fish and Wildlife Service.

Comparison of ablation centration after bilateral sequential versus simultaneous LASIK.

Science.gov (United States)

Lin, Jane-Ming; Tsai, Yi-Yu

2005-01-01

To compare ablation centration after bilateral sequential and simultaneous myopic LASIK. A retrospective randomized case series was performed of 670 eyes of 335 consecutive patients who had undergone either bilateral sequential (group 1) or simultaneous (group 2) myopic LASIK between July 2000 and July 2001 at the China Medical University Hospital, Taichung, Taiwan. The ablation centrations of the first and second eyes in the two groups were compared 3 months postoperatively. Of 670 eyes, 274 eyes (137 patients) comprised the sequential group and 396 eyes (198 patients) comprised the simultaneous group. Three months post-operatively, 220 eyes of 110 patients (80%) in the sequential group and 236 eyes of 118 patients (60%) in the simultaneous group provided topographic data for centration analysis. For the first eyes, mean decentration was 0.39 +/- 0.26 mm in the sequential group and 0.41 +/- 0.19 mm in the simultaneous group (P = .30). For the second eyes, mean decentration was 0.28 +/- 0.23 mm in the sequential group and 0.30 +/- 0.21 mm in the simultaneous group (P = .36). Decentration in the second eyes significantly improved in both groups (group 1, P = .02; group 2, P sequential group and 0.32 +/- 0.18 mm in the simultaneous group (P = .33). The difference of ablation center angles between the first and second eyes was 43.2 sequential group and 45.1 +/- 50.8 degrees in the simultaneous group (P = .42). Simultaneous bilateral LASIK is comparable to sequential surgery in ablation centration.

A Survey of Multi-Objective Sequential Decision-Making

NARCIS (Netherlands)

Roijers, D.M.; Vamplew, P.; Whiteson, S.; Dazeley, R.

2013-01-01

Sequential decision-making problems with multiple objectives arise naturally in practice and pose unique challenges for research in decision-theoretic planning and learning, which has largely focused on single-objective settings. This article surveys algorithms designed for sequential

Approximate Bayesian evaluations of measurement uncertainty

Science.gov (United States)

Possolo, Antonio; Bodnar, Olha

2018-04-01

The Guide to the Expression of Uncertainty in Measurement (GUM) includes formulas that produce an estimate of a scalar output quantity that is a function of several input quantities, and an approximate evaluation of the associated standard uncertainty. This contribution presents approximate, Bayesian counterparts of those formulas for the case where the output quantity is a parameter of the joint probability distribution of the input quantities, also taking into account any information about the value of the output quantity available prior to measurement expressed in the form of a probability distribution on the set of possible values for the measurand. The approximate Bayesian estimates and uncertainty evaluations that we present have a long history and illustrious pedigree, and provide sufficiently accurate approximations in many applications, yet are very easy to implement in practice. Differently from exact Bayesian estimates, which involve either (analytical or numerical) integrations, or Markov Chain Monte Carlo sampling, the approximations that we describe involve only numerical optimization and simple algebra. Therefore, they make Bayesian methods widely accessible to metrologists. We illustrate the application of the proposed techniques in several instances of measurement: isotopic ratio of silver in a commercial silver nitrate; odds of cryptosporidiosis in AIDS patients; height of a manometer column; mass fraction of chromium in a reference material; and potential-difference in a Zener voltage standard.

Sequential lineups: shift in criterion or decision strategy?

Science.gov (United States)

Gronlund, Scott D

2004-04-01

R. C. L. Lindsay and G. L. Wells (1985) argued that a sequential lineup enhanced discriminability because it elicited use of an absolute decision strategy. E. B. Ebbesen and H. D. Flowe (2002) argued that a sequential lineup led witnesses to adopt a more conservative response criterion, thereby affecting bias, not discriminability. Height was encoded as absolute (e.g., 6 ft [1.83 m] tall) or relative (e.g., taller than). If a sequential lineup elicited an absolute decision strategy, the principle of transfer-appropriate processing predicted that performance should be best when height was encoded absolutely. Conversely, if a simultaneous lineup elicited a relative decision strategy, performance should be best when height was encoded relatively. The predicted interaction was observed, providing direct evidence for the decision strategies explanation of what happens when witnesses view a sequential lineup.

Sequential reduction of external networks for the security- and short circuit monitor in power system control centers

Energy Technology Data Exchange (ETDEWEB)

Dietze, P [Siemens A.G., Erlangen (Germany, F.R.). Abt. ESTE

1978-01-01

For the evaluation of the effects of switching operations or simulation of line, transformer, and generator outages the influence of interconnected neighbor networks is modelled by network equivalents in the process computer. The basic passive conductivity model is produced by sequential reduction and adapted to fit the active network behavior. The reduction routine uses the admittance matrix, sparse technique and optimal ordering; it is applicable to process computer applications.

Learning of state-space models with highly informative observations: A tempered sequential Monte Carlo solution

Science.gov (United States)

Svensson, Andreas; Schön, Thomas B.; Lindsten, Fredrik

2018-05-01

Probabilistic (or Bayesian) modeling and learning offers interesting possibilities for systematic representation of uncertainty using probability theory. However, probabilistic learning often leads to computationally challenging problems. Some problems of this type that were previously intractable can now be solved on standard personal computers thanks to recent advances in Monte Carlo methods. In particular, for learning of unknown parameters in nonlinear state-space models, methods based on the particle filter (a Monte Carlo method) have proven very useful. A notoriously challenging problem, however, still occurs when the observations in the state-space model are highly informative, i.e. when there is very little or no measurement noise present, relative to the amount of process noise. The particle filter will then struggle in estimating one of the basic components for probabilistic learning, namely the likelihood p (data | parameters). To this end we suggest an algorithm which initially assumes that there is substantial amount of artificial measurement noise present. The variance of this noise is sequentially decreased in an adaptive fashion such that we, in the end, recover the original problem or possibly a very close approximation of it. The main component in our algorithm is a sequential Monte Carlo (SMC) sampler, which gives our proposed method a clear resemblance to the SMC2 method. Another natural link is also made to the ideas underlying the approximate Bayesian computation (ABC). We illustrate it with numerical examples, and in particular show promising results for a challenging Wiener-Hammerstein benchmark problem.

Multi-sheet surface rebinning methods for reconstruction from asymmetrically truncated cone beam projections: I. Approximation and optimality

International Nuclear Information System (INIS)

Betcke, Marta M; Lionheart, William R B

2013-01-01

The mechanical motion of the gantry in conventional cone beam CT scanners restricts the speed of data acquisition in applications with near real time requirements. A possible resolution of this problem is to replace the moving source detector assembly with static parts that are electronically activated. An example of such a system is the Rapiscan Systems RTT80 real time tomography scanner, with a static ring of sources and axially offset static cylinder of detectors. A consequence of such a design is asymmetrical axial truncation of the cone beam projections resulting, in the sense of integral geometry, in severely incomplete data. In particular we collect data only in a fraction of the Tam–Danielsson window, hence the standard cone beam reconstruction techniques do not apply. In this work we propose a family of multi-sheet surface rebinning methods for reconstruction from such truncated projections. The proposed methods combine analytical and numerical ideas utilizing linearity of the ray transform to reconstruct data on multi-sheet surfaces, from which the volumetric image is obtained through deconvolution. In this first paper in the series, we discuss the rebinning to multi-sheet surfaces. In particular we concentrate on the underlying transforms on multi-sheet surfaces and their approximation with data collected by offset multi-source scanning geometries like the RTT. The optimal multi-sheet surface and the corresponding rebinning function are found as a solution of a variational problem. In the case of the quadratic objective, the variational problem for the optimal rebinning pair can be solved by a globally convergent iteration. Examples of optimal rebinning pairs are computed for different trajectories. We formulate the axial deconvolution problem for the recovery of the volumetric image from the reconstructions on multi-sheet surfaces. Efficient and stable solution of the deconvolution problem is the subject of the second paper in this series (Betcke and

Device-independent two-party cryptography secure against sequential attacks

International Nuclear Information System (INIS)

Kaniewski, Jędrzej; Wehner, Stephanie

2016-01-01

The goal of two-party cryptography is to enable two parties, Alice and Bob, to solve common tasks without the need for mutual trust. Examples of such tasks are private access to a database, and secure identification. Quantum communication enables security for all of these problems in the noisy-storage model by sending more signals than the adversary can store in a certain time frame. Here, we initiate the study of device-independent (DI) protocols for two-party cryptography in the noisy-storage model. Specifically, we present a relatively easy to implement protocol for a cryptographic building block known as weak string erasure and prove its security even if the devices used in the protocol are prepared by the dishonest party. DI two-party cryptography is made challenging by the fact that Alice and Bob do not trust each other, which requires new techniques to establish security. We fully analyse the case of memoryless devices (for which sequential attacks are optimal) and the case of sequential attacks for arbitrary devices. The key ingredient of the proof, which might be of independent interest, is an explicit (and tight) relation between the violation of the Clauser–Horne–Shimony–Holt inequality observed by Alice and Bob and uncertainty generated by Alice against Bob who is forced to measure his system before finding out Alice’s setting (guessing with postmeasurement information). In particular, we show that security is possible for arbitrarily small violation. (paper)

Device-independent two-party cryptography secure against sequential attacks

Science.gov (United States)

Kaniewski, Jędrzej; Wehner, Stephanie

2016-05-01

The goal of two-party cryptography is to enable two parties, Alice and Bob, to solve common tasks without the need for mutual trust. Examples of such tasks are private access to a database, and secure identification. Quantum communication enables security for all of these problems in the noisy-storage model by sending more signals than the adversary can store in a certain time frame. Here, we initiate the study of device-independent (DI) protocols for two-party cryptography in the noisy-storage model. Specifically, we present a relatively easy to implement protocol for a cryptographic building block known as weak string erasure and prove its security even if the devices used in the protocol are prepared by the dishonest party. DI two-party cryptography is made challenging by the fact that Alice and Bob do not trust each other, which requires new techniques to establish security. We fully analyse the case of memoryless devices (for which sequential attacks are optimal) and the case of sequential attacks for arbitrary devices. The key ingredient of the proof, which might be of independent interest, is an explicit (and tight) relation between the violation of the Clauser-Horne-Shimony-Holt inequality observed by Alice and Bob and uncertainty generated by Alice against Bob who is forced to measure his system before finding out Alice’s setting (guessing with postmeasurement information). In particular, we show that security is possible for arbitrarily small violation.

Optimizing delivery of a behavioral pain intervention in cancer patients using a sequential multiple assignment randomized trial SMART.

Science.gov (United States)

Kelleher, Sarah A; Dorfman, Caroline S; Plumb Vilardaga, Jen C; Majestic, Catherine; Winger, Joseph; Gandhi, Vicky; Nunez, Christine; Van Denburg, Alyssa; Shelby, Rebecca A; Reed, Shelby D; Murphy, Susan; Davidian, Marie; Laber, Eric B; Kimmick, Gretchen G; Westbrook, Kelly W; Abernethy, Amy P; Somers, Tamara J

2017-06-01

Pain is common in cancer patients and results in lower quality of life, depression, poor physical functioning, financial difficulty, and decreased survival time. Behavioral pain interventions are effective and nonpharmacologic. Traditional randomized controlled trials (RCT) test interventions of fixed time and dose, which poorly represent successive treatment decisions in clinical practice. We utilize a novel approach to conduct a RCT, the sequential multiple assignment randomized trial (SMART) design, to provide comparative evidence of: 1) response to differing initial doses of a pain coping skills training (PCST) intervention and 2) intervention dose sequences adjusted based on patient response. We also examine: 3) participant characteristics moderating intervention responses and 4) cost-effectiveness and practicality. Breast cancer patients (N=327) having pain (ratings≥5) are recruited and randomly assigned to: 1) PCST-Full or 2) PCST-Brief. PCST-Full consists of 5 PCST sessions. PCST-Brief consists of one 60-min PCST session. Five weeks post-randomization, participants re-rate their pain and are re-randomized, based on intervention response, to receive additional PCST sessions, maintenance calls, or no further intervention. Participants complete measures of pain intensity, interference and catastrophizing. Novel RCT designs may provide information that can be used to optimize behavioral pain interventions to be adaptive, better meet patients' needs, reduce barriers, and match with clinical practice. This is one of the first trials to use a novel design to evaluate symptom management in cancer patients and in chronic illness; if successful, it could serve as a model for future work with a wide range of chronic illnesses. Copyright © 2016. Published by Elsevier Inc.

How to Read the Tractatus Sequentially

Directory of Open Access Journals (Sweden)

Tim Kraft

2016-11-01

Full Text Available One of the unconventional features of Wittgenstein’s Tractatus Logico-Philosophicus is its use of an elaborated and detailed numbering system. Recently, Bazzocchi, Hacker und Kuusela have argued that the numbering system means that the Tractatus must be read and interpreted not as a sequentially ordered book, but as a text with a two-dimensional, tree-like structure. Apart from being able to explain how the Tractatus was composed, the tree reading allegedly solves exegetical issues both on the local (e. g. how 4.02 fits into the series of remarks surrounding it and the global level (e. g. relation between ontology and picture theory, solipsism and the eye analogy, resolute and irresolute readings. This paper defends the sequential reading against the tree reading. After presenting the challenges generated by the numbering system and the two accounts as attempts to solve them, it is argued that Wittgenstein’s own explanation of the numbering system, anaphoric references within the Tractatus and the exegetical issues mentioned above do not favour the tree reading, but a version of the sequential reading. This reading maintains that the remarks of the Tractatus form a sequential chain: The role of the numbers is to indicate how remarks on different levels are interconnected to form a concise, surveyable and unified whole.

Sequential reductive and oxidative biodegradation of chloroethenes stimulated in a coupled bioelectro-process.

Science.gov (United States)

Lohner, Svenja T; Becker, Dirk; Mangold, Klaus-Michael; Tiehm, Andreas

2011-08-01

This article for the first time demonstrates successful application of electrochemical processes to stimulate sequential reductive/oxidative microbial degradation of perchloroethene (PCE) in mineral medium and in contaminated groundwater. In a flow-through column system, hydrogen generation at the cathode supported reductive dechlorination of PCE to cis-dichloroethene (cDCE), vinyl chloride (VC), and ethene (ETH). Electrolytically generated oxygen at the anode allowed subsequent oxidative degradation of the lower chlorinated metabolites. Aerobic cometabolic degradation of cDCE proved to be the bottleneck for complete metabolite elimination. Total removal of chloroethenes was demonstrated for a PCE load of approximately 1.5 μmol/d. In mineral medium, long-term operation with stainless steel electrodes was demonstrated for more than 300 days. In contaminated groundwater, corrosion of the stainless steel anode occurred, whereas DSA (dimensionally stable anodes) proved to be stable. Precipitation of calcareous deposits was observed at the cathode, resulting in a higher voltage demand and reduced dechlorination activity. With DSA and groundwater from a contaminated site, complete degradation of chloroethenes in groundwater was obtained for two months thus demonstrating the feasibility of the sequential bioelectro-approach for field application.

Aggregators’ Optimal Bidding Strategy in Sequential Day-Ahead and Intraday Electricity Spot Markets

Directory of Open Access Journals (Sweden)

Xiaolin Ayón

2017-04-01

Full Text Available This paper proposes a probabilistic optimization method that produces optimal bidding curves to be submitted by an aggregator to the day-ahead electricity market and the intraday market, considering the flexible demand of his customers (based in time dependent resources such as batteries and shiftable demand and taking into account the possible imbalance costs as well as the uncertainty of forecasts (market prices, demand, and renewable energy sources (RES generation. The optimization strategy aims to minimize the total cost of the traded energy over a whole day, taking into account the intertemporal constraints. The proposed formulation leads to the solution of different linear optimization problems, following the natural temporal sequence of electricity spot markets. Intertemporal constraints regarding time dependent resources are fulfilled through a scheduling process performed after the day-ahead market clearing. Each of the different problems is of moderate dimension and requires short computation times. The benefits of the proposed strategy are assessed comparing the payments done by an aggregator over a sample period of one year following different deterministic and probabilistic strategies. Results show that probabilistic strategy reports better benefits for aggregators participating in power markets.

Multichannel, sequential or combined X-ray spectrometry

International Nuclear Information System (INIS)

Florestan, J.

1979-01-01

X-ray spectrometer qualities and defects are evaluated for sequential and multichannel categories. Multichannel X-ray spectrometer has time-coherency advantage and its results could be more reproducible; on the other hand some spatial incoherency limits low percentage and traces applications, specially when backgrounds are very variable. In this last case, sequential X-ray spectrometer would find again great usefulness [fr

Sequential optimization of a terrestrial biosphere model constrained by multiple satellite based products

Science.gov (United States)

Ichii, K.; Kondo, M.; Wang, W.; Hashimoto, H.; Nemani, R. R.

2012-12-01

Various satellite-based spatial products such as evapotranspiration (ET) and gross primary productivity (GPP) are now produced by integration of ground and satellite observations. Effective use of these multiple satellite-based products in terrestrial biosphere models is an important step toward better understanding of terrestrial carbon and water cycles. However, due to the complexity of terrestrial biosphere models with large number of model parameters, the application of these spatial data sets in terrestrial biosphere models is difficult. In this study, we established an effective but simple framework to refine a terrestrial biosphere model, Biome-BGC, using multiple satellite-based products as constraints. We tested the framework in the monsoon Asia region covered by AsiaFlux observations. The framework is based on the hierarchical analysis (Wang et al. 2009) with model parameter optimization constrained by satellite-based spatial data. The Biome-BGC model is separated into several tiers to minimize the freedom of model parameter selections and maximize the independency from the whole model. For example, the snow sub-model is first optimized using MODIS snow cover product, followed by soil water sub-model optimized by satellite-based ET (estimated by an empirical upscaling method; Support Vector Regression (SVR) method; Yang et al. 2007), photosynthesis model optimized by satellite-based GPP (based on SVR method), and respiration and residual carbon cycle models optimized by biomass data. As a result of initial assessment, we found that most of default sub-models (e.g. snow, water cycle and carbon cycle) showed large deviations from remote sensing observations. However, these biases were removed by applying the proposed framework. For example, gross primary productivities were initially underestimated in boreal and temperate forest and overestimated in tropical forests. However, the parameter optimization scheme successfully reduced these biases. Our analysis

Induction of simultaneous and sequential malolactic fermentation in durian wine.

Science.gov (United States)

Taniasuri, Fransisca; Lee, Pin-Rou; Liu, Shao-Quan

2016-08-02

This study represented for the first time the impact of malolactic fermentation (MLF) induced by Oenococcus oeni and its inoculation strategies (simultaneous vs. sequential) on the fermentation performance as well as aroma compound profile of durian wine. There was no negative impact of simultaneous inoculation of O. oeni and Saccharomyces cerevisiae on the growth and fermentation kinetics of S. cerevisiae as compared to sequential fermentation. Simultaneous MLF did not lead to an excessive increase in volatile acidity as compared to sequential MLF. The kinetic changes of organic acids (i.e. malic, lactic, succinic, acetic and α-ketoglutaric acids) varied with simultaneous and sequential MLF relative to yeast alone. MLF, regardless of inoculation mode, resulted in higher production of fermentation-derived volatiles as compared to control (alcoholic fermentation only), including esters, volatile fatty acids, and terpenes, except for higher alcohols. Most indigenous volatile sulphur compounds in durian were decreased to trace levels with little differences among the control, simultaneous and sequential MLF. Among the different wines, the wine with simultaneous MLF had higher concentrations of terpenes and acetate esters while sequential MLF had increased concentrations of medium- and long-chain ethyl esters. Relative to alcoholic fermentation only, both simultaneous and sequential MLF reduced acetaldehyde substantially with sequential MLF being more effective. These findings illustrate that MLF is an effective and novel way of modulating the volatile and aroma compound profile of durian wine. Copyright © 2016 Elsevier B.V. All rights reserved.

Sequential Banking.

OpenAIRE

Bizer, David S; DeMarzo, Peter M

1992-01-01

The authors study environments in which agents may borrow sequentially from more than one leader. Although debt is prioritized, additional lending imposes an externality on prior debt because, with moral hazard, the probability of repayment of prior loans decreases. Equilibrium interest rates are higher than they would be if borrowers could commit to borrow from at most one bank. Even though the loan terms are less favorable than they would be under commitment, the indebtedness of borrowers i...

Equivalence between quantum simultaneous games and quantum sequential games

OpenAIRE

Kobayashi, Naoki

2007-01-01

A framework for discussing relationships between different types of games is proposed. Within the framework, quantum simultaneous games, finite quantum simultaneous games, quantum sequential games, and finite quantum sequential games are defined. In addition, a notion of equivalence between two games is defined. Finally, the following three theorems are shown: (1) For any quantum simultaneous game G, there exists a quantum sequential game equivalent to G. (2) For any finite quantum simultaneo...

Accounting for Heterogeneous Returns in Sequential Schooling Decisions

NARCIS (Netherlands)

Zamarro, G.

2006-01-01

This paper presents a method for estimating returns to schooling that takes into account that returns may be heterogeneous among agents and that educational decisions are made sequentially.A sequential decision model is interesting because it explicitly considers that the level of education of each

Simultaneous Versus Sequential Ptosis and Strabismus Surgery in Children.

Science.gov (United States)

Revere, Karen E; Binenbaum, Gil; Li, Jonathan; Mills, Monte D; Katowitz, William R; Katowitz, James A

The authors sought to compare the clinical outcomes of simultaneous versus sequential ptosis and strabismus surgery in children. Retrospective, single-center cohort study of children requiring both ptosis and strabismus surgery on the same eye. Simultaneous surgeries were performed during a single anesthetic event; sequential surgeries were performed at least 7 weeks apart. Outcomes were ptosis surgery success (margin reflex distance 1 ≥ 2 mm, good eyelid contour, and good eyelid crease); strabismus surgery success (ocular alignment within 10 prism diopters of orthophoria and/or improved head position); surgical complications; and reoperations. Fifty-six children were studied, 38 had simultaneous surgery and 18 sequential. Strabismus surgery was performed first in 38/38 simultaneous and 6/18 sequential cases. Mean age at first surgery was 64 months, with mean follow up 27 months. A total of 75% of children had congenital ptosis; 64% had comitant strabismus. A majority of ptosis surgeries were frontalis sling (59%) or Fasanella-Servat (30%) procedures. There were no significant differences between simultaneous and sequential groups with regards to surgical success rates, complications, or reoperations (all p > 0.28). In the first comparative study of simultaneous versus sequential ptosis and strabismus surgery, no advantage for sequential surgery was seen. Despite a theoretical risk of postoperative eyelid malposition or complications when surgeries were performed in a combined manner, the rate of such outcomes was not increased with simultaneous surgeries. Performing ptosis and strabismus surgery together appears to be clinically effective and safe, and reduces anesthesia exposure during childhood.

Approximate Inference for Wireless Communications

DEFF Research Database (Denmark)

Hansen, Morten

This thesis investigates signal processing techniques for wireless communication receivers. The aim is to improve the performance or reduce the computationally complexity of these, where the primary focus area is cellular systems such as Global System for Mobile communications (GSM) (and extensions...... to the optimal one, which usually requires an unacceptable high complexity. Some of the treated approximate methods are based on QL-factorization of the channel matrix. In the work presented in this thesis it is proven how the QL-factorization of frequency-selective channels asymptotically provides the minimum...

Forced Sequence Sequential Decoding

DEFF Research Database (Denmark)

Jensen, Ole Riis

In this thesis we describe a new concatenated decoding scheme based on iterations between an inner sequentially decoded convolutional code of rate R=1/4 and memory M=23, and block interleaved outer Reed-Solomon codes with non-uniform profile. With this scheme decoding with good performance...... is possible as low as Eb/No=0.6 dB, which is about 1.7 dB below the signal-to-noise ratio that marks the cut-off rate for the convolutional code. This is possible since the iteration process provides the sequential decoders with side information that allows a smaller average load and minimizes the probability...... of computational overflow. Analytical results for the probability that the first Reed-Solomon word is decoded after C computations are presented. This is supported by simulation results that are also extended to other parameters....

A sequential quadratic programming algorithm using an incomplete solution of the subproblem

Energy Technology Data Exchange (ETDEWEB)

Murray, W. [Stanford Univ., CA (United States). Systems Optimization Lab.; Prieto, F.J. [Universidad `Carlos III` de Madrid (Spain). Dept. de Estadistica y Econometria

1993-05-01

We analyze sequential quadratic programming (SQP) methods to solve nonlinear constrained optimization problems that are more flexible in their definition than standard SQP methods. The type of flexibility introduced is motivated by the necessity to deviate from the standard approach when solving large problems. Specifically we no longer require a minimizer of the QP subproblem to be determined or particular Lagrange multiplier estimates to be used. Our main focus is on an SQP algorithm that uses a particular augmented Lagrangian merit function. New results are derived for this algorithm under weaker conditions than previously assumed; in particular, it is not assumed that the iterates lie on a compact set.

Empty tracks optimization based on Z-Map model

Science.gov (United States)

Liu, Le; Yan, Guangrong; Wang, Zaijun; Zang, Genao

2017-12-01

For parts with many features, there are more empty tracks during machining. If these tracks are not optimized, the machining efficiency will be seriously affected. In this paper, the characteristics of the empty tracks are studied in detail. Combining with the existing optimization algorithm, a new tracks optimization method based on Z-Map model is proposed. In this method, the tool tracks are divided into the unit processing section, and then the Z-Map model simulation technique is used to analyze the order constraint between the unit segments. The empty stroke optimization problem is transformed into the TSP with sequential constraints, and then through the genetic algorithm solves the established TSP problem. This kind of optimization method can not only optimize the simple structural parts, but also optimize the complex structural parts, so as to effectively plan the empty tracks and greatly improve the processing efficiency.

Strong convergence and convergence rates of approximating solutions for algebraic Riccati equations in Hilbert spaces

Science.gov (United States)

Ito, Kazufumi

1987-01-01

The linear quadratic optimal control problem on infinite time interval for linear time-invariant systems defined on Hilbert spaces is considered. The optimal control is given by a feedback form in terms of solution pi to the associated algebraic Riccati equation (ARE). A Ritz type approximation is used to obtain a sequence pi sup N of finite dimensional approximations of the solution to ARE. A sufficient condition that shows pi sup N converges strongly to pi is obtained. Under this condition, a formula is derived which can be used to obtain a rate of convergence of pi sup N to pi. The results of the Galerkin approximation is demonstrated and applied for parabolic systems and the averaging approximation for hereditary differential systems.

Reading Remediation Based on Sequential and Simultaneous Processing.

Science.gov (United States)

Gunnison, Judy; And Others

1982-01-01

The theory postulating a dichotomy between sequential and simultaneous processing is reviewed and its implications for remediating reading problems are reviewed. Research is cited on sequential-simultaneous processing for early and advanced reading. A list of remedial strategies based on the processing dichotomy addresses decoding and lexical…

Involving young people in decision making about sequential cochlear implantation.

Science.gov (United States)

Ion, Rebecca; Cropper, Jenny; Walters, Hazel

2013-11-01

The National Institute for Health and Clinical Excellence guidelines recommended young people who currently have one cochlear implant be offered assessment for a second, sequential implant, due to the reported improvements in sound localization and speech perception in noise. The possibility and benefits of group information and counselling assessments were considered. Previous research has shown advantages of group sessions involving young people and their families and such groups which also allow young people opportunity to discuss their concerns separately to their parents/guardians are found to be 'hugely important'. Such research highlights the importance of involving children in decision-making processes. Families considering a sequential cochlear implant were invited to a group information/counselling session, which included time for parents and children to meet separately. Fourteen groups were held with approximately four to five families in each session, totalling 62 patients. The sessions were facilitated by the multi-disciplinary team, with a particular psychological focus in the young people's session. Feedback from families has demonstrated positive support for this format. Questionnaire feedback, to which nine families responded, indicated that seven preferred the group session to an individual session and all approved of separate groups for the child and parents/guardians. Overall the group format and psychological focus were well received in this typically surgical setting and emphasized the importance of involving the young person in the decision-making process. This positive feedback also opens up the opportunity to use a group format in other assessment processes.

Hierarchical matrix approximation of large covariance matrices

KAUST Repository

Litvinenko, Alexander

2015-01-07

We approximate large non-structured covariance matrices in the H-matrix format with a log-linear computational cost and storage O(n log n). We compute inverse, Cholesky decomposition and determinant in H-format. As an example we consider the class of Matern covariance functions, which are very popular in spatial statistics, geostatistics, machine learning and image analysis. Applications are: kriging and optimal design

Hierarchical matrix approximation of large covariance matrices

KAUST Repository

Litvinenko, Alexander; Genton, Marc G.; Sun, Ying; Tempone, Raul

2015-01-01

We approximate large non-structured covariance matrices in the H-matrix format with a log-linear computational cost and storage O(n log n). We compute inverse, Cholesky decomposition and determinant in H-format. As an example we consider the class of Matern covariance functions, which are very popular in spatial statistics, geostatistics, machine learning and image analysis. Applications are: kriging and optimal design

Improving adsorption dryer energy efficiency by simultaneous optimization and heat integration

NARCIS (Netherlands)

Atuonwu, J.C.; Straten, G. van; Deventer, H.C. van; Boxtel, A.J.B. van

2011-01-01

Conventionally, energy-saving techniques in drying technology are sequential in nature. First, the dryer is optimized without heat recovery and then, based on the obtained process conditions, heat recovery possibilities are explored. This work presents a methodology for energy-efficient adsorption

Enhancing inulinase yield by irradiation mutation associated with optimization of culture conditions

Directory of Open Access Journals (Sweden)

Yafeng Gou

2015-09-01

Full Text Available A new inulinase-producing strain was isolated from rhizosphere soils of Jerusalem artichoke collected from Shihezi (Xinjiang, China using Jerusalem artichoke power (JAP as sole carbon source. It was identified as an Aspergillus niger strain by analysis of 16S rRNA. To improve inulinase production, this fungus was subjected to mutagenesis induced by 60Co γ-irradiation. A genetically stable mutant (designated E12 was obtained and it showed 2.7-fold higher inulinase activity (128 U/mL than the parental strain in the supernatant of a submerged culture. Sequential methodology was used to optimize the inulinase production of stain E12. A screening trial was first performed using Plackett-Burman design and variables with statistically significant effects on inulinase bio-production were identified. These significant factors were further optimized by central composite design experiments and response surface methodology. Finally, it was found that the maximum inulinase production (185 U/mL could be achieved under the optimized conditions namely pH 7.0, yeast extract concentration of 5.0 g/L, JAP concentration of 66.5 g/L, peptone concentration of 29.1 g/L, solution volume of 49.4 mL in 250-mL shake flasks, agitation speed of 180 rpm, and fermentation time of 60 h. The yield of inulinase under optimized culture conditions was approximately 1.4-fold of that obtained by using basal culture medium. These findings are of significance for the potential industrial application of the mutant E12.

Optimization of sequential decisions by least squares Monte Carlo method

DEFF Research Database (Denmark)

Nishijima, Kazuyoshi; Anders, Annett

change adaptation measures, and evacuation of people and assets in the face of an emerging natural hazard event. Focusing on the last example, an efficient solution scheme is proposed by Anders and Nishijima (2011). The proposed solution scheme takes basis in the least squares Monte Carlo method, which...... is proposed by Longstaff and Schwartz (2001) for pricing of American options. The present paper formulates the decision problem in a more general manner and explains how the solution scheme proposed by Anders and Nishijima (2011) is implemented for the optimization of the formulated decision problem...

Approximating the Pareto Set of Multiobjective Linear Programs via Robust Optimization

NARCIS (Netherlands)

Gorissen, B.L.; den Hertog, D.

2012-01-01

Abstract: The Pareto set of a multiobjective optimization problem consists of the solutions for which one or more objectives can not be improved without deteriorating one or more other objectives. We consider problems with linear objectives and linear constraints and use Adjustable Robust

C-quence: a tool for analyzing qualitative sequential data.

Science.gov (United States)

Duncan, Starkey; Collier, Nicholson T

2002-02-01

C-quence is a software application that matches sequential patterns of qualitative data specified by the user and calculates the rate of occurrence of these patterns in a data set. Although it was designed to facilitate analyses of face-to-face interaction, it is applicable to any data set involving categorical data and sequential information. C-quence queries are constructed using a graphical user interface. The program does not limit the complexity of the sequential patterns specified by the user.

Sequential effect of phages and cold nitrogen plasma against Escherichia coli O157:H7 biofilms on different vegetables.

Science.gov (United States)

Cui, Haiying; Bai, Mei; Yuan, Lu; Surendhiran, Duraiarasan; Lin, Lin

2018-03-02

Escherichia coli O157:H7 (E. coli O157:H7) is one of the most common pathogens in fresh vegetables and fruits, and most of the diseases produced by E. coli O157:H7 are associated with biofilms. Cold nitrogen plasma (CNP) is a cold sterilization technique which has no residue. However to completely eliminate the biofilm on the surface of vegetables the processing power and time of CNP have to be enhanced, which will impact on the quality of fruits and vegetables. Thus the sequential treatment of CNP and phage techniques was engineered in this study. Compared to treatment performed separately, sequential treatment not only had more mild treatment conditions as 400W CNP treatment for 2min and 5% phage treatment for 30min, but also exhibited more remarkable effect on eradicating E. coli O157:H7 biofilms in vitro and on vegetables. The population of E. coli O157:H7 was approximately reduced by 2logCFU/cm 2 after individual treatment of 5% phages for 30min or 500W CNP for 3min. While the sequential treatment of CNP (400W, 2min) and phages (5%, 30min) reduced the E. coli O157:H7 viable count in biofilm by 5.71logCFU/cm 2 . Therefore, the sequential treatment holds a great promise to improve the current treatment systems of bacterial contamination on different vegetable surfaces. Copyright © 2018 Elsevier B.V. All rights reserved.

Top-down attention affects sequential regularity representation in the human visual system.

Science.gov (United States)

Kimura, Motohiro; Widmann, Andreas; Schröger, Erich

2010-08-01

Recent neuroscience studies using visual mismatch negativity (visual MMN), an event-related brain potential (ERP) index of memory-mismatch processes in the visual sensory system, have shown that although sequential regularities embedded in successive visual stimuli can be automatically represented in the visual sensory system, an existence of sequential regularity itself does not guarantee that the sequential regularity will be automatically represented. In the present study, we investigated the effects of top-down attention on sequential regularity representation in the visual sensory system. Our results showed that a sequential regularity (SSSSD) embedded in a modified oddball sequence where infrequent deviant (D) and frequent standard stimuli (S) differing in luminance were regularly presented (SSSSDSSSSDSSSSD...) was represented in the visual sensory system only when participants attended the sequential regularity in luminance, but not when participants ignored the stimuli or simply attended the dimension of luminance per se. This suggests that top-down attention affects sequential regularity representation in the visual sensory system and that top-down attention is a prerequisite for particular sequential regularities to be represented. Copyright 2010 Elsevier B.V. All rights reserved.

The Approximate Bayesian Computation methods in the localization of the atmospheric contamination source

International Nuclear Information System (INIS)

Kopka, P; Wawrzynczak, A; Borysiewicz, M

2015-01-01

In many areas of application, a central problem is a solution to the inverse problem, especially estimation of the unknown model parameters to model the underlying dynamics of a physical system precisely. In this situation, the Bayesian inference is a powerful tool to combine observed data with prior knowledge to gain the probability distribution of searched parameters. We have applied the modern methodology named Sequential Approximate Bayesian Computation (S-ABC) to the problem of tracing the atmospheric contaminant source. The ABC is technique commonly used in the Bayesian analysis of complex models and dynamic system. Sequential methods can significantly increase the efficiency of the ABC. In the presented algorithm, the input data are the on-line arriving concentrations of released substance registered by distributed sensor network from OVER-LAND ATMOSPHERIC DISPERSION (OLAD) experiment. The algorithm output are the probability distributions of a contamination source parameters i.e. its particular location, release rate, speed and direction of the movement, start time and duration. The stochastic approach presented in this paper is completely general and can be used in other fields where the parameters of the model bet fitted to the observable data should be found. (paper)

Fatigue reduction during aggregated and distributed sequential stimulation.

Science.gov (United States)

Bergquist, Austin J; Babbar, Vishvek; Ali, Saima; Popovic, Milos R; Masani, Kei

2017-08-01

Transcutaneous neuromuscular electrical stimulation (NMES) can generate muscle contractions for rehabilitation and exercise. However, NMES-evoked contractions are limited by fatigue when they are delivered "conventionally" (CONV) using a single active electrode. Researchers have developed "sequential" (SEQ) stimulation, involving rotation of pulses between multiple "aggregated" (AGGR-SEQ) or "distributed" (DISTR-SEQ) active electrodes, to reduce fatigue (torque-decline) by reducing motor unit discharge rates. The primary objective was to compare fatigue-related outcomes, "potentiation," "variability," and "efficiency" between CONV, AGGR-SEQ, and DISTR-SEQ stimulation of knee extensors in healthy participants. Torque and current were recorded during testing with fatiguing trains using each NMES type under isometric and isokinetic (180°/s) conditions. Compared with CONV stimulation, SEQ techniques reduced fatigue-related outcomes, increased potentiation, did not affect variability, and reduced efficiency. SEQ techniques hold promise for reducing fatigue during NMES-based rehabilitation and exercise; however, optimization is required to improve efficiency. Muscle Nerve 56: 271-281, 2017. © 2016 Wiley Periodicals, Inc.

Dynamic optimization deterministic and stochastic models

CERN Document Server

Hinderer, Karl; Stieglitz, Michael

2016-01-01

This book explores discrete-time dynamic optimization and provides a detailed introduction to both deterministic and stochastic models. Covering problems with finite and infinite horizon, as well as Markov renewal programs, Bayesian control models and partially observable processes, the book focuses on the precise modelling of applications in a variety of areas, including operations research, computer science, mathematics, statistics, engineering, economics and finance. Dynamic Optimization is a carefully presented textbook which starts with discrete-time deterministic dynamic optimization problems, providing readers with the tools for sequential decision-making, before proceeding to the more complicated stochastic models. The authors present complete and simple proofs and illustrate the main results with numerous examples and exercises (without solutions). With relevant material covered in four appendices, this book is completely self-contained.

A fast and efficient method for sequential cone-beam tomography

International Nuclear Information System (INIS)

Koehler, Th.; Proksa, R.; Grass, M.

2001-01-01

Sequential cone-beam tomography is a method that uses data of two or more parallel circular trajectories of a cone-beam scanner to reconstruct the object function. We propose a condition for the data acquisition that ensures that all object points between two successive circles are irradiated over an angular span of the x-ray source position of exactly 360 deg. in total as seen along the rotation axis. A fast and efficient approximative reconstruction method for the proposed acquisition is presented which uses data from exactly 360 deg. for every object point. It is based on the Tent-FDK method which was recently developed for single circular cone-beam CT. The measurement geometry does not provide sufficient data for exact reconstruction but it is shown that the proposed reconstruction method provides satisfying image quality for small cone angles

A Sequential Quadratically Constrained Quadratic Programming Method of Feasible Directions

International Nuclear Information System (INIS)

Jian Jinbao; Hu Qingjie; Tang Chunming; Zheng Haiyan

2007-01-01

In this paper, a sequential quadratically constrained quadratic programming method of feasible directions is proposed for the optimization problems with nonlinear inequality constraints. At each iteration of the proposed algorithm, a feasible direction of descent is obtained by solving only one subproblem which consist of a convex quadratic objective function and simple quadratic inequality constraints without the second derivatives of the functions of the discussed problems, and such a subproblem can be formulated as a second-order cone programming which can be solved by interior point methods. To overcome the Maratos effect, an efficient higher-order correction direction is obtained by only one explicit computation formula. The algorithm is proved to be globally convergent and superlinearly convergent under some mild conditions without the strict complementarity. Finally, some preliminary numerical results are reported

Mining compressing sequential problems

NARCIS (Netherlands)

Hoang, T.L.; Mörchen, F.; Fradkin, D.; Calders, T.G.K.

2012-01-01

Compression based pattern mining has been successfully applied to many data mining tasks. We propose an approach based on the minimum description length principle to extract sequential patterns that compress a database of sequences well. We show that mining compressing patterns is NP-Hard and

Sparse approximation of multilinear problems with applications to kernel-based methods in UQ

KAUST Repository

Nobile, Fabio; Tempone, Raul; Wolfers, Sö ren

2017-01-01

We provide a framework for the sparse approximation of multilinear problems and show that several problems in uncertainty quantification fit within this framework. In these problems, the value of a multilinear map has to be approximated using approximations of different accuracy and computational work of the arguments of this map. We propose and analyze a generalized version of Smolyak’s algorithm, which provides sparse approximation formulas with convergence rates that mitigate the curse of dimension that appears in multilinear approximation problems with a large number of arguments. We apply the general framework to response surface approximation and optimization under uncertainty for parametric partial differential equations using kernel-based approximation. The theoretical results are supplemented by numerical experiments.

Sparse approximation of multilinear problems with applications to kernel-based methods in UQ

KAUST Repository

Nobile, Fabio

2017-11-16

We provide a framework for the sparse approximation of multilinear problems and show that several problems in uncertainty quantification fit within this framework. In these problems, the value of a multilinear map has to be approximated using approximations of different accuracy and computational work of the arguments of this map. We propose and analyze a generalized version of Smolyak’s algorithm, which provides sparse approximation formulas with convergence rates that mitigate the curse of dimension that appears in multilinear approximation problems with a large number of arguments. We apply the general framework to response surface approximation and optimization under uncertainty for parametric partial differential equations using kernel-based approximation. The theoretical results are supplemented by numerical experiments.

Computing sequential equilibria for two-player games

DEFF Research Database (Denmark)

Miltersen, Peter Bro

2006-01-01

Koller, Megiddo and von Stengel showed how to efficiently compute minimax strategies for two-player extensive-form zero-sum games with imperfect information but perfect recall using linear programming and avoiding conversion to normal form. Their algorithm has been used by AI researchers...... for constructing prescriptive strategies for concrete, often fairly large games. Koller and Pfeffer pointed out that the strategies obtained by the algorithm are not necessarily sequentially rational and that this deficiency is often problematic for the practical applications. We show how to remove this deficiency...... by modifying the linear programs constructed by Koller, Megiddo and von Stengel so that pairs of strategies forming a sequential equilibrium are computed. In particular, we show that a sequential equilibrium for a two-player zero-sum game with imperfect information but perfect recall can be found in polynomial...

Computing Sequential Equilibria for Two-Player Games

DEFF Research Database (Denmark)

Miltersen, Peter Bro; Sørensen, Troels Bjerre

2006-01-01

Koller, Megiddo and von Stengel showed how to efficiently compute minimax strategies for two-player extensive-form zero-sum games with imperfect information but perfect recall using linear programming and avoiding conversion to normal form. Koller and Pfeffer pointed out that the strategies...... obtained by the algorithm are not necessarily sequentially rational and that this deficiency is often problematic for the practical applications. We show how to remove this deficiency by modifying the linear programs constructed by Koller, Megiddo and von Stengel so that pairs of strategies forming...... a sequential equilibrium are computed. In particular, we show that a sequential equilibrium for a two-player zero-sum game with imperfect information but perfect recall can be found in polynomial time. In addition, the equilibrium we find is normal-form perfect. Our technique generalizes to general-sum games...

An approximation theory for nonlinear partial differential equations with applications to identification and control

Science.gov (United States)

Banks, H. T.; Kunisch, K.

1982-01-01

Approximation results from linear semigroup theory are used to develop a general framework for convergence of approximation schemes in parameter estimation and optimal control problems for nonlinear partial differential equations. These ideas are used to establish theoretical convergence results for parameter identification using modal (eigenfunction) approximation techniques. Results from numerical investigations of these schemes for both hyperbolic and parabolic systems are given.

SeGRAm - A practical and versatile tool for spacecraft trajectory optimization

Science.gov (United States)

Rishikof, Brian H.; Mccormick, Bernell R.; Pritchard, Robert E.; Sponaugle, Steven J.

1991-01-01

An implementation of the Sequential Gradient/Restoration Algorithm, SeGRAm, is presented along with selected examples. This spacecraft trajectory optimization and simulation program uses variational calculus to solve problems of spacecraft flying under the influence of one or more gravitational bodies. It produces a series of feasible solutions to problems involving a wide range of vehicles, environments and optimization functions, until an optimal solution is found. The examples included highlight the various capabilities of the program and emphasize in particular its versatility over a wide spectrum of applications from ascent to interplanetary trajectories.

Low-temperature excitations within the Bethe approximation

International Nuclear Information System (INIS)

Biazzo, I; Ramezanpour, A

2013-01-01

We propose the variational quantum cavity method to construct a minimal energy subspace of wavevectors that are used to obtain some upper bounds for the energy cost of the low-temperature excitations. Given a trial wavefunction we use the cavity method of statistical physics to estimate the Hamiltonian expectation and to find the optimal variational parameters in the subspace of wavevectors orthogonal to the lower-energy wavefunctions. To this end, we write the overlap between two wavefunctions within the Bethe approximation, which allows us to replace the global orthogonality constraint with some local constraints on the variational parameters. The method is applied to the transverse Ising model and different levels of approximations are compared with the exact numerical solutions for small systems. (paper)

Sequential Exposure of Bortezomib and Vorinostat is Synergistic in Multiple Myeloma Cells

Science.gov (United States)

Nanavati, Charvi; Mager, Donald E.

2018-01-01

Purpose To examine the combination of bortezomib and vorinostat in multiple myeloma cells (U266) and xenografts, and to assess the nature of their potential interactions with semi-mechanistic pharmacodynamic models and biomarkers. Methods U266 proliferation was examined for a range of bortezomib and vorinostat exposure times and concentrations (alone and in combination). A non-competitive interaction model was used with interaction parameters that reflect the nature of drug interactions after simultaneous and sequential exposures. p21 and cleaved PARP were measured using immunoblotting to assess critical biomarker dynamics. For xenografts, data were extracted from literature and modeled with a PK/PD model with an interaction parameter. Results Estimated model parameters for simultaneous in vitro and xenograft treatments suggested additive drug effects. The sequence of bortezomib preincubation for 24 hours, followed by vorinostat for 24 hours, resulted in an estimated interaction term significantly less than 1, suggesting synergistic effects. p21 and cleaved PARP were also up-regulated the most in this sequence. Conclusions Semi-mechanistic pharmacodynamic modeling suggests synergistic pharmacodynamic interactions for the sequential administration of bortezomib followed by vorinostat. Increased p21 and cleaved PARP expression can potentially explain mechanisms of their enhanced effects, which require further PK/PD systems analysis to suggest an optimal dosing regimen. PMID:28101809

Precise Sequential DNA Ligation on A Solid Substrate: Solid-Based Rapid Sequential Ligation of Multiple DNA Molecules

Science.gov (United States)

Takita, Eiji; Kohda, Katsunori; Tomatsu, Hajime; Hanano, Shigeru; Moriya, Kanami; Hosouchi, Tsutomu; Sakurai, Nozomu; Suzuki, Hideyuki; Shinmyo, Atsuhiko; Shibata, Daisuke

2013-01-01

Ligation, the joining of DNA fragments, is a fundamental procedure in molecular cloning and is indispensable to the production of genetically modified organisms that can be used for basic research, the applied biosciences, or both. Given that many genes cooperate in various pathways, incorporating multiple gene cassettes in tandem in a transgenic DNA construct for the purpose of genetic modification is often necessary when generating organisms that produce multiple foreign gene products. Here, we describe a novel method, designated PRESSO (precise sequential DNA ligation on a solid substrate), for the tandem ligation of multiple DNA fragments. We amplified donor DNA fragments with non-palindromic ends, and ligated the fragment to acceptor DNA fragments on solid beads. After the final donor DNA fragments, which included vector sequences, were joined to the construct that contained the array of fragments, the ligation product (the construct) was thereby released from the beads via digestion with a rare-cut meganuclease; the freed linear construct was circularized via an intra-molecular ligation. PRESSO allowed us to rapidly and efficiently join multiple genes in an optimized order and orientation. This method can overcome many technical challenges in functional genomics during the post-sequencing generation. PMID:23897972

The sequential structure of brain activation predicts skill.

Science.gov (United States)

Anderson, John R; Bothell, Daniel; Fincham, Jon M; Moon, Jungaa

2016-01-29

In an fMRI study, participants were trained to play a complex video game. They were scanned early and then again after substantial practice. While better players showed greater activation in one region (right dorsal striatum) their relative skill was better diagnosed by considering the sequential structure of whole brain activation. Using a cognitive model that played this game, we extracted a characterization of the mental states that are involved in playing a game and the statistical structure of the transitions among these states. There was a strong correspondence between this measure of sequential structure and the skill of different players. Using multi-voxel pattern analysis, it was possible to recognize, with relatively high accuracy, the cognitive states participants were in during particular scans. We used the sequential structure of these activation-recognized states to predict the skill of individual players. These findings indicate that important features about information-processing strategies can be identified from a model-based analysis of the sequential structure of brain activation. Copyright © 2015 Elsevier Ltd. All rights reserved.

A one-sided sequential test

Energy Technology Data Exchange (ETDEWEB)

Racz, A.; Lux, I. [Hungarian Academy of Sciences, Budapest (Hungary). Atomic Energy Research Inst.

1996-04-16

The applicability of the classical sequential probability ratio testing (SPRT) for early failure detection problems is limited by the fact that there is an extra time delay between the occurrence of the failure and its first recognition. Chien and Adams developed a method to minimize this time for the case when the problem can be formulated as testing the mean value of a Gaussian signal. In our paper we propose a procedure that can be applied for both mean and variance testing and that minimizes the time delay. The method is based on a special parametrization of the classical SPRT. The one-sided sequential tests (OSST) can reproduce the results of the Chien-Adams test when applied for mean values. (author).

Finite Gaussian Mixture Approximations to Analytically Intractable Density Kernels

DEFF Research Database (Denmark)

Khorunzhina, Natalia; Richard, Jean-Francois

The objective of the paper is that of constructing finite Gaussian mixture approximations to analytically intractable density kernels. The proposed method is adaptive in that terms are added one at the time and the mixture is fully re-optimized at each step using a distance measure that approxima...

A Multi-Objective Optimization Framework for Offshore Wind Farm Layouts and Electric Infrastructures

Directory of Open Access Journals (Sweden)

Silvio Rodrigues

2016-03-01

Full Text Available Current offshore wind farms (OWFs design processes are based on a sequential approach which does not guarantee system optimality because it oversimplifies the problem by discarding important interdependencies between design aspects. This article presents a framework to integrate, automate and optimize the design of OWF layouts and the respective electrical infrastructures. The proposed framework optimizes simultaneously different goals (e.g., annual energy delivered and investment cost which leads to efficient trade-offs during the design phase, e.g., reduction of wake losses vs collection system length. Furthermore, the proposed framework is independent of economic assumptions, meaning that no a priori values such as the interest rate or energy price, are needed. The proposed framework was applied to the Dutch Borssele areas I and II. A wide range of OWF layouts were obtained through the optimization framework. OWFs with similar energy production and investment cost as layouts designed with standard sequential strategies were obtained through the framework, meaning that the proposed framework has the capability to create different OWF layouts that would have been missed by the designers. In conclusion, the proposed multi-objective optimization framework represents a mind shift in design tools for OWFs which allows cost savings in the design and operation phases.

Sequential Bayesian geoacoustic inversion for mobile and compact source-receiver configuration.

Science.gov (United States)

Carrière, Olivier; Hermand, Jean-Pierre

2012-04-01

Geoacoustic characterization of wide areas through inversion requires easily deployable configurations including free-drifting platforms, underwater gliders and autonomous vehicles, typically performing repeated transmissions during their course. In this paper, the inverse problem is formulated as sequential Bayesian filtering to take advantage of repeated transmission measurements. Nonlinear Kalman filters implement a random-walk model for geometry and environment and an acoustic propagation code in the measurement model. Data from MREA/BP07 sea trials are tested consisting of multitone and frequency-modulated signals (bands: 0.25-0.8 and 0.8-1.6 kHz) received on a shallow vertical array of four hydrophones 5-m spaced drifting over 0.7-1.6 km range. Space- and time-coherent processing are applied to the respective signal types. Kalman filter outputs are compared to a sequence of global optimizations performed independently on each received signal. For both signal types, the sequential approach is more accurate but also more efficient. Due to frequency diversity, the processing of modulated signals produces a more stable tracking. Although an extended Kalman filter provides comparable estimates of the tracked parameters, the ensemble Kalman filter is necessary to properly assess uncertainty. In spite of mild range dependence and simplified bottom model, all tracked geoacoustic parameters are consistent with high-resolution seismic profiling, core logging P-wave velocity, and previous inversion results with fixed geometries.

Mining Emerging Sequential Patterns for Activity Recognition in Body Sensor Networks

DEFF Research Database (Denmark)

Gu, Tao; Wang, Liang; Chen, Hanhua

2010-01-01

Body Sensor Networks oer many applications in healthcare, well-being and entertainment. One of the emerging applications is recognizing activities of daily living. In this paper, we introduce a novel knowledge pattern named Emerging Sequential Pattern (ESP)|a sequential pattern that discovers...... signicant class dierences|to recognize both simple (i.e., sequential) and complex (i.e., interleaved and concurrent) activities. Based on ESPs, we build our complex activity models directly upon the sequential model to recognize both activity types. We conduct comprehensive empirical studies to evaluate...

Value-based decision making via sequential sampling with hierarchical competition and attentional modulation.

Science.gov (United States)

Colas, Jaron T

2017-01-01

In principle, formal dynamical models of decision making hold the potential to represent fundamental computations underpinning value-based (i.e., preferential) decisions in addition to perceptual decisions. Sequential-sampling models such as the race model and the drift-diffusion model that are grounded in simplicity, analytical tractability, and optimality remain popular, but some of their more recent counterparts have instead been designed with an aim for more feasibility as architectures to be implemented by actual neural systems. Connectionist models are proposed herein at an intermediate level of analysis that bridges mental phenomena and underlying neurophysiological mechanisms. Several such models drawing elements from the established race, drift-diffusion, feedforward-inhibition, divisive-normalization, and competing-accumulator models were tested with respect to fitting empirical data from human participants making choices between foods on the basis of hedonic value rather than a traditional perceptual attribute. Even when considering performance at emulating behavior alone, more neurally plausible models were set apart from more normative race or drift-diffusion models both quantitatively and qualitatively despite remaining parsimonious. To best capture the paradigm, a novel six-parameter computational model was formulated with features including hierarchical levels of competition via mutual inhibition as well as a static approximation of attentional modulation, which promotes "winner-take-all" processing. Moreover, a meta-analysis encompassing several related experiments validated the robustness of model-predicted trends in humans' value-based choices and concomitant reaction times. These findings have yet further implications for analysis of neurophysiological data in accordance with computational modeling, which is also discussed in this new light.