Sample records for algorithms

  1. Evolutionary algorithms

    Szöllösi, Tomáš


    The first part of this work deals with the optimization and evolutionary algorithms which are used as a tool to solve complex optimization problems. The discussed algorithms are Differential Evolution, Genetic Algorithm, Simulated Annealing and deterministic non-evolutionary algorithm Taboo Search.. Consequently the discussion is held on the issue of testing the optimization algorithms through the use of the test function gallery and comparison solution all algorithms on Travelling salesman p...

  2. Evolutionary algorithms

    Eremeev, Anton V.


    This manuscript contains an outline of lectures course "Evolutionary Algorithms" read by the author in Omsk State University n.a. F.M.Dostoevsky. The course covers Canonic Genetic Algorithm and various other genetic algorithms as well as evolutioanry algorithms in general. Some facts, such as the Rotation Property of crossover, the Schemata Theorem, GA performance as a local search and "almost surely" convergence of evolutionary algorithms are given with complete proofs. The text is in Russian.

  3. Algorithmic cryptanalysis

    Joux, Antoine


    Illustrating the power of algorithms, Algorithmic Cryptanalysis describes algorithmic methods with cryptographically relevant examples. Focusing on both private- and public-key cryptographic algorithms, it presents each algorithm either as a textual description, in pseudo-code, or in a C code program.Divided into three parts, the book begins with a short introduction to cryptography and a background chapter on elementary number theory and algebra. It then moves on to algorithms, with each chapter in this section dedicated to a single topic and often illustrated with simple cryptographic applic

  4. Total algorithms

    Tel, G.


    We define the notion of total algorithms for networks of processes. A total algorithm enforces that a "decision" is taken by a subset of the processes, and that participation of all processes is required to reach this decision. Total algorithms are an important building block in the design of distri

  5. Quantum algorithms

    Abrams, Daniel S.

    This thesis describes several new quantum algorithms. These include a polynomial time algorithm that uses a quantum fast Fourier transform to find eigenvalues and eigenvectors of a Hamiltonian operator, and that can be applied in cases (commonly found in ab initio physics and chemistry problems) for which all known classical algorithms require exponential time. Fast algorithms for simulating many body Fermi systems are also provided in both first and second quantized descriptions. An efficient quantum algorithm for anti-symmetrization is given as well as a detailed discussion of a simulation of the Hubbard model. In addition, quantum algorithms that calculate numerical integrals and various characteristics of stochastic processes are described. Two techniques are given, both of which obtain an exponential speed increase in comparison to the fastest known classical deterministic algorithms and a quadratic speed increase in comparison to classical Monte Carlo (probabilistic) methods. I derive a simpler and slightly faster version of Grover's mean algorithm, show how to apply quantum counting to the problem, develop some variations of these algorithms, and show how both (apparently distinct) approaches can be understood from the same unified framework. Finally, the relationship between physics and computation is explored in some more depth, and it is shown that computational complexity theory depends very sensitively on physical laws. In particular, it is shown that nonlinear quantum mechanics allows for the polynomial time solution of NP-complete and #P oracle problems. Using the Weinberg model as a simple example, the explicit construction of the necessary gates is derived from the underlying physics. Nonlinear quantum algorithms are also presented using Polchinski type nonlinearities which do not allow for superluminal communication. (Copies available exclusively from MIT Libraries, Rm. 14- 0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)

  6. Algorithmic Adventures

    Hromkovic, Juraj


    Explores the science of computing. This book starts with the development of computer science, algorithms and programming, and then explains and shows how to exploit the concepts of infinity, computability, computational complexity, nondeterminism and randomness.

  7. Combinatorial algorithms

    Hu, T C


    Newly enlarged, updated second edition of a valuable text presents algorithms for shortest paths, maximum flows, dynamic programming and backtracking. Also discusses binary trees, heuristic and near optimums, matrix multiplication, and NP-complete problems. 153 black-and-white illus. 23 tables.Newly enlarged, updated second edition of a valuable, widely used text presents algorithms for shortest paths, maximum flows, dynamic programming and backtracking. Also discussed are binary trees, heuristic and near optimums, matrix multiplication, and NP-complete problems. New to this edition: Chapter 9

  8. Autodriver algorithm

    Anna Bourmistrova; Milan Simic; Reza Hoseinnezhad; Jazar, Reza N.


    The autodriver algorithm is an intelligent method to eliminate the need of steering by a driver on a well-defined road. The proposed method performs best on a four-wheel steering (4WS) vehicle, though it is also applicable to two-wheel-steering (TWS) vehicles. The algorithm is based on coinciding the actual vehicle center of rotation and road center of curvature, by adjusting the kinematic center of rotation. The road center of curvature is assumed prior information for a given road, while th...

  9. Autodriver algorithm

    Anna Bourmistrova


    Full Text Available The autodriver algorithm is an intelligent method to eliminate the need of steering by a driver on a well-defined road. The proposed method performs best on a four-wheel steering (4WS vehicle, though it is also applicable to two-wheel-steering (TWS vehicles. The algorithm is based on coinciding the actual vehicle center of rotation and road center of curvature, by adjusting the kinematic center of rotation. The road center of curvature is assumed prior information for a given road, while the dynamic center of rotation is the output of dynamic equations of motion of the vehicle using steering angle and velocity measurements as inputs. We use kinematic condition of steering to set the steering angles in such a way that the kinematic center of rotation of the vehicle sits at a desired point. At low speeds the ideal and actual paths of the vehicle are very close. With increase of forward speed the road and tire characteristics, along with the motion dynamics of the vehicle cause the vehicle to turn about time-varying points. By adjusting the steering angles, our algorithm controls the dynamic turning center of the vehicle so that it coincides with the road curvature center, hence keeping the vehicle on a given road autonomously. The position and orientation errors are used as feedback signals in a closed loop control to adjust the steering angles. The application of the presented autodriver algorithm demonstrates reliable performance under different driving conditions.

  10. Algorithmic Self

    Markham, Annette

    This paper takes an actor network theory approach to explore some of the ways that algorithms co-construct identity and relational meaning in contemporary use of social media. Based on intensive interviews with participants as well as activity logging and data tracking, the author presents a richly...... layered set of accounts to help build our understanding of how individuals relate to their devices, search systems, and social network sites. This work extends critical analyses of the power of algorithms in implicating the social self by offering narrative accounts from multiple perspectives. It also...... contributes an innovative method for blending actor network theory with symbolic interaction to grapple with the complexity of everyday sensemaking practices within networked global information flows....

  11. Algorithmic chemistry

    Fontana, W.


    In this paper complex adaptive systems are defined by a self- referential loop in which objects encode functions that act back on these objects. A model for this loop is presented. It uses a simple recursive formal language, derived from the lambda-calculus, to provide a semantics that maps character strings into functions that manipulate symbols on strings. The interaction between two functions, or algorithms, is defined naturally within the language through function composition, and results in the production of a new function. An iterated map acting on sets of functions and a corresponding graph representation are defined. Their properties are useful to discuss the behavior of a fixed size ensemble of randomly interacting functions. This function gas'', or Turning gas'', is studied under various conditions, and evolves cooperative interaction patterns of considerable intricacy. These patterns adapt under the influence of perturbations consisting in the addition of new random functions to the system. Different organizations emerge depending on the availability of self-replicators.

  12. Evolutionary Graph Drawing Algorithms

    Huang Jing-wei; Wei Wen-fang


    In this paper, graph drawing algorithms based on genetic algorithms are designed for general undirected graphs and directed graphs. As being shown, graph drawing algorithms designed by genetic algorithms have the following advantages: the frames of the algorithms are unified, the method is simple, different algorithms may be attained by designing different objective functions, therefore enhance the reuse of the algorithms. Also, aesthetics or constrains may be added to satisfy different requirements.

  13. Super Greedy Type Algorithms

    Liu, Entao; Temlyakov, Vladimir N.


    We study greedy-type algorithms such that at a greedy step we pick several dictionary elements contrary to a single dictionary element in standard greedy-type algorithms. We call such greedy algorithms {\\it super greedy algorithms}. The idea of picking several elements at a greedy step of the algorithm is not new. Recently, we observed the following new phenomenon. For incoherent dictionaries these new type of algorithms (super greedy algorithms) provide the same (in the sense of order) upper...

  14. Algorithm Theory - SWAT 2006

    This book constitutes the refereed proceedings of the 10th Scandinavian Workshop on Algorithm Theory, SWAT 2006, held in Riga, Latvia, in July 2006. The 36 revised full papers presented together with 3 invited papers were carefully reviewed and selected from 154 submissions. The papers address all...... issues of theoretical algorithmics and applications in various fields including graph algorithms, computational geometry, scheduling, approximation algorithms, network algorithms, data storage and manipulation, combinatorics, sorting, searching, online algorithms, optimization, etc....

  15. Converting online algorithms to local computation algorithms

    Mansour, Yishay; Vardi, Shai; Xie, Ning


    We propose a general method for converting online algorithms to local computation algorithms by selecting a random permutation of the input, and simulating running the online algorithm. We bound the number of steps of the algorithm using a query tree, which models the dependencies between queries. We improve previous analyses of query trees on graphs of bounded degree, and extend the analysis to the cases where the degrees are distributed binomially, and to a special case of bipartite graphs. Using this method, we give a local computation algorithm for maximal matching in graphs of bounded degree, which runs in time and space O(log^3 n). We also show how to convert a large family of load balancing algorithms (related to balls and bins problems) to local computation algorithms. This gives several local load balancing algorithms which achieve the same approximation ratios as the online algorithms, but run in O(log n) time and space. Finally, we modify existing local computation algorithms for hypergraph 2-color...

  16. A hybrid bat algorithm:

    Fister, Iztok; Yang, Xin-She; Fister, Dušan


    Swarm intelligence is a very powerful technique to be used for optimization purposes. In this paper we present a new swarm intelligence algorithm, based on the bat algorithm. The Bat algorithm is hybridized with differential evolution strategies. Besides showing very promising results of the standard benchmark functions, this hybridization also significantly improves the original bat algorithm.

  17. The BR eigenvalue algorithm

    Geist, G.A. [Oak Ridge National Lab., TN (United States). Computer Science and Mathematics Div.; Howell, G.W. [Florida Inst. of Tech., Melbourne, FL (United States). Dept. of Applied Mathematics; Watkins, D.S. [Washington State Univ., Pullman, WA (United States). Dept. of Pure and Applied Mathematics


    The BR algorithm, a new method for calculating the eigenvalues of an upper Hessenberg matrix, is introduced. It is a bulge-chasing algorithm like the QR algorithm, but, unlike the QR algorithm, it is well adapted to computing the eigenvalues of the narrowband, nearly tridiagonal matrices generated by the look-ahead Lanczos process. This paper describes the BR algorithm and gives numerical evidence that it works well in conjunction with the Lanczos process. On the biggest problems run so far, the BR algorithm beats the QR algorithm by a factor of 30--60 in computing time and a factor of over 100 in matrix storage space.


    Priyamvada Paliwal#1, Meghna Sharma


    The DBSCAN algorithm can identify clusters in large spatial data sets by looking at the local density of database elements, using only one input parameter. This paper presents a comprehensive study of DBSCAN algorithm and the enhanced version of DBSCAN algorithm The salient of this paper to present enhanced DBSCAN algorithm with its implementation with the complexity and the difference between the older version of DBSCAN algorithm. And there are also additional features described with this al...

  19. Algorithmically specialized parallel computers

    Snyder, Lawrence; Gannon, Dennis B


    Algorithmically Specialized Parallel Computers focuses on the concept and characteristics of an algorithmically specialized computer.This book discusses the algorithmically specialized computers, algorithmic specialization using VLSI, and innovative architectures. The architectures and algorithms for digital signal, speech, and image processing and specialized architectures for numerical computations are also elaborated. Other topics include the model for analyzing generalized inter-processor, pipelined architecture for search tree maintenance, and specialized computer organization for raster

  20. Overview: Evolutionary Algorithms

    Bartz-Beielstein, Thomas (Dr.); Mersmann, Olaf


    Evolutionary algorithm (EA) is an umbrella term used to describe population-based stochastic direct search algorithms that in some sense mimic natural evolution. Prominent representatives of such algorithms are genetic algorithms, evolution strategies, evolutionary programming, and genetic programming. On the basis of the evolutionary cycle, similarities and differences between these algorithms are described. We briefly discuss how EAs can be adapted to work well in case of multiple objective...

  1. Overview: Evolutionary Algorithms

    Bartz-Beielstein, Thomas (Dr.); Branke, Jürgen; Mehnen, Jörn; Mersmann, Olaf


    Evolutionary algorithm (EA) is an umbrella term used to describe population-based stochastic direct search algorithms that in some sense mimic natural evolution. Prominent representatives of such algorithms are genetic algorithms, evolution strategies, evolutionary programming, and genetic programming. On the basis of the evolutionary cycle, similarities and differences between these algorithms are described. We briefly discuss how EAs can be adapted to work well in case of multiple objective...

  2. Quantum Computation and Algorithms

    It is now firmly established that quantum algorithms provide a substantial speedup over classical algorithms for a variety of problems, including the factorization of large numbers and the search for a marked element in an unsorted database. In this talk I will review the principles of quantum algorithms, the basic quantum gates and their operation. The combination of superposition and interference, that makes these algorithms efficient, will be discussed. In particular, Grover's search algorithm will be presented as an example. I will show that the time evolution of the amplitudes in Grover's algorithm can be found exactly using recursion equations, for any initial amplitude distribution

  3. New focused crawling algorithm

    Su Guiyang; Li Jianhua; Ma Yinghua; Li Shenghong; Song Juping


    Focused carawling is a new research approach of search engine. It restricts information retrieval and provides search service in specific topic area. Focused crawling search algorithm is a key technique of focused crawler which directly affects the search quality. This paper first introduces several traditional topic-specific crawling algorithms, then an inverse link based topic-specific crawling algorithm is put forward. Comparison experiment proves that this algorithm has a good performance in recall, obviously better than traditional Breadth-First and Shark-Search algorithms. The experiment also proves that this algorithm has a good precision.

  4. Symplectic algebraic dynamics algorithm


    Based on the algebraic dynamics solution of ordinary differential equations andintegration of  ,the symplectic algebraic dynamics algorithm sn is designed,which preserves the local symplectic geometric structure of a Hamiltonian systemand possesses the same precision of the na ve algebraic dynamics algorithm n.Computer experiments for the 4th order algorithms are made for five test modelsand the numerical results are compared with the conventional symplectic geometric algorithm,indicating that sn has higher precision,the algorithm-inducedphase shift of the conventional symplectic geometric algorithm can be reduced,and the dynamical fidelity can be improved by one order of magnitude.

  5. Competing Sudakov Veto Algorithms

    Kleiss, Ronald


    We present a way to analyze the distribution produced by a Monte Carlo algorithm. We perform these analyses on several versions of the Sudakov veto algorithm, adding a cutoff, a second variable and competition between emission channels. The analysis allows us to prove that multiple, seemingly different competition algorithms, including those that are currently implemented in most parton showers, lead to the same result. Finally, we test their performance and show that there are significantly faster alternatives to the commonly used algorithms.

  6. Grover search algorithm

    Borbely, Eva


    A quantum algorithm is a set of instructions for a quantum computer, however, unlike algorithms in classical computer science their results cannot be guaranteed. A quantum system can undergo two types of operation, measurement and quantum state transformation, operations themselves must be unitary (reversible). Most quantum algorithms involve a series of quantum state transformations followed by a measurement. Currently very few quantum algorithms are known and no general design methodology e...

  7. Accurate Finite Difference Algorithms

    Goodrich, John W.


    Two families of finite difference algorithms for computational aeroacoustics are presented and compared. All of the algorithms are single step explicit methods, they have the same order of accuracy in both space and time, with examples up to eleventh order, and they have multidimensional extensions. One of the algorithm families has spectral like high resolution. Propagation with high order and high resolution algorithms can produce accurate results after O(10(exp 6)) periods of propagation with eight grid points per wavelength.

  8. Approximate iterative algorithms

    Almudevar, Anthony Louis


    Iterative algorithms often rely on approximate evaluation techniques, which may include statistical estimation, computer simulation or functional approximation. This volume presents methods for the study of approximate iterative algorithms, providing tools for the derivation of error bounds and convergence rates, and for the optimal design of such algorithms. Techniques of functional analysis are used to derive analytical relationships between approximation methods and convergence properties for general classes of algorithms. This work provides the necessary background in functional analysis a

  9. Autonomous Star Tracker Algorithms

    Betto, Maurizio; Jørgensen, John Leif; Kilsgaard, Søren;


    Proposal, in response to an ESA R.f.P., to design algorithms for autonomous star tracker operations.The proposal also included the development of a star tracker breadboard to test the algorithms performances.......Proposal, in response to an ESA R.f.P., to design algorithms for autonomous star tracker operations.The proposal also included the development of a star tracker breadboard to test the algorithms performances....

  10. Hybridization of evolutionary algorithms

    Fister, Iztok; Mernik, Marjan; Brest, Janez


    Evolutionary algorithms are good general problem solver but suffer from a lack of domain specific knowledge. However, the problem specific knowledge can be added to evolutionary algorithms by hybridizing. Interestingly, all the elements of the evolutionary algorithms can be hybridized. In this chapter, the hybridization of the three elements of the evolutionary algorithms is discussed: the objective function, the survivor selection operator and the parameter settings. As an objective function...

  11. Graph Colouring Algorithms

    Husfeldt, Thore


    This chapter presents an introduction to graph colouring algorithms. The focus is on vertex-colouring algorithms that work for general classes of graphs with worst-case performance guarantees in a sequential model of computation. The presentation aims to demonstrate the breadth of available...... techniques and is organized by algorithmic paradigm....

  12. Nature-inspired optimization algorithms

    Yang, Xin-She


    Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning

  13. State Transition Algorithm

    Zhou, Xiaojun; Gui, Weihua


    In terms of the concepts of state and state transition, a new heuristic random search algorithm named state transition algorithm is proposed. For continuous function optimization problems, four special transformation operators called rotation, translation, expansion and axesion are designed. Adjusting measures of the transformations are mainly studied to keep the balance of exploration and exploitation. Convergence analysis is also discussed about the algorithm based on random search method. In the meanwhile, to strengthen the search ability in high dimensional space, communication strategy is introduced into the basic algorithm and intermittent exchange is presented to prevent premature convergence. Finally, experiments are carried out for the algorithms. With 10 common benchmark unconstrained continuous functions used to test the performance, the results show that state transition algorithms are promising algorithms due to their good global search capability and convergence property when compared with some ...

  14. Deductive Algorithmic Knowledge

    Pucella, Riccardo


    The framework of algorithmic knowledge assumes that agents use algorithms to compute the facts they explicitly know. In many cases of interest, a deductive system, rather than a particular algorithm, captures the formal reasoning used by the agents to compute what they explicitly know. We introduce a logic for reasoning about both implicit and explicit knowledge with the latter defined with respect to a deductive system formalizing a logical theory for agents. The highly structured nature of ...

  15. Fingerprint Feature Extraction Algorithm

    Mehala. G


    Full Text Available The goal of this paper is to design an efficient Fingerprint Feature Extraction (FFE algorithm to extract the fingerprint features for Automatic Fingerprint Identification Systems (AFIS. FFE algorithm, consists of two major subdivisions, Fingerprint image preprocessing, Fingerprint image postprocessing. A few of the challenges presented in an earlier are, consequently addressed, in this paper. The proposed algorithm is able to enhance the fingerprint image and also extracting true minutiae.

  16. Recursive forgetting algorithms

    Parkum, Jens; Poulsen, Niels Kjølstad; Holst, Jan


    In the first part of the paper, a general forgetting algorithm is formulated and analysed. It contains most existing forgetting schemes as special cases. Conditions are given ensuring that the basic convergence properties will hold. In the second part of the paper, the results are applied to a...... specific algorithm with selective forgetting. Here, the forgetting is non-uniform in time and space. The theoretical analysis is supported by a simulation example demonstrating the practical performance of this algorithm...

  17. Fingerprint Feature Extraction Algorithm

    Mehala. G


    The goal of this paper is to design an efficient Fingerprint Feature Extraction (FFE) algorithm to extract the fingerprint features for Automatic Fingerprint Identification Systems (AFIS). FFE algorithm, consists of two major subdivisions, Fingerprint image preprocessing, Fingerprint image postprocessing. A few of the challenges presented in an earlier are, consequently addressed, in this paper. The proposed algorithm is able to enhance the fingerprint image and also extractin...

  18. Integer factorization algorithms

    Bogataj, Polona


    The decomposition of a natural number into a product of prime numbers is called factorization. The main problem with factorization is the fact that there is no known efficient algorithm which would factor a given natural number n in polynomial time. The closest equivalent to such an algorithm is Shor's algorithm for quantum computers, which is still not practically applicable. The difficulties with factorization form the basis for modern cryptosystems—the most renowned among them is the RSA a...

  19. Introduction to Evolutionary Algorithms

    Yu, Xinjie


    Evolutionary algorithms (EAs) are becoming increasingly attractive for researchers from various disciplines, such as operations research, computer science, industrial engineering, electrical engineering, social science, economics, etc. This book presents an insightful, comprehensive, and up-to-date treatment of EAs, such as genetic algorithms, differential evolution, evolution strategy, constraint optimization, multimodal optimization, multiobjective optimization, combinatorial optimization, evolvable hardware, estimation of distribution algorithms, ant colony optimization, particle swarm opti

  20. Recursive forgetting algorithms

    Parkum, Jens; Poulsen, Niels Kjølstad; Holst, Jan


    In the first part of the paper, a general forgetting algorithm is formulated and analysed. It contains most existing forgetting schemes as special cases. Conditions are given ensuring that the basic convergence properties will hold. In the second part of the paper, the results are applied...... to a specific algorithm with selective forgetting. Here, the forgetting is non-uniform in time and space. The theoretical analysis is supported by a simulation example demonstrating the practical performance of this algorithm...

  1. Spectral Decomposition Algorithm (SDA)

    National Aeronautics and Space Administration — Spectral Decomposition Algorithm (SDA) is an unsupervised feature extraction technique similar to PCA that was developed to better distinguish spectral features in...

  2. Explaining algorithms using metaphors

    Forišek, Michal


    There is a significant difference between designing a new algorithm, proving its correctness, and teaching it to an audience. When teaching algorithms, the teacher's main goal should be to convey the underlying ideas and to help the students form correct mental models related to the algorithm. This process can often be facilitated by using suitable metaphors. This work provides a set of novel metaphors identified and developed as suitable tools for teaching many of the 'classic textbook' algorithms taught in undergraduate courses worldwide. Each chapter provides exercises and didactic notes fo

  3. Evolutionary Algorithm Definition

    Nada M.A. AL-Salami


    Full Text Available Problem statement: Most resent evolutionary algorithms work under weak theoretical basis and thus, they are computationally expensive. Approach: This study discussed the use of new evolutionary algorithm for automatic programming, based on theoretical definitions of program behaviors. Evolutionary process adapted fixed and self-organized input-output specification of the problem, to evolve good finite state machine that efficiently satisfies these specifications. Results: The proposed algorithm enhanced evolutionary process by simultaneously solving multi-parts from the same problem. Conclusion: The probability that the algorithm will converge to the optimal solution was highly enhanced when decomposing the main problem into multi-part.

  4. Parallel Algorithms for Normalization

    Boehm, Janko; Laplagne, Santiago; Pfister, Gerhard; Steenpass, Andreas; Steidel, Stefan


    Given a reduced affine algebra A over a perfect field K, we present parallel algorithms to compute the normalization \\bar{A} of A. Our starting point is the algorithm of Greuel, Laplagne, and Seelisch, which is an improvement of de Jong's algorithm. First, we propose to stratify the singular locus Sing(A) in a way which is compatible with normalization, apply a local version of the normalization algorithm at each stratum, and find \\bar{A} by putting the local results together. Second, in the case where K = Q is the field of rationals, we propose modular versions of the global and local algorithms. We have implemented our algorithms in the computer algebra system SINGULAR and compare their performance with that of other algorithms. In the case where K = Q, we also discuss the use of modular computations of Groebner bases, radicals and primary decompositions. We point out that in most examples, the new algorithms outperform the algorithm of Greuel, Laplagne, and Seelisch by far, even if we do not run them in pa...

  5. Fatigue Evaluation Algorithms: Review

    Passipoularidis, Vaggelis; Brøndsted, Povl

    rotor blade construction. Two versions of the algorithm, the one using single-step and the other using incremental application of each load cycle (in case of ply failure) are implemented and compared. Simulation results confirm the ability of the algorithm to take into account load sequence effects. In...

  6. Algorithmic Meta-Theorems

    Kreutzer, Stephan


    Algorithmic meta-theorems are general algorithmic results applying to a whole range of problems, rather than just to a single problem alone. They often have a "logical" and a "structural" component, that is they are results of the form: every computational problem that can be formalised in a given logic L can be solved efficiently on every class C of structures satisfying certain conditions. This paper gives a survey of algorithmic meta-theorems obtained in recent years and the methods used to prove them. As many meta-theorems use results from graph minor theory, we give a brief introduction to the theory developed by Robertson and Seymour for their proof of the graph minor theorem and state the main algorithmic consequences of this theory as far as they are needed in the theory of algorithmic meta-theorems.

  7. A New Modified Firefly Algorithm

    Medha Gupta


    Full Text Available Nature inspired meta-heuristic algorithms studies the emergent collective intelligence of groups of simple agents. Firefly Algorithm is one of the new such swarm-based metaheuristic algorithm inspired by the flashing behavior of fireflies. The algorithm was first proposed in 2008 and since then has been successfully used for solving various optimization problems. In this work, we intend to propose a new modified version of Firefly algorithm (MoFA and later its performance is compared with the standard firefly algorithm along with various other meta-heuristic algorithms. Numerical studies and results demonstrate that the proposed algorithm is superior to existing algorithms.

  8. Algorithms in Singular

    Hans Schonemann


    Full Text Available Some algorithms for singularity theory and algebraic geometry The use of Grobner basis computations for treating systems of polynomial equations has become an important tool in many areas. This paper introduces of the concept of standard bases (a generalization of Grobner bases and the application to some problems from algebraic geometry. The examples are presented as SINGULAR commands. A general introduction to Grobner bases can be found in the textbook [CLO], an introduction to syzygies in [E] and [St1]. SINGULAR is a computer algebra system for computing information about singularities, for use in algebraic geometry. The basic algorithms in SINGULAR are several variants of a general standard basis algorithm for general monomial orderings (see [GG]. This includes wellorderings (Buchberger algorithm ([B1], [B2] and tangent cone orderings (Mora algorithm ([M1], [MPT] as special cases: It is able to work with non-homogeneous and homogeneous input and also to compute in the localization of the polynomial ring in 0. Recent versions include algorithms to factorize polynomials and a factorizing Grobner basis algorithm. For a complete description of SINGULAR see [Si].

  9. Comparison Study for Clonal Selection Algorithm and Genetic Algorithm

    Ezgi Deniz Ulker; Sadık Ulker


    Two metaheuristic algorithms namely Artificial Immune Systems (AIS) and Genetic Algorithms are classified as computational systems inspired by theoretical immunology and genetics mechanisms. In this work we examine the comparative performances of two algorithms. A special selection algorithm, Clonal Selection Algorithm (CLONALG), which is a subset of Artificial Immune Systems, and Genetic Algorithms are tested with certain benchmark functions. It is shown that depending on type of a function ...

  10. Diagnostic Algorithm Benchmarking

    Poll, Scott


    A poster for the NASA Aviation Safety Program Annual Technical Meeting. It describes empirical benchmarking on diagnostic algorithms using data from the ADAPT Electrical Power System testbed and a diagnostic software framework.

  11. Unsupervised learning algorithms

    Aydin, Kemal


    This book summarizes the state-of-the-art in unsupervised learning. The contributors discuss how with the proliferation of massive amounts of unlabeled data, unsupervised learning algorithms, which can automatically discover interesting and useful patterns in such data, have gained popularity among researchers and practitioners. The authors outline how these algorithms have found numerous applications including pattern recognition, market basket analysis, web mining, social network analysis, information retrieval, recommender systems, market research, intrusion detection, and fraud detection. They present how the difficulty of developing theoretically sound approaches that are amenable to objective evaluation have resulted in the proposal of numerous unsupervised learning algorithms over the past half-century. The intended audience includes researchers and practitioners who are increasingly using unsupervised learning algorithms to analyze their data. Topics of interest include anomaly detection, clustering,...

  12. A Simple Calculator Algorithm.

    Cook, Lyle; McWilliam, James


    The problem of finding cube roots when limited to a calculator with only square root capability is discussed. An algorithm is demonstrated and explained which should always produce a good approximation within a few iterations. (MP)

  13. δf Algorithm

    The δf Algorithm is a low noise particle code algorithm. The perturbation of the distribution function (δf) away from a large equilibrium is evolved rather than the total distribution function. ''Particles'' in the code are actually Lagrangian markers at which the value of the distribution functalon is known. The magnitude of the numerical noise is characteristic of the size of the perturbation rather than the equilibrium, and scales roughly as the inverse of the number of particles. In this paper. the algorithm is described. and conserved energies are derived for linear and nonlinear sets of equations. Two different forms of the energy principle test separately adequate resolution in time and space and adequacy of the number of simulation particles. A semi-implicit time step method is described which allows violation of the Courant condition. Low noise capabilities of a linear code using the algorithm are demonstrated

  14. A more robust boosting algorithm

    Freund, Yoav


    We present a new boosting algorithm, motivated by the large margins theory for boosting. We give experimental evidence that the new algorithm is significantly more robust against label noise than existing boosting algorithm.

  15. Quantum algorithmic information theory

    Svozil, Karl


    The agenda of quantum algorithmic information theory, ordered `top-down,' is the quantum halting amplitude, followed by the quantum algorithmic information content, which in turn requires the theory of quantum computation. The fundamental atoms processed by quantum computation are the quantum bits which are dealt with in quantum information theory. The theory of quantum computation will be based upon a model of universal quantum computer whose elementary unit is a two-port interferometer capa...

  16. Algorithmic Problem Complexity

    Burgin, Mark


    People solve different problems and know that some of them are simple, some are complex and some insoluble. The main goal of this work is to develop a mathematical theory of algorithmic complexity for problems. This theory is aimed at determination of computer abilities in solving different problems and estimation of resources that computers need to do this. Here we build the part of this theory related to static measures of algorithms. At first, we consider problems for finite words and stud...

  17. The proximal distance algorithm

    Lange, Kenneth; Keys, Kevin L.


    The MM principle is a device for creating optimization algorithms satisfying the ascent or descent property. The current survey emphasizes the role of the MM principle in nonlinear programming. For smooth functions, one can construct an adaptive interior point method based on scaled Bregmann barriers. This algorithm does not follow the central path. For convex programming subject to nonsmooth constraints, one can combine an exact penalty method with distance majorization to create versatile a...

  18. Incremental algorithms on lists

    Jeuring, J.T.


    Incremental computations can improve the performance of interactive programs such as spreadsheet programs, program development environments, text editors, etc. Incremental algorithms describe how to compute a required value depending on the input, after the input has been edited. By considering the possible different edit actions on the data type lists, the basic data type used in spreadsheet programs and text editors, we define incremental algorithms on lists. Some theory for the constructio...

  19. Multicanonical Cluster Algorithm

    Rummukainen, K.


    In this talk I present a multicanonical hybrid-like two-step algorithm, which consists of a microcanonical spin system update with demons, and a multicanonical demon refresh. The demons act as a buffer between the multicanonical heat bath and the spin system, allowing for a large variety of update schemes. In this work the cluster algorithm is demonstrated with the 2-dimensional 7-state Potts model, using volumes up to $128^2$.

  20. BESⅢ track fitting algorithm

    WANG Ji-Ke; MAO Ze-Pu; BIAN Jian-Ming; CAO Guo-Fu; CAO Xue-Xiang; CHEN Shen-Jian; DENG Zi-Yan; FU Cheng-Dong; GAO Yuan-Ning; HE Kang-Lin; HE Miao; HUA Chun-Fei; HUANG Bin; HUANG Xing-Tao; JI Xiao-Sin; LI Fei; LI Hai-Bo; LI Wei-Dong; LIANG Yu-Tie; LIU Chun-Xiu; LIU Huai-Min; LIU Suo; LIU Ying-Jie; MA Qiu-Mei; MA Xiang; MAO Ya-Jun; MO Xiao-Hu; PAN Ming-Hua; PANG Cai-Ying; PING Rong-Gang; QIN Ya-Hong; QIU Jin-Fa; SUN Sheng-Sen; SUN Yong-Zhao; WANG Liang-Liang; WEN Shuo-Pin; WU Ling-Hui; XIE Yu-Guang; XU Min; YAN Liang; YOU Zheng-Yun; YUAN Chang-Zheng; YUAN Ye; ZHANG Bing-Yun; ZHANG Chang-Chun; ZHANG Jian-Yong; ZHANG Xue-Yao; ZHANG Yao; ZHENG Yang-Heng; ZHU Ke-Jun; ZHU Yong-Sheng; ZHU Zhi-Li; ZOU Jia-Heng


    A track fitting algorithm based on the Kalman filter method has been developed for BESⅢ of BEPCⅡ.The effects of multiple scattering and energy loss when the charged particles go through the detector,non-uniformity of magnetic field (NUMF) and wire sag, etc., have been carefully handled.This algorithm works well and the performance satisfies the physical requirements tested by the simulation data.

  1. Local approximate inference algorithms

    Jung, Kyomin; Shah, Devavrat


    We present a new local approximation algorithm for computing Maximum a Posteriori (MAP) and log-partition function for arbitrary exponential family distribution represented by a finite-valued pair-wise Markov random field (MRF), say $G$. Our algorithm is based on decomposition of $G$ into {\\em appropriately} chosen small components; then computing estimates locally in each of these components and then producing a {\\em good} global solution. We show that if the underlying graph $G$ either excl...

  2. Online Genetic Algorithms

    Milani, Alfredo


    This paper present a technique based on genetic algorithms for generating online adaptive services. Online adaptive systems provide flexible services to a mass of clients/users for maximising some system goals, they dynamically adapt the form and the content of the issued services while the population of clients evolve over time. The idea of online genetic algorithms (online GAs) is to use the online clients response behaviour as a fitness function in order to produce the next ...

  3. The Motif Tracking Algorithm


    The search for patterns or motifs in data represents a problem area of key interest to finance and economic researchers. In this paper, we introduce the motif tracking algorithm (MTA), a novel immune inspired (IS) pattern identification tool that is able to identify unknown motifs of a non specified length which repeat within time series data. The power of the algorithm comes from the fact that it uses a small number of parameters with minimal assumptions regarding the data being examined or the underlying motifs. Our interest lies in applying the algorithm to financial time series data to identify unknown patterns that exist. The algorithm is tested using three separate data sets. Particular suitability to financial data is shown by applying it to oil price data. In all cases, the algorithm identifies the presence of a motif population in a fast and efficient manner due to the utilization of an intuitive symbolic representation.The resulting population of motifs is shown to have considerable potential value for other applications such as forecasting and algorithm seeding.

  4. Algebraic dynamics algorithm:Numerical comparison with Runge-Kutta algorithm and symplectic geometric algorithm


    Based on the exact analytical solution of ordinary differential equations, a truncation of the Taylor series of the exact solution to the Nth order leads to the Nth order algebraic dynamics algorithm. A detailed numerical comparison is presented with Runge-Kutta algorithm and symplectic geometric algorithm for 12 test models. The results show that the algebraic dynamics algorithm can better preserve both geometrical and dynamical fidelity of a dynamical system at a controllable precision, and it can solve the problem of algorithm-induced dissipation for the Runge-Kutta algorithm and the problem of algorithm-induced phase shift for the symplectic geometric algorithm.

  5. Algebraic dynamics algorithm: Numerical comparison with Runge-Kutta algorithm and symplectic geometric algorithm

    WANG ShunJin; ZHANG Hua


    Based on the exact analytical solution of ordinary differential equations,a truncation of the Taylor series of the exact solution to the Nth order leads to the Nth order algebraic dynamics algorithm.A detailed numerical comparison is presented with Runge-Kutta algorithm and symplectic geometric algorithm for 12 test models.The results show that the algebraic dynamics algorithm can better preserve both geometrical and dynamical fidelity of a dynamical system at a controllable precision,and it can solve the problem of algorithm-induced dissipation for the Runge-Kutta algorithm and the problem of algorithm-induced phase shift for the symplectic geometric algorithm.

  6. A Parallel Butterfly Algorithm

    Poulson, Jack


    The butterfly algorithm is a fast algorithm which approximately evaluates a discrete analogue of the integral transform (Equation Presented.) at large numbers of target points when the kernel, K(x, y), is approximately low-rank when restricted to subdomains satisfying a certain simple geometric condition. In d dimensions with O(Nd) quasi-uniformly distributed source and target points, when each appropriate submatrix of K is approximately rank-r, the running time of the algorithm is at most O(r2Nd logN). A parallelization of the butterfly algorithm is introduced which, assuming a message latency of α and per-process inverse bandwidth of β, executes in at most (Equation Presented.) time using p processes. This parallel algorithm was then instantiated in the form of the open-source DistButterfly library for the special case where K(x, y) = exp(iΦ(x, y)), where Φ(x, y) is a black-box, sufficiently smooth, real-valued phase function. Experiments on Blue Gene/Q demonstrate impressive strong-scaling results for important classes of phase functions. Using quasi-uniform sources, hyperbolic Radon transforms, and an analogue of a three-dimensional generalized Radon transform were, respectively, observed to strong-scale from 1-node/16-cores up to 1024-nodes/16,384-cores with greater than 90% and 82% efficiency, respectively. © 2014 Society for Industrial and Applied Mathematics.

  7. The Motif Tracking Algorithm

    Wilson, William; Aickelin, Uwe; 10.1007/s11633.008.0032.0


    The search for patterns or motifs in data represents a problem area of key interest to finance and economic researchers. In this paper we introduce the Motif Tracking Algorithm, a novel immune inspired pattern identification tool that is able to identify unknown motifs of a non specified length which repeat within time series data. The power of the algorithm comes from the fact that it uses a small number of parameters with minimal assumptions regarding the data being examined or the underlying motifs. Our interest lies in applying the algorithm to financial time series data to identify unknown patterns that exist. The algorithm is tested using three separate data sets. Particular suitability to financial data is shown by applying it to oil price data. In all cases the algorithm identifies the presence of a motif population in a fast and efficient manner due to the utilisation of an intuitive symbolic representation. The resulting population of motifs is shown to have considerable potential value for other ap...

  8. A Subspace Algorithm

    Vissing, S.; Hededal, O.

    An algorithm is presented for computing the m smallest eigenvalues and corresponding eigenvectors of the generalized eigenvalue problem (A - λB)Φ = 0 where A and B are real n x n symmetric matrices. In an iteration scheme the matrices A and B are projected simultaneously onto an m-dimensional sub......An algorithm is presented for computing the m smallest eigenvalues and corresponding eigenvectors of the generalized eigenvalue problem (A - λB)Φ = 0 where A and B are real n x n symmetric matrices. In an iteration scheme the matrices A and B are projected simultaneously onto an m......-dimensional subspace in order to establish and solve a symmetric generalized eigenvalue problem in the subspace. The algorithm is described in pseudo code and implemented in the C programming language for lower triangular matrices A and B. The implementation includes procedures for selecting initial iteration vectors...

  9. Handbook of Memetic Algorithms

    Cotta, Carlos; Moscato, Pablo


    Memetic Algorithms (MAs) are computational intelligence structures combining multiple and various operators in order to address optimization problems.  The combination and interaction amongst operators evolves and promotes the diffusion of the most successful units and generates an algorithmic behavior which can handle complex objective functions and hard fitness landscapes.   “Handbook of Memetic Algorithms” organizes, in a structured way, all the the most important results in the field of MAs since their earliest definition until now.  A broad review including various algorithmic solutions as well as successful applications is included in this book. Each class of optimization problems, such as constrained optimization, multi-objective optimization, continuous vs combinatorial problems, uncertainties, are analysed separately and, for each problem,  memetic recipes for tackling the difficulties are given with some successful examples. Although this book contains chapters written by multiple authors, ...

  10. The Retina Algorithm

    CERN. Geneva; PUNZI, Giovanni


    Charge particle reconstruction is one of the most demanding computational tasks found in HEP, and it becomes increasingly important to perform it in real time. We envision that HEP would greatly benefit from achieving a long-term goal of making track reconstruction happen transparently as part of the detector readout ("detector-embedded tracking"). We describe here a track-reconstruction approach based on a massively parallel pattern-recognition algorithm, inspired by studies of the processing of visual images by the brain as it happens in nature ('RETINA algorithm'). It turns out that high-quality tracking in large HEP detectors is possible with very small latencies, when this algorithm is implemented in specialized processors, based on current state-of-the-art, high-speed/high-bandwidth digital devices.

  11. Temperature Corrected Bootstrap Algorithm

    Comiso, Joey C.; Zwally, H. Jay


    A temperature corrected Bootstrap Algorithm has been developed using Nimbus-7 Scanning Multichannel Microwave Radiometer data in preparation to the upcoming AMSR instrument aboard ADEOS and EOS-PM. The procedure first calculates the effective surface emissivity using emissivities of ice and water at 6 GHz and a mixing formulation that utilizes ice concentrations derived using the current Bootstrap algorithm but using brightness temperatures from 6 GHz and 37 GHz channels. These effective emissivities are then used to calculate surface ice which in turn are used to convert the 18 GHz and 37 GHz brightness temperatures to emissivities. Ice concentrations are then derived using the same technique as with the Bootstrap algorithm but using emissivities instead of brightness temperatures. The results show significant improvement in the area where ice temperature is expected to vary considerably such as near the continental areas in the Antarctic, where the ice temperature is colder than average, and in marginal ice zones.

  12. Tiled QR factorization algorithms

    Bouwmeester, Henricus; Langou, Julien; Robert, Yves


    This work revisits existing algorithms for the QR factorization of rectangular matrices composed of p-by-q tiles, where p >= q. Within this framework, we study the critical paths and performance of algorithms such as Sameh and Kuck, Modi and Clarke, Greedy, and those found within PLASMA. Although neither Modi and Clarke nor Greedy is optimal, both are shown to be asymptotically optimal for all matrices of size p = q^2 f(q), where f is any function such that \\lim_{+\\infty} f= 0. This novel and important complexity result applies to all matrices where p and q are proportional, p = \\lambda q, with \\lambda >= 1, thereby encompassing many important situations in practice (least squares). We provide an extensive set of experiments that show the superiority of the new algorithms for tall matrices.

  13. Algorithms for Global Positioning

    Borre, Kai; Strang, Gilbert

    The emergence of satellite technology has changed the lives of millions of people. In particular, GPS has brought an unprecedented level of accuracy to the field of geodesy. This text is a guide to the algorithms and mathematical principles that account for the success of GPS technology and repla......The emergence of satellite technology has changed the lives of millions of people. In particular, GPS has brought an unprecedented level of accuracy to the field of geodesy. This text is a guide to the algorithms and mathematical principles that account for the success of GPS technology....... At the heart of the matter are the positioning algorithms on which GPS technology relies, the discussion of which will affirm the mathematical contents of the previous chapters. Numerous ready-to-use MATLAB codes are included for the reader. This comprehensive guide will be invaluable for engineers...

  14. Fusion of motion segmentation algorithms

    Ellis, Anna-Louise


    Many algorithms have been developed to achieve motion segmentation for video surveillance. The algorithms produce varying performances under the infinite amount of changing conditions. It has been recognised that individually these algorithms have useful properties. Fusing the statistical result of these algorithms is investigated, with robust motion segmentation in mind.

  15. Quantum CPU and Quantum Algorithm

    Wang, An Min


    Making use of an universal quantum network -- QCPU proposed by me\\upcite{My1}, it is obtained that the whole quantum network which can implement some the known quantum algorithms including Deutsch algorithm, quantum Fourier transformation, Shor's algorithm and Grover's algorithm.

  16. An Improved Algorithm of Apriori

    Liao, Binhua

    This paper puts forward a kind of improved algorithm after analyzing the classical Apriori algorithm. Through scanning database only once, all transactions are transformed into components of a two-dimensional array. The algorithm becomes more practical by introducing weight. Moreover, the unnecessary data are deleted in time, and the joining and pruning steps become simple. This, therefore, improves the efficiency of Apriori algorithm.

  17. Yet Another Efficient Unification Algorithm

    Suciu, Alin


    The unification algorithm is at the core of the logic programming paradigm, the first unification algorithm being developed by Robinson [5]. More efficient algorithms were developed later [3] and I introduce here yet another efficient unification algorithm centered on a specific data structure, called the Unification Table.

  18. A Generalized Jacobi Algorithm

    Vissing, S.; Krenk, S.

    An algorithm is developed for the generalized eigenvalue problem (A - λB)φ = O where A and B are real symmetric matrices. The matrices A and B are diagonalized simultaneously by a series of generalized Jacobi transformations and all eigenvalues and eigenvectors are obtained. A criterion expressed...... in terms of the transformation parameters is used to omit transformations leading to very small changes. The algorithm is described in pseudo code for lower triangular matrices A and B and implemented in the programming Language C....

  19. Fast Local Computation Algorithms

    Rubinfeld, Ronitt; Tamir, Gil; Vardi, Shai; Xie, Ning


    For input $x$, let $F(x)$ denote the set of outputs that are the "legal" answers for a computational problem $F$. Suppose $x$ and members of $F(x)$ are so large that there is not time to read them in their entirety. We propose a model of {\\em local computation algorithms} which for a given input $x$, support queries by a user to values of specified locations $y_i$ in a legal output $y \\in F(x)$. When more than one legal output $y$ exists for a given $x$, the local computation algorithm should...

  20. Algorithms for Reinforcement Learning

    Szepesvari, Csaba


    Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms'

  1. Wireless communications algorithmic techniques

    Vitetta, Giorgio; Colavolpe, Giulio; Pancaldi, Fabrizio; Martin, Philippa A


    This book introduces the theoretical elements at the basis of various classes of algorithms commonly employed in the physical layer (and, in part, in MAC layer) of wireless communications systems. It focuses on single user systems, so ignoring multiple access techniques. Moreover, emphasis is put on single-input single-output (SISO) systems, although some relevant topics about multiple-input multiple-output (MIMO) systems are also illustrated.Comprehensive wireless specific guide to algorithmic techniquesProvides a detailed analysis of channel equalization and channel coding for wi

  2. Tiled QR factorization algorithms

    Bouwmeester, Henricus; Jacquelin, Mathias; Langou, Julien; Robert, Yves


    This work revisits existing algorithms for the QR factorization of rectangular matrices composed of p-by-q tiles, where p >= q. Within this framework, we study the critical paths and performance of algorithms such as Sameh and Kuck, Modi and Clarke, Greedy, and those found within PLASMA. Although neither Modi and Clarke nor Greedy is optimal, both are shown to be asymptotically optimal for all matrices of size p = q^2 f(q), where f is any function such that \\lim_{+\\infty} f= 0. This novel and...

  3. Algorithm for structure constants

    Paiva, F M


    In a $n$-dimensional Lie algebra, random numerical values are assigned by computer to $n(n-1)$ especially selected structure constants. An algorithm is then created, which calculates without ambiguity the remaining constants, obeying the Jacobi conditions. Differently from others, this algorithm is suitable even for poor personal computer. ------------- En $n$-dimensia algebro de Lie, hazardaj numeraj valoroj estas asignitaj per komputilo al $n(n-1)$ speciale elektitaj konstantoj de strukturo. Tiam algoritmo estas kreita, kalkulante senambigue la ceterajn konstantojn, obeante kondicxojn de Jacobi. Malsimile al aliaj algoritmoj, tiu cxi tauxgas ecx por malpotenca komputilo.

  4. Parallel Algorithms and Patterns

    Robey, Robert W. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)


    This is a powerpoint presentation on parallel algorithms and patterns. A parallel algorithm is a well-defined, step-by-step computational procedure that emphasizes concurrency to solve a problem. Examples of problems include: Sorting, searching, optimization, matrix operations. A parallel pattern is a computational step in a sequence of independent, potentially concurrent operations that occurs in diverse scenarios with some frequency. Examples are: Reductions, prefix scans, ghost cell updates. We only touch on parallel patterns in this presentation. It really deserves its own detailed discussion which Gabe Rockefeller would like to develop.

  5. New Effective Multithreaded Matching Algorithms

    Manne, Fredrik; Halappanavar, Mahantesh


    Matching is an important combinatorial problem with a number of applications in areas such as community detection, sparse linear algebra, and network alignment. Since computing optimal matchings can be very time consuming, several fast approximation algorithms, both sequential and parallel, have been suggested. Common to the algorithms giving the best solutions is that they tend to be sequential by nature, while algorithms more suitable for parallel computation give solutions of less quality. We present a new simple 1 2 -approximation algorithm for the weighted matching problem. This algorithm is both faster than any other suggested sequential 1 2 -approximation algorithm on almost all inputs and also scales better than previous multithreaded algorithms. We further extend this to a general scalable multithreaded algorithm that computes matchings of weight comparable with the best sequential algorithms. The performance of the suggested algorithms is documented through extensive experiments on different multithreaded architectures.

  6. An Ordering Linear Unification Algorithm



    In this paper,we present an ordering linear unification algorithm(OLU).A new idea on substituteion of the binding terms is introduced to the algorithm,which is able to overcome some drawbacks of other algorithms,e.g.,MM algorithm[1],RG1 and RG2 algorithms[2],Particularly,if we use the directed eyclie graphs,the algoritm needs not check the binding order,then the OLU algorithm can also be aplied to the infinite tree data struceture,and a higher efficiency can be expected.The paper focuses upon the discussion of OLU algorithm and a partial order structure with respect to the unification algorithm.This algorithm has been implemented in the GKD-PROLOG/VAX 780 interpreting system.Experimental results have shown that the algorithm is very simple and efficient.

  7. A Panoply of Quantum Algorithms

    Furrow, Bartholomew


    We create a variety of new quantum algorithms that use Grover's algorithm and similar techniques to give polynomial speedups over their classical counterparts. We begin by introducing a set of tools that carefully minimize the impact of errors on running time; those tools provide us with speedups to already-published quantum algorithms, such as improving Durr, Heiligman, Hoyer and Mhalla's algorithm for single-source shortest paths [quant-ph/0401091] by a factor of lg N. The algorithms we con...

  8. Fuzzy Priority CPU Scheduling Algorithm

    Bashir Alam; M.N. Doja; R. Biswas; Mansaf Alam


    There are several CPU scheduling algorithms like FCFS, SRTN,RR , priority etc. Scheduling decision of these algorithms are based on parameters which are assumed to be crisp. However, in many circumstances these parameters are vague. The vagueness of these parameters suggests that scheduler should use fuzzy logic in scheduling the jobs. A fuzzy priority CPU scheduling algorithm has been proposed. This proposed algorithm improves the priority based CPU scheduling algorithm as obvious from simul...

  9. The Middle Pivot Element Algorithm

    Anchala Kumari; Soubhik Chakraborty


    This paper is an improvement over the previous work on New Sorting Algorithm first proposed by Sundararajan and Chakraborty (2007). Here we have taken the pivot element as the middle element of the array. We call this improved version Middle Pivot Element Algorithm (MPA) and it is found that MPA is much faster than the two algorithms RPA (Random Pivot element Algorithm) and FPA (First Pivot element Algorithm) in which the pivot element was selected either randomly or as the first element, res...

  10. Graphs Theory and Algorithms

    Thulasiraman, K


    This adaptation of an earlier work by the authors is a graduate text and professional reference on the fundamentals of graph theory. It covers the theory of graphs, its applications to computer networks and the theory of graph algorithms. Also includes exercises and an updated bibliography.

  11. de Casteljau's Algorithm Revisited

    Gravesen, Jens


    It is demonstrated how all the basic properties of Bezier curves can be derived swiftly and efficiently without any reference to the Bernstein polynomials and essentially with only geometric arguments. This is achieved by viewing one step in de Casteljau's algorithm as an operator (the de Casteljau...

  12. Multisource Algorithmic Information Theory

    Shen, Alexander


    Multisource information theory is well known in Shannon setting. It studies the possibilities of information transfer through a network with limited capacities. Similar questions could be studied for algorithmic information theory and provide a framework for several known results and interesting questions.

  13. Sorting Algorithms with Restrictions

    Aslanyan, Hakob


    Sorting is one of the most used and well investigated algorithmic problem [1]. Traditional postulation supposes the sorting data archived, and the elementary operation as comparisons of two numbers. In a view of appearance of new processors and applied problems with data streams, sorting changed its face. This changes and generalizations are the subject of investigation in the research below.

  14. de Casteljau's Algorithm Revisited

    Gravesen, Jens

    It is demonstrated how all the basic properties of Bezier curves can be derived swiftly and efficiently without any reference to the Bernstein polynomials and essentially with only geometric arguments. This is achieved by viewing one step in de Casteljau's algorithm as an operator (the de Casteljau...

  15. The Lure of Algorithms

    Drake, Michael


    One debate that periodically arises in mathematics education is the issue of how to teach calculation more effectively. "Modern" approaches seem to initially favour mental calculation, informal methods, and the development of understanding before introducing written forms, while traditionalists tend to champion particular algorithms. The debate is…

  16. The Xmath Integration Algorithm

    Bringslid, Odd


    The projects Xmath (Bringslid and Canessa, 2002) and dMath (Bringslid, de la Villa and Rodriguez, 2007) were supported by the European Commission in the so called Minerva Action (Xmath) and The Leonardo da Vinci programme (dMath). The Xmath eBook (Bringslid, 2006) includes algorithms into a wide range of undergraduate mathematical issues embedded…

  17. General cardinality genetic algorithms

    Koehler; Bhattacharyya; Vose


    A complete generalization of the Vose genetic algorithm model from the binary to higher cardinality case is provided. Boolean AND and EXCLUSIVE-OR operators are replaced by multiplication and addition over rings of integers. Walsh matrices are generalized with finite Fourier transforms for higher cardinality usage. Comparison of results to the binary case are provided. PMID:10021767

  18. Modular Regularization Algorithms

    Jacobsen, Michael


    The class of linear ill-posed problems is introduced along with a range of standard numerical tools and basic concepts from linear algebra, statistics and optimization. Known algorithms for solving linear inverse ill-posed problems are analyzed to determine how they can be decomposed into indepen......The class of linear ill-posed problems is introduced along with a range of standard numerical tools and basic concepts from linear algebra, statistics and optimization. Known algorithms for solving linear inverse ill-posed problems are analyzed to determine how they can be decomposed...... into independent modules. These modules are then combined to form new regularization algorithms with other properties than those we started out with. Several variations are tested using the Matlab toolbox MOORe Tools created in connection with this thesis. Object oriented programming techniques are explained...... and used to set up the illposed problems in the toolbox. Hereby, we are able to write regularization algorithms that automatically exploit structure in the ill-posed problem without being rewritten explicitly. We explain how to implement a stopping criteria for a parameter choice method based upon...

  19. Python algorithms mastering basic algorithms in the Python language

    Hetland, Magnus Lie


    Python Algorithms, Second Edition explains the Python approach to algorithm analysis and design. Written by Magnus Lie Hetland, author of Beginning Python, this book is sharply focused on classical algorithms, but it also gives a solid understanding of fundamental algorithmic problem-solving techniques. The book deals with some of the most important and challenging areas of programming and computer science in a highly readable manner. It covers both algorithmic theory and programming practice, demonstrating how theory is reflected in real Python programs. Well-known algorithms and data struc

  20. Benchmarking monthly homogenization algorithms

    V. K. C. Venema


    Full Text Available The COST (European Cooperation in Science and Technology Action ES0601: Advances in homogenization methods of climate series: an integrated approach (HOME has executed a blind intercomparison and validation study for monthly homogenization algorithms. Time series of monthly temperature and precipitation were evaluated because of their importance for climate studies and because they represent two important types of statistics (additive and multiplicative. The algorithms were validated against a realistic benchmark dataset. The benchmark contains real inhomogeneous data as well as simulated data with inserted inhomogeneities. Random break-type inhomogeneities were added to the simulated datasets modeled as a Poisson process with normally distributed breakpoint sizes. To approximate real world conditions, breaks were introduced that occur simultaneously in multiple station series within a simulated network of station data. The simulated time series also contained outliers, missing data periods and local station trends. Further, a stochastic nonlinear global (network-wide trend was added.

    Participants provided 25 separate homogenized contributions as part of the blind study as well as 22 additional solutions submitted after the details of the imposed inhomogeneities were revealed. These homogenized datasets were assessed by a number of performance metrics including (i the centered root mean square error relative to the true homogeneous value at various averaging scales, (ii the error in linear trend estimates and (iii traditional contingency skill scores. The metrics were computed both using the individual station series as well as the network average regional series. The performance of the contributions depends significantly on the error metric considered. Contingency scores by themselves are not very informative. Although relative homogenization algorithms typically improve the homogeneity of temperature data, only the best ones improve

  1. The Deterministic Dendritic Cell Algorithm

    Greensmith, Julie


    The Dendritic Cell Algorithm is an immune-inspired algorithm orig- inally based on the function of natural dendritic cells. The original instantiation of the algorithm is a highly stochastic algorithm. While the performance of the algorithm is good when applied to large real-time datasets, it is difficult to anal- yse due to the number of random-based elements. In this paper a deterministic version of the algorithm is proposed, implemented and tested using a port scan dataset to provide a controllable system. This version consists of a controllable amount of parameters, which are experimented with in this paper. In addition the effects are examined of the use of time windows and variation on the number of cells, both which are shown to influence the algorithm. Finally a novel metric for the assessment of the algorithms output is introduced and proves to be a more sensitive metric than the metric used with the original Dendritic Cell Algorithm.

  2. An Adaptive Memory Evolution Algorithm

    Caihong Wu


    Full Text Available Memory mechanism is applied to the optimization algorithm by more study. In order to improve the algorithm of adaptive ability, introducing memory ability in evolutionary algorithm framework, an Adaptive Memory Evolution Algorithm (AMEA is proposed. The algorithm set matrix to record the exploring experiences and exploring results of the individual parent. The algorithm uses these records to guide the generation of offspring. And thus AMEA can adaptively select the dimension to mutate and exploring radius. In addition, to improve the algorithm accuracy, the algorithm raises the best opportunities by using super-variation operator. In the simulation test, compared with similar algorithms, the results show that AMEA has fast convergence speed and optimum performance of global convergence.

  3. Skeletonization Algorithm for Numeral Patterns

    Gupta Rakesh


    Full Text Available Skeletonization has been a part of morphological image processing for a wide variety of applications. Skeletonization algorithms have played an important role in the preprocessing phase of OCR systems. Many algorithms for vectorization by skeletonization have been devised and applied to a great variety of pictures and drawings for data compression, pattern recognition and raster-to-vector conversion. The vectorization algorithms often used in pattern recognition tasks also require one-pixel-wide lines as input. But parallel skeletonization algorithms which generate one-pixel-wide skeletons can have difficulty in preserving the connectivity of an image or generate spurious branches. In this paper an alternative parallel skeletonization algorithm has been developed and implemented. This algorithm is better than already existing algorithms in terms of connectivity and spurious branches. A few most common skeletonization algorithms have been implemented and evaluated on the basis of performance parameters and compared with newly developed algorithm.

  4. Genetic Algorithms and Local Search

    Whitley, Darrell


    The first part of this presentation is a tutorial level introduction to the principles of genetic search and models of simple genetic algorithms. The second half covers the combination of genetic algorithms with local search methods to produce hybrid genetic algorithms. Hybrid algorithms can be modeled within the existing theoretical framework developed for simple genetic algorithms. An application of a hybrid to geometric model matching is given. The hybrid algorithm yields results that improve on the current state-of-the-art for this problem.

  5. Robust seed selection algorithm for k-means type algorithms

    Pavan, K. Karteeka; Rao, Allam Appa; Rao, A. V. Dattatreya; Sridhar, G. R.


    Selection of initial seeds greatly affects the quality of the clusters and in k-means type algorithms. Most of the seed selection methods result different results in different independent runs. We propose a single, optimal, outlier insensitive seed selection algorithm for k-means type algorithms as extension to k-means++. The experimental results on synthetic, real and on microarray data sets demonstrated that effectiveness of the new algorithm in producing the clustering results

  6. An Improved Apriori Algorithm

    LIU Shan; LIAO Yongyi


    In this paper,We study the Apriori and FP-growth algorithm in mining association rules and give a method for computing all the frequent item-sets in a database.Its basic idea is giving a concept based on the boolean vector business product,which be computed between all the businesses,then we can get all the two frequent item-sets (min_sup=2).We basis their inclusive relation to construct a set-tree of item-sets in database transaction,and then traverse path in it and get all the frequent item-sets.Therefore,we can get minimal frequent item sets between transactions and items in the database without scanning the database and iteratively computing in Apriori algorithm.

  7. Partitional clustering algorithms


    This book summarizes the state-of-the-art in partitional clustering. Clustering, the unsupervised classification of patterns into groups, is one of the most important tasks in exploratory data analysis. Primary goals of clustering include gaining insight into, classifying, and compressing data. Clustering has a long and rich history that spans a variety of scientific disciplines including anthropology, biology, medicine, psychology, statistics, mathematics, engineering, and computer science. As a result, numerous clustering algorithms have been proposed since the early 1950s. Among these algorithms, partitional (nonhierarchical) ones have found many applications, especially in engineering and computer science. This book provides coverage of consensus clustering, constrained clustering, large scale and/or high dimensional clustering, cluster validity, cluster visualization, and applications of clustering. Examines clustering as it applies to large and/or high-dimensional data sets commonly encountered in reali...

  8. The Relegation Algorithm

    Deprit, André; Palacián, Jesúus; Deprit, Etienne


    The relegation algorithm extends the method of normalization by Lie transformations. Given a Hamiltonian that is a power series ℋ = ℋ0+ ɛℋ1+ ... of a small parameter ɛ, normalization constructs a map which converts the principal part ℋ0into an integral of the transformed system — relegation does the same for an arbitrary function ℋ[G]. If the Lie derivative induced by ℋ[G] is semi-simple, a double recursion produces the generator of the relegating transformation. The relegation algorithm is illustrated with an elementary example borrowed from galactic dynamics; the exercise serves as a standard against which to test software implementations. Relegation is also applied to the more substantial example of a Keplerian system perturbed by radiation pressure emanating from a rotating source.

  9. Genetic Algorithm for Optimization: Preprocessor and Algorithm

    Sen, S. K.; Shaykhian, Gholam A.


    Genetic algorithm (GA) inspired by Darwin's theory of evolution and employed to solve optimization problems - unconstrained or constrained - uses an evolutionary process. A GA has several parameters such the population size, search space, crossover and mutation probabilities, and fitness criterion. These parameters are not universally known/determined a priori for all problems. Depending on the problem at hand, these parameters need to be decided such that the resulting GA performs the best. We present here a preprocessor that achieves just that, i.e., it determines, for a specified problem, the foregoing parameters so that the consequent GA is a best for the problem. We stress also the need for such a preprocessor both for quality (error) and for cost (complexity) to produce the solution. The preprocessor includes, as its first step, making use of all the information such as that of nature/character of the function/system, search space, physical/laboratory experimentation (if already done/available), and the physical environment. It also includes the information that can be generated through any means - deterministic/nondeterministic/graphics. Instead of attempting a solution of the problem straightway through a GA without having/using the information/knowledge of the character of the system, we would do consciously a much better job of producing a solution by using the information generated/created in the very first step of the preprocessor. We, therefore, unstintingly advocate the use of a preprocessor to solve a real-world optimization problem including NP-complete ones before using the statistically most appropriate GA. We also include such a GA for unconstrained function optimization problems.

  10. Convex hull ranking algorithm for multi-objective evolutionary algorithms

    Davoodi Monfrared, M.; Mohades, A.; Rezaei, J.


    Due to many applications of multi-objective evolutionary algorithms in real world optimization problems, several studies have been done to improve these algorithms in recent years. Since most multi-objective evolutionary algorithms are based on the non-dominated principle, and their complexity depen

  11. An efficient algorithm for function optimization: modified stem cells algorithm

    Taherdangkoo, Mohammad; Paziresh, Mahsa; Yazdi, Mehran; Bagheri, Mohammad


    In this paper, we propose an optimization algorithm based on the intelligent behavior of stem cell swarms in reproduction and self-organization. Optimization algorithms, such as the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm, Ant Colony Optimization (ACO) algorithm and Artificial Bee Colony (ABC) algorithm, can give solutions to linear and non-linear problems near to the optimum for many applications; however, in some case, they can suffer from becoming trapped in local optima. The Stem Cells Algorithm (SCA) is an optimization algorithm inspired by the natural behavior of stem cells in evolving themselves into new and improved cells. The SCA avoids the local optima problem successfully. In this paper, we have made small changes in the implementation of this algorithm to obtain improved performance over previous versions. Using a series of benchmark functions, we assess the performance of the proposed algorithm and compare it with that of the other aforementioned optimization algorithms. The obtained results prove the superiority of the Modified Stem Cells Algorithm (MSCA).

  12. Quantum gate decomposition algorithms.

    Slepoy, Alexander


    Quantum computing algorithms can be conveniently expressed in a format of a quantum logical circuits. Such circuits consist of sequential coupled operations, termed ''quantum gates'', or quantum analogs of bits called qubits. We review a recently proposed method [1] for constructing general ''quantum gates'' operating on an qubits, as composed of a sequence of generic elementary ''gates''.

  13. Combinatory CPU Scheduling Algorithm

    Saeeda Bibi; Farooque Azam*; Yasir Chaudhry


    Central Processing Unit (CPU) plays a significant role in computer system by transferring its control among different processes. As CPU is a central component, hence it must be used efficiently. Operating system performs an essential task that is known as CPU scheduling for efficient utilization of CPU. CPU scheduling has strong effect on resource utilization as well as overall performance of the system. In this paper, a new CPU scheduling algorithm called Combinatory is proposed that combine...

  14. Graph algorithms for bioinformatics

    Profiti, Giuseppe


    Biological data are inherently interconnected: protein sequences are connected to their annotations, the annotations are structured into ontologies, and so on. While protein-protein interactions are already represented by graphs, in this work I am presenting how a graph structure can be used to enrich the annotation of protein sequences thanks to algorithms that analyze the graph topology. We also describe a novel solution to restrict the data generation needed for building such a graph, than...

  15. Benchmarking conflict resolution algorithms

    Vanaret, Charlie; Gianazza, David; Durand, Nicolas; Gotteland, Jean-Baptiste


    Applying a benchmarking approach to conflict resolution problems is a hard task, as the analytical form of the constraints is not simple. This is especially the case when using realistic dynamics and models, considering accelerating aircraft that may follow flight paths that are not direct. Currently, there is a lack of common problems and data that would allow researchers to compare the performances of several conflict resolution algorithms. The present paper introduces a benchmarking approa...

  16. Algorithmics from early years

    Jochemczyk, Wanda; Olędzka, Katarzyna; Samulska, Agnieszka


    We teach algorithmics at early stages of children development by means of various programming languages and ICT tools in order to develop their cognitive and mathematical skills. Computational thinking is not only a useful for computer scientists, but also a fundamental skill for other people. We should teach children computational skills along with reading, writing, and arithmetic. In this article we present our three-year experience of teaching courses, during which we combine face-to-fa...

  17. Universal Algorithmic Ethics

    Leuenberger, Gabriel


    This paper proposes a novel research direction for algorithmic probability theory, which now allows to formalize philosophical questions about concepts such as the simulation argument, personal identity, and the rationality of utilitarianism. Pushing progress in these directions might be of high relevance for future society and artificial general intelligence. We start to build up a set of formulae while relating them to the real world. We then use our gained understanding to outline in a few...

  18. Energy-Efficient Algorithms

    Demaine, Erik D.; Lynch, Jayson; Mirano, Geronimo J.; Tyagi, Nirvan


    We initiate the systematic study of the energy complexity of algorithms (in addition to time and space complexity) based on Landauer's Principle in physics, which gives a lower bound on the amount of energy a system must dissipate if it destroys information. We propose energy-aware variations of three standard models of computation: circuit RAM, word RAM, and transdichotomous RAM. On top of these models, we build familiar high-level primitives such as control logic, memory allocation, and gar...

  19. Boosting foundations and algorithms

    Schapire, Robert E


    Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate "rules of thumb." A remarkably rich theory has evolved around boosting, with connections to a range of topics, including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterious, controversial, even paradoxical.

  20. SPA: Solar Position Algorithm

    Reda, Ibrahim; Andreas, Afshin


    The Solar Position Algorithm (SPA) calculates the solar zenith and azimuth angles in the period from the year -2000 to 6000, with uncertainties of +/- 0.0003 degrees based on the date, time, and location on Earth. SPA is implemented in C; in addition to being available for download, an online calculator using this code is available at

  1. Clustering using Genetic Algorithms

    Kudová, Petra

    Ostrava : VŠB Technická univerzita, 2007 - (Snášel, V.; Platoš, J.), s. 1-11 ISBN 978-80-248-1332-5. [WETDAP 2007. Workshop in Conjunction with Znalosti 2007 /1./. Ostrava (CZ), 22.02.2007-22.02.2007] R&D Projects: GA AV ČR 1ET100300414 Institutional research plan: CEZ:AV0Z10300504 Keywords : evolutionary techniques * genetic algorithms * unsupervised learning * clustering Subject RIV: IN - Informatics, Computer Science

  2. Clustering Genetic Algorithm

    Kudová, Petra

    Los Alamitos : IEEE, 2007 - (Tjoa, A.; Wagner, R.), s. 138-142 ISBN 978-0-7695-2932-5. [ETID '07. International Workshop on Evolutionary Techniques /1./, DEXA 2007 International Conference /18./. Regensburg (DE), 03.09.2007-07.09.2007] R&D Projects: GA AV ČR 1ET100300414 Institutional research plan: CEZ:AV0Z10300504 Keywords : clustering * genetic algorithms * k-means Subject RIV: IN - Informatics, Computer Science

  3. RADFLO physics and algorithms

    Symbalisty, E.M.D.; Zinn, J.; Whitaker, R.W.


    This paper describes the history, physics, and algorithms of the computer code RADFLO and its extension HYCHEM. RADFLO is a one-dimensional, radiation-transport hydrodynamics code that is used to compute early-time fireball behavior for low-altitude nuclear bursts. The primary use of the code is the prediction of optical signals produced by nuclear explosions. It has also been used to predict thermal and hydrodynamic effects that are used for vulnerability and lethality applications. Another closely related code, HYCHEM, is an extension of RADFLO which includes the effects of nonequilibrium chemistry. Some examples of numerical results will be shown, along with scaling expressions derived from those results. We describe new computations of the structures and luminosities of steady-state shock waves and radiative thermal waves, which have been extended to cover a range of ambient air densities for high-altitude applications. We also describe recent modifications of the codes to use a one-dimensional analog of the CAVEAT fluid-dynamics algorithm in place of the former standard Richtmyer-von Neumann algorithm.

  4. Large scale tracking algorithms.

    Hansen, Ross L.; Love, Joshua Alan; Melgaard, David Kennett; Karelitz, David B.; Pitts, Todd Alan; Zollweg, Joshua David; Anderson, Dylan Z.; Nandy, Prabal; Whitlow, Gary L.; Bender, Daniel A.; Byrne, Raymond Harry


    Low signal-to-noise data processing algorithms for improved detection, tracking, discrimination and situational threat assessment are a key research challenge. As sensor technologies progress, the number of pixels will increase signi cantly. This will result in increased resolution, which could improve object discrimination, but unfortunately, will also result in a significant increase in the number of potential targets to track. Many tracking techniques, like multi-hypothesis trackers, suffer from a combinatorial explosion as the number of potential targets increase. As the resolution increases, the phenomenology applied towards detection algorithms also changes. For low resolution sensors, "blob" tracking is the norm. For higher resolution data, additional information may be employed in the detection and classfication steps. The most challenging scenarios are those where the targets cannot be fully resolved, yet must be tracked and distinguished for neighboring closely spaced objects. Tracking vehicles in an urban environment is an example of such a challenging scenario. This report evaluates several potential tracking algorithms for large-scale tracking in an urban environment.

  5. Iterative Algorithms for Nonexpansive Mappings

    Yao Yonghong


    Full Text Available Abstract We suggest and analyze two new iterative algorithms for a nonexpansive mapping in Banach spaces. We prove that the proposed iterative algorithms converge strongly to some fixed point of .

  6. Building Better Nurse Scheduling Algorithms

    Aickelin, Uwe


    The aim of this research is twofold: Firstly, to model and solve a complex nurse scheduling problem with an integer programming formulation and evolutionary algorithms. Secondly, to detail a novel statistical method of comparing and hence building better scheduling algorithms by identifying successful algorithm modifications. The comparison method captures the results of algorithms in a single figure that can then be compared using traditional statistical techniques. Thus, the proposed method of comparing algorithms is an objective procedure designed to assist in the process of improving an algorithm. This is achieved even when some results are non-numeric or missing due to infeasibility. The final algorithm outperforms all previous evolutionary algorithms, which relied on human expertise for modification.

  7. Disk Scheduling: Selection of Algorithm

    Yashvir, S.; Prakash, Om


    The objective of this paper is to take some aspects of disk scheduling and scheduling algorithms. The disk scheduling is discussed with a sneak peak in general and selection of algorithm in particular.

  8. Fast Density Based Clustering Algorithm

    Priyanka Trikha; Singh Vijendra


    Clustering problem is an unsupervised learning problem. It is a procedure that partition data objects into matching clusters. The data objects in the same cluster are quite similar to each other and dissimilar in the other clusters. The traditional algorithms do not meet the latest multiple requirements simultaneously for objects. Density-based clustering algorithms find clusters based on density of data points in a region. DBSCAN algorithm is one of the density-based clustering algorithms. I...

  9. Join-Graph Propagation Algorithms

    Mateescu, Robert; Kask, Kalev; Gogate, Vibhav; Dechter, Rina


    The paper investigates parameterized approximate message-passing schemes that are based on bounded inference and are inspired by Pearl's belief propagation algorithm (BP). We start with the bounded inference mini-clustering algorithm and then move to the iterative scheme called Iterative Join-Graph Propagation (IJGP), that combines both iteration and bounded inference. Algorithm IJGP belongs to the class of Generalized Belief Propagation algorithms, a framework that allowed connections with a...

  10. Open Mass Spectrometry Search Algorithm

    Geer, L Y; Kowalak, J A; Wagner, L; Xu, M; Maynard, D M; Yang, X; Shi, W; Bryant, S H; Geer, Lewis Y.; Markey, Sanford P.; Kowalak, Jeffrey A.; Wagner, Lukas; Xu, Ming; Maynard, Dawn M.; Yang, Xiaoyu; Shi, Wenyao; Bryant, Stephen H.


    Large numbers of MS/MS peptide spectra generated in proteomics experiments require efficient, sensitive and specific algorithms for peptide identification. In the Open Mass Spectrometry Search Algorithm [OMSSA], specificity is calculated by a classic probability score using an explicit model for matching experimental spectra to sequences. At default thresholds, OMSSA matches more spectra from a standard protein cocktail than a comparable algorithm. OMSSA is designed to be faster than published algorithms in searching large MS/MS datasets.

  11. Genetic Algorithms and Quantum Computation

    Giraldi, Gilson A.; Portugal, Renato; Thess, Ricardo N.


    Recently, researchers have applied genetic algorithms (GAs) to address some problems in quantum computation. Also, there has been some works in the designing of genetic algorithms based on quantum theoretical concepts and techniques. The so called Quantum Evolutionary Programming has two major sub-areas: Quantum Inspired Genetic Algorithms (QIGAs) and Quantum Genetic Algorithms (QGAs). The former adopts qubit chromosomes as representations and employs quantum gates for the search of the best ...

  12. Foundations of genetic algorithms 1991



    Foundations of Genetic Algorithms 1991 (FOGA 1) discusses the theoretical foundations of genetic algorithms (GA) and classifier systems.This book compiles research papers on selection and convergence, coding and representation, problem hardness, deception, classifier system design, variation and recombination, parallelization, and population divergence. Other topics include the non-uniform Walsh-schema transform; spurious correlations and premature convergence in genetic algorithms; and variable default hierarchy separation in a classifier system. The grammar-based genetic algorithm; condition

  13. Combinatorial optimization algorithms and complexity

    Papadimitriou, Christos H


    This clearly written, mathematically rigorous text includes a novel algorithmic exposition of the simplex method and also discusses the Soviet ellipsoid algorithm for linear programming; efficient algorithms for network flow, matching, spanning trees, and matroids; the theory of NP-complete problems; approximation algorithms, local search heuristics for NP-complete problems, more. All chapters are supplemented by thought-provoking problems. A useful work for graduate-level students with backgrounds in computer science, operations research, and electrical engineering.

  14. Evolutionary Algorithms and Dynamic Programming

    Doerr, Benjamin; Eremeev, Anton; Neumann, Frank; Theile, Madeleine; Thyssen, Christian


    Recently, it has been proven that evolutionary algorithms produce good results for a wide range of combinatorial optimization problems. Some of the considered problems are tackled by evolutionary algorithms that use a representation which enables them to construct solutions in a dynamic programming fashion. We take a general approach and relate the construction of such algorithms to the development of algorithms using dynamic programming techniques. Thereby, we give general guidelines on how ...

  15. Essential algorithms a practical approach to computer algorithms

    Stephens, Rod


    A friendly and accessible introduction to the most useful algorithms Computer algorithms are the basic recipes for programming. Professional programmers need to know how to use algorithms to solve difficult programming problems. Written in simple, intuitive English, this book describes how and when to use the most practical classic algorithms, and even how to create new algorithms to meet future needs. The book also includes a collection of questions that can help readers prepare for a programming job interview. Reveals methods for manipulating common data structures s

  16. Comparison of Text Categorization Algorithms

    SHI Yong-feng; ZHAO Yan-ping


    This paper summarizes several automatic text categorization algorithms in common use recently, analyzes and compares their advantages and disadvantages.It provides clues for making use of appropriate automatic classifying algorithms in different fields.Finally some evaluations and summaries of these algorithms are discussed, and directions to further research have been pointed out.

  17. Continuous Media Tasks Scheduling Algorithm

    Myungryun Yoo


    Full Text Available In this paper the authors propose modified proportional share scheduling algorithm considering the characteristics of continuous media such as its continuity and time dependency. Proposed scheduling algorithm shows graceful degradation of performance in overloaded situation and decreases the number of context switching. Proposed scheduling algorithm is evaluated using several numerical tests under various conditions, especially overloaded situation.

  18. Multipartite entanglement in quantum algorithms

    Bruss D.; MacChiavello C.


    We investigate the entanglement features of the quantum states employed in quantum algorithms. In particular, we analyse the multipartite entanglement properties in the Deutsch-Jozsa, Grover and Simon algorithms. Our results show that for these algorithms most instances involve multipartite entanglement.

  19. Algorithmic approach to diagram techniques

    An algorithmic approach to diagram techniques of elementary particles is proposed. The definition and axiomatics of the theory of algorithms are presented, followed by the list of instructions of an algorithm formalizing the construction of graphs and the assignment of mathematical objects to them. (T.A.)

  20. Opposite Degree Algorithm and Its Applications

    Xiao-Guang Yue


    Full Text Available The opposite (Opposite Degree, referred to as OD algorithm is an intelligent algorithm proposed by Yue Xiaoguang et al. Opposite degree algorithm is mainly based on the concept of opposite degree, combined with the idea of design of neural network and genetic algorithm and clustering analysis algorithm. The OD algorithm is divided into two sub algorithms, namely: opposite degree - numerical computation (OD-NC algorithm and opposite degree - Classification computation (OD-CC algorithm.

  1. Opposite Degree Algorithm and Its Applications

    Xiao-Guang Yue


    The opposite (Opposite Degree, referred to as OD) algorithm is an intelligent algorithm proposed by Yue Xiaoguang et al. Opposite degree algorithm is mainly based on the concept of opposite degree, combined with the idea of design of neural network and genetic algorithm and clustering analysis algorithm. The OD algorithm is divided into two sub algorithms, namely: opposite degree - numerical computation (OD-NC) algorithm and opposite degree - Classification computation (OD-CC) algorithm.


    Narendran Rajagopalan


    Full Text Available Performance of Wireless LAN can be improved at each layer of the protocol stack with respect to energy efficiency. The Media Access Control layer is responsible for the key functions like access control and flow control. During contention, Backoff algorithm is used to gain access to the medium with minimum probability of collision. After studying different variations of back off algorithms that have been proposed, a new variant called History based Probabilistic Backoff Algorithm is proposed. Through mathematical analysis and simulation results using NS-2, it is seen that proposed History based Probabilistic Backoff algorithm performs better than Binary Exponential Backoff algorithm.

  3. Parallelization of the PC Algorithm

    Madsen, Anders Læsø; Jensen, Frank; Salmerón, Antonio; Langseth, Helge; Nielsen, Thomas Dyhre


    This paper describes a parallel version of the PC algorithm for learning the structure of a Bayesian network from data. The PC algorithm is a constraint-based algorithm consisting of five steps where the first step is to perform a set of (conditional) independence tests while the remaining four....... The proposed parallel PC algorithm is evaluated on data sets generated at random from five different real- world Bayesian networks. The results demonstrate that significant time performance improvements are possible using the proposed algorithm....

  4. Analysis of Virus Algorithms

    Jyoti Kalyani


    Full Text Available Security of wired and wireless networks is the most challengeable in today's computer world. The aim of this study was to give brief introduction about viruses and worms, their creators and characteristics of algorithms used by viruses. Here wired and wireless network viruses are elaborated. Also viruses are compared with human immune system. On the basis of this comparison four guidelines are given to detect viruses so that more secure systems are made. While concluding this study it is found that the security is most challengeable, thus it is required to make more secure models which automatically detect viruses and prevent the system from its affect.

  5. Algorithmic States of Exception

    McQuillan, Daniel


    In this paper I argue that pervasive tracking and data-mining are leading to shifts in governmentality that can be characterised as algorithmic states of exception. I also argue that the apparatus that performs this change owes as much to everyday business models as it does to mass surveillance. I look at technical changes at the level of data structures, such as the move to NoSQL databases, and how this combines with data-mining and machine learning to accelerate the use of prediction as a ...

  6. Algorithms and Science

    Chazelle, Bernard


    The following anecdote, perhaps apocryphal, is told about the great Danish physicist Niels Bohr:­­– Professor Bohr, I see you have a horseshoe hanging on the wall. Don’t tell me you believe in this kind of thing!­­– Don’t worry, I don’t believe in it at all, but I was told that it works even if you don’t believe in it. So it goes for the algorithmic revolution. Beyond the scepticism or infatuation of the day regarding the latest IT novelty hides one of those paradigm shifts dear to Thomas Kuh...

  7. Online Planning Algorithm

    Rabideau, Gregg R.; Chien, Steve A.


    AVA v2 software selects goals for execution from a set of goals that oversubscribe shared resources. The term goal refers to a science or engineering request to execute a possibly complex command sequence, such as image targets or ground-station downlinks. Developed as an extension to the Virtual Machine Language (VML) execution system, the software enables onboard and remote goal triggering through the use of an embedded, dynamic goal set that can oversubscribe resources. From the set of conflicting goals, a subset must be chosen that maximizes a given quality metric, which in this case is strict priority selection. A goal can never be pre-empted by a lower priority goal, and high-level goals can be added, removed, or updated at any time, and the "best" goals will be selected for execution. The software addresses the issue of re-planning that must be performed in a short time frame by the embedded system where computational resources are constrained. In particular, the algorithm addresses problems with well-defined goal requests without temporal flexibility that oversubscribes available resources. By using a fast, incremental algorithm, goal selection can be postponed in a "just-in-time" fashion allowing requests to be changed or added at the last minute. Thereby enabling shorter response times and greater autonomy for the system under control.

  8. Algorithmic Relative Complexity

    Daniele Cerra


    Full Text Available Information content and compression are tightly related concepts that can be addressed through both classical and algorithmic information theories, on the basis of Shannon entropy and Kolmogorov complexity, respectively. The definition of several entities in Kolmogorov’s framework relies upon ideas from classical information theory, and these two approaches share many common traits. In this work, we expand the relations between these two frameworks by introducing algorithmic cross-complexity and relative complexity, counterparts of the cross-entropy and relative entropy (or Kullback-Leibler divergence found in Shannon’s framework. We define the cross-complexity of an object x with respect to another object y as the amount of computational resources needed to specify x in terms of y, and the complexity of x related to y as the compression power which is lost when adopting such a description for x, compared to the shortest representation of x. Properties of analogous quantities in classical information theory hold for these new concepts. As these notions are incomputable, a suitable approximation based upon data compression is derived to enable the application to real data, yielding a divergence measure applicable to any pair of strings. Example applications are outlined, involving authorship attribution and satellite image classification, as well as a comparison to similar established techniques.

  9. Fatigue evaluation algorithms: Review

    Passipoularidis, V.A.; Broendsted, P.


    A progressive damage fatigue simulator for variable amplitude loads named FADAS is discussed in this work. FADAS (Fatigue Damage Simulator) performs ply by ply stress analysis using classical lamination theory and implements adequate stiffness discount tactics based on the failure criterion of Puck, to model the degradation caused by failure events in ply level. Residual strength is incorporated as fatigue damage accumulation metric. Once the typical fatigue and static properties of the constitutive ply are determined,the performance of an arbitrary lay-up under uniaxial and/or multiaxial load time series can be simulated. The predictions are validated against fatigue life data both from repeated block tests at a single stress ratio as well as against spectral fatigue using the WISPER, WISPERX and NEW WISPER load sequences on a Glass/Epoxy multidirectional laminate typical of a wind turbine rotor blade construction. Two versions of the algorithm, the one using single-step and the other using incremental application of each load cycle (in case of ply failure) are implemented and compared. Simulation results confirm the ability of the algorithm to take into account load sequence effects. In general, FADAS performs well in predicting life under both spectral and block loading fatigue. (author)

  10. Online Pairwise Learning Algorithms.

    Ying, Yiming; Zhou, Ding-Xuan


    Pairwise learning usually refers to a learning task that involves a loss function depending on pairs of examples, among which the most notable ones are bipartite ranking, metric learning, and AUC maximization. In this letter we study an online algorithm for pairwise learning with a least-square loss function in an unconstrained setting of a reproducing kernel Hilbert space (RKHS) that we refer to as the Online Pairwise lEaRning Algorithm (OPERA). In contrast to existing works (Kar, Sriperumbudur, Jain, & Karnick, 2013 ; Wang, Khardon, Pechyony, & Jones, 2012 ), which require that the iterates are restricted to a bounded domain or the loss function is strongly convex, OPERA is associated with a non-strongly convex objective function and learns the target function in an unconstrained RKHS. Specifically, we establish a general theorem that guarantees the almost sure convergence for the last iterate of OPERA without any assumptions on the underlying distribution. Explicit convergence rates are derived under the condition of polynomially decaying step sizes. We also establish an interesting property for a family of widely used kernels in the setting of pairwise learning and illustrate the convergence results using such kernels. Our methodology mainly depends on the characterization of RKHSs using its associated integral operators and probability inequalities for random variables with values in a Hilbert space. PMID:26890352

  11. An Improved Robot Path Planning Algorithm Based on Genetic Algorithm

    Hammin Liu


    Full Text Available Robot path planning is a NP problem; traditional optimization methods are not very effective to solve it. Traditional genetic algorithm trapped into the local minimum easily. Therefore, based on a simple genetic algorithm and combine the base ideology of orthogonal design method then applied it to the population initialization, using the intergenerational elite mechanism, as well as the introduction of adaptive local search operator to prevent trapped into the local minimum and improve the convergence speed to form a new genetic algorithm. Through the series of numerical experiments, the new algorithm has been proved to be efficiency. We also use the proposed algorithm to solve the robot path planning problem and the experiment results indicated that the new algorithm is efficiency for solving the robot path planning problems and the best path usually can be found.

  12. The Watershed Algorithm for Image Segmentation

    OU Yan; LIN Nan


    This article introduced the watershed algorithm for the segmentation, illustrated the segmation process by implementing this algorithm. By comparing with another three related algorithm, this article revealed both the advantages and drawbacks of the watershed algorithm.

  13. Probabilistic analysis of geometric algorithms

    Golin, M.J.


    In the paper, the authors describe the subtleties involved in probabilistically analyzing simple algorithms in computational geometry. The authors also work through a few easy examples of such analyses. The first example analyzes Quicksort, the second analyzes Quickhull, and the third analyzes interpoint distances between uniformly distributed points in hypercubes or hypertori. The authors present a simple but effective preprocessing algorithm for calculating convex hulls. The algorithm is short and intuitive. It does a fast linear scan through the points, identifying many which are not on the convex hull and can therefore be eliminated. The authors perform an exact analysis of this algorithm showing that, given n points distributed uniformly in the unit square, only about 8 sq. root (n) of them remain after the preprocessing step: in higher dimensions only d{sub d}n{sup 1 {minus}1/d} will remain. The authors present the results of simulations comparing our mathematical analysis to reality. Finally, the authors end with a discussion of what distinguishes this algorithm from certain obvious variants. The authors analyze closet pair algorithms. First, the authors analyze a sweep-line closest-pair algorithm, one similar in spirit to Hoey and Shamos' divide and conquer algorithm for solving the same problem. The result is that, given n points uniformly distributed in the unit square and then sorted, there is a six line algorithm that finds the closest pair in O(n) expected time. Moreover, this algorithm uses no complicated data structures. The authors then analyze a second algorithm, one that finds the closest pair using a modified version of Bentley and Papadimitriou's nearest neighbor projection algorithm. The result, again, is that after the sorting stage, the linear scan stage of this new algorithm also finds the closest pair in O(n) expected time.

  14. STAR Algorithm Integration Team - Facilitating operational algorithm development

    Mikles, V. J.


    The NOAA/NESDIS Center for Satellite Research and Applications (STAR) provides technical support of the Joint Polar Satellite System (JPSS) algorithm development and integration tasks. Utilizing data from the S-NPP satellite, JPSS generates over thirty Environmental Data Records (EDRs) and Intermediate Products (IPs) spanning atmospheric, ocean, cryosphere, and land weather disciplines. The Algorithm Integration Team (AIT) brings technical expertise and support to product algorithms, specifically in testing and validating science algorithms in a pre-operational environment. The AIT verifies that new and updated algorithms function in the development environment, enforces established software development standards, and ensures that delivered packages are functional and complete. AIT facilitates the development of new JPSS-1 algorithms by implementing a review approach based on the Enterprise Product Lifecycle (EPL) process. Building on relationships established during the S-NPP algorithm development process and coordinating directly with science algorithm developers, the AIT has implemented structured reviews with self-contained document suites. The process has supported algorithm improvements for products such as ozone, active fire, vegetation index, and temperature and moisture profiles.

  15. Efficient Kriging Algorithms

    Memarsadeghi, Nargess


    More efficient versions of an interpolation method, called kriging, have been introduced in order to reduce its traditionally high computational cost. Written in C++, these approaches were tested on both synthetic and real data. Kriging is a best unbiased linear estimator and suitable for interpolation of scattered data points. Kriging has long been used in the geostatistic and mining communities, but is now being researched for use in the image fusion of remotely sensed data. This allows a combination of data from various locations to be used to fill in any missing data from any single location. To arrive at the faster algorithms, sparse SYMMLQ iterative solver, covariance tapering, Fast Multipole Methods (FMM), and nearest neighbor searching techniques were used. These implementations were used when the coefficient matrix in the linear system is symmetric, but not necessarily positive-definite.

  16. Quicksort algorithm again revisited

    Charles Knessl


    Full Text Available We consider the standard Quicksort algorithm that sorts n distinct keys with all possible n! orderings of keys being equally likely. Equivalently, we analyze the total path length L(n in a randomly built binary search tree. Obtaining the limiting distribution of L(n is still an outstanding open problem. In this paper, we establish an integral equation for the probability density of the number of comparisons L(n. Then, we investigate the large deviations of L(n. We shall show that the left tail of the limiting distribution is much ``thinner'' (i.e., double exponential than the right tail (which is only exponential. Our results contain some constants that must be determined numerically. We use formal asymptotic methods of applied mathematics such as the WKB method and matched asymptotics.

  17. Algorithmes, machines et langages

    Berry, Gérard


    Enseignement : Le temps et les événements en informatique J’ai donné le cours « Le temps et les événements en informatique » dans le cadre de la chaire Algorithmes, machines et langages, créée le 4 juillet 2013 comme première chaire de plein exercice en informatique. J’avais introduit l’informatique au Collège de France en 2007-2008 par le cours « Pourquoi et comment le monde devient numérique », au sein de la chaire annuelle d’Innovation technologique Liliane Bettencourt, puis, en 2009-2010,...

  18. Fighting Censorship with Algorithms

    Mahdian, Mohammad

    In countries such as China or Iran where Internet censorship is prevalent, users usually rely on proxies or anonymizers to freely access the web. The obvious difficulty with this approach is that once the address of a proxy or an anonymizer is announced for use to the public, the authorities can easily filter all traffic to that address. This poses a challenge as to how proxy addresses can be announced to users without leaking too much information to the censorship authorities. In this paper, we formulate this question as an interesting algorithmic problem. We study this problem in a static and a dynamic model, and give almost tight bounds on the number of proxy servers required to give access to n people k of whom are adversaries. We will also discuss how trust networks can be used in this context.

  19. Multisensor data fusion algorithm development

    Yocky, D.A.; Chadwick, M.D.; Goudy, S.P.; Johnson, D.K.


    This report presents a two-year LDRD research effort into multisensor data fusion. We approached the problem by addressing the available types of data, preprocessing that data, and developing fusion algorithms using that data. The report reflects these three distinct areas. First, the possible data sets for fusion are identified. Second, automated registration techniques for imagery data are analyzed. Third, two fusion techniques are presented. The first fusion algorithm is based on the two-dimensional discrete wavelet transform. Using test images, the wavelet algorithm is compared against intensity modulation and intensity-hue-saturation image fusion algorithms that are available in commercial software. The wavelet approach outperforms the other two fusion techniques by preserving spectral/spatial information more precisely. The wavelet fusion algorithm was also applied to Landsat Thematic Mapper and SPOT panchromatic imagery data. The second algorithm is based on a linear-regression technique. We analyzed the technique using the same Landsat and SPOT data.

  20. Fractal simplex algorithm in VBA

    Ouzký, Karel


    Basic idea of fractal simplex algorithm is based in the theory of matrix counting and knowledge of matrix representation of simplex tabuleao from revised simplex method. My desire is to explain theoretical basics on which this algorithm works and provide solution in language Visual Basic for Applications in application MS Excel 2007. Main benefit I see in the fact, that algorithm can solved specific class of mathematical problems in a way of exactness counting whithout necessity of using deci...

  1. Algorithms over partially ordered sets

    Baer, Robert M.; Østerby, Ole


    partially ordered sets, answer the combinatorial question of how many maximal chains might exist in a partially ordered set withn elements, and we give an algorithm for enumerating all maximal chains. We give (in § 3) algorithms which decide whether a partially ordered set is a (lower or upper) semi......-lattice, and whether a lattice has distributive, modular, and Boolean properties. Finally (in § 4) we give Algol realizations of the various algorithms....

  2. GPU accelerated Rna folding algorithm

    Rizk, Guillaume; Lavenier, Dominique


    Many bioinformatics studies require the analysis of RNA or DNA structures. More specifically, extensive work is done to elaborate efficient algorithms able to predict the 2-D folding structures of RNA or DNA sequences. However, the high computational complexity of the algorithms, combined with the rapid increase of genomic data, triggers the need of faster methods. Current approaches focus on parallelizing these algorithms on multiprocessor systems or on clusters, yielding to good performance...

  3. Tau reconstruction and identification algorithm

    Raman Khurana


    CMS has developed sophisticated tau identification algorithms for tau hadronic decay modes. Production of tau lepton decaying to hadrons are studied at 7 TeV centre-of-mass energy with 2011 collision data collected by CMS detector and has been used to measure the performance of tau identification algorithms by measuring identification efficiency and misidentification rates from electrons, muons and hadronic jets. These algorithms enable extended reach for the searches for MSSM Higgs, and other exotic particles.


    Narendran Rajagopalan; C.Mala


    Performance of Wireless LAN can be improved at each layer of the protocol stack with respect to energy efficiency. The Media Access Control layer is responsible for the key functions like access control and flow control. During contention, Backoff algorithm is used to gain access to the medium with minimum probability of collision. After studying different variations of back off algorithms that have been proposed, a new variant called History based Probabilistic Backoff Algorithm is proposed....

  5. Generating Specials: The Zorro Algorithm

    Thamsborg, Jacob


    The concept of a configuration graph associated to a primitive, aperiodic substitution is introduced in [1] as a convenient graphical representation of the infinite indeterminism of the shift space of the substitution. The main result of [1] is an algorithm to calculate this graph from the substitution, in this paper we turn the tables and produce substitutions from graphs. We do this using the Zorro algorithm, an entirely constructive and easily applicable algorithm. In the process we show t...

  6. Theoretical Aspects of Evolutionary Algorithms

    Wegener, Ingo


    Randomized search heuristics like simulated annealing and evolutionary algorithms are applied successfully in many different situations. However, the theory on these algorithms is still in its infancy. Here it is discussed how and why such a theory should be developed. Afterwards, some fundamental results on evolutionary algorithms are presented in order to show how theoretical results on randomized search heuristics can be proved and how they contribute to the understanding of evolutionary a...

  7. Machine Learning an algorithmic perspective

    Marsland, Stephen


    Traditional books on machine learning can be divided into two groups - those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how to code algorithms. The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but also provides the background needed to understand how and why these algorithms work. Machine Learning: An Algorithmic Perspective is that text.Theory Backed up by Practical ExamplesThe book covers neural networks, graphical models, reinforcement le

  8. Recent results on howard's algorithm

    Miltersen, P.B.


    Howard’s algorithm is a fifty-year old generally applicable algorithm for sequential decision making in face of uncertainty. It is routinely used in practice in numerous application areas that are so important that they usually go by their acronyms, e.g., OR, AI, and CAV. While Howard’s algorithm...... is generally recognized as fast in practice, until recently, its worst case time complexity was poorly understood. However, a surge of results since 2009 has led us to a much more satisfactory understanding of the worst case time complexity of the algorithm in the various settings in which it applies...

  9. Preconditioned quantum linear system algorithm.

    Clader, B D; Jacobs, B C; Sprouse, C R


    We describe a quantum algorithm that generalizes the quantum linear system algorithm [Harrow et al., Phys. Rev. Lett. 103, 150502 (2009)] to arbitrary problem specifications. We develop a state preparation routine that can initialize generic states, show how simple ancilla measurements can be used to calculate many quantities of interest, and integrate a quantum-compatible preconditioner that greatly expands the number of problems that can achieve exponential speedup over classical linear systems solvers. To demonstrate the algorithm's applicability, we show how it can be used to compute the electromagnetic scattering cross section of an arbitrary target exponentially faster than the best classical algorithm. PMID:23829722

  10. Diversity-Guided Evolutionary Algorithms

    Ursem, Rasmus Kjær


    Population diversity is undoubtably a key issue in the performance of evolutionary algorithms. A common hypothesis is that high diversity is important to avoid premature convergence and to escape local optima. Various diversity measures have been used to analyze algorithms, but so far few...... algorithms have used a measure to guide the search. The diversity-guided evolutionary algorithm (DGEA) uses the wellknown distance-to-average-point measure to alternate between phases of exploration (mutation) and phases of exploitation (recombination and selection). The DGEA showed remarkable results...

  11. Practical Parallel External Memory Algorithms via Simulation of Parallel Algorithms

    Robillard, David E


    This thesis introduces PEMS2, an improvement to PEMS (Parallel External Memory System). PEMS executes Bulk-Synchronous Parallel (BSP) algorithms in an External Memory (EM) context, enabling computation with very large data sets which exceed the size of main memory. Many parallel algorithms have been designed and implemented for Bulk-Synchronous Parallel models of computation. Such algorithms generally assume that the entire data set is stored in main memory at once. PEMS overcomes this limitation without requiring any modification to the algorithm by using disk space as memory for additional "virtual processors". Previous work has shown this to be a promising approach which scales well as computational resources (i.e. processors and disks) are added. However, the technique incurs significant overhead when compared with purpose-built EM algorithms. PEMS2 introduces refinements to the simulation process intended to reduce this overhead as well as the amount of disk space required to run the simulation. New func...

  12. A New Metaheuristic Bat-Inspired Algorithm

    Yang, Xin-She


    Metaheuristic algorithms such as particle swarm optimization, firefly algorithm and harmony search are now becoming powerful methods for solving many tough optimization problems. In this paper, we propose a new metaheuristic method, the Bat Algorithm, based on the echolocation behaviour of bats. We also intend to combine the advantages of existing algorithms into the new bat algorithm. After a detailed formulation and explanation of its implementation, we will then compare the proposed algorithm with other existing algorithms, including genetic algorithms and particle swarm optimization. Simulations show that the proposed algorithm seems much superior to other algorithms, and further studies are also discussed.

  13. Toward an Algorithmic Pedagogy

    Holly Willis


    Full Text Available The demand for an expanded definition of literacy to accommodate visual and aural media is not particularly new, but it gains urgency as college students transform, becoming producers of media in many of their everyday social activities. The response among those who grapple with these issues as instructors has been to advocate for new definitions of literacy and particularly, an understanding of visual literacy. These efforts are exemplary, and promote a much needed rethinking of literacy and models of pedagogy. However, in what is more akin to a manifesto than a polished argument, this essay argues that we need to push farther: What if we moved beyond visual rhetoric, as well as a game-based pedagogy and the adoption of a broad range of media tools on campus, toward a pedagogy grounded fundamentally in a media ecology? Framing this investigation in terms of a media ecology allows us to take account of the multiply determining relationships wrought not just by individual media, but by the interrelationships, dependencies and symbioses that take place within the dynamic system that is today’s high-tech university. An ecological approach allows us to examine what happens when new media practices collide with computational models, providing a glimpse of possible transformations not only ways of being but ways of teaching and learning. How, then, may pedagogical practices be transformed computationally or algorithmically and to what ends?

  14. Pulmonary thromboembolism diagnosis algorithms

    Kasai, Takeshi; Eto, Jun; Hayano, Daisuke; Ohashi, Masaki; Yoneda, Takahiro; Oyama, Hisaya; Inaba, Akira [Kameda General Hospital, Kamogawa, Chiba (Japan). Trauma and Emergency Care Center


    Our algorithm for diagnosing pulmonary thromboembolism combines ventilation/perfusion scanning with clinical criteria. Our perfusion scanning criterion states that high probability defines 2 segmental perfusion defects without corresponding radiographic abnormality and indeterminate probability defines less than 2 segmental perfusion defects (low probability: less than one segmental perfusion defect; intermediate: perfusion defects between high and low probability). The clinical criterion is divided into 7 items related to symptoms and signs suggestive of pulmonary thromboembolism. More than 4 items are defined as a highly suspicious clinical manifestation (HSCM), and less than 4 are considered a low suspicious clinical manifestation (LSCM). In 31 cases of high probability, 18 of HSCM did not include pulmonary angiograhy (PAG), and 13 of LSCM included PAG (positive: 11; negative: 2). In 12 cases of indeterminate probability, 7 of LSCM were observed without PAG and 5 of HSCM with PAG (positive: 4; negative: 1). PAG performance thus decreased to 41.9%. The positive prediction of high probability is 93.5%, which is very high, compared to indeterminate probability at 33.3%. (author)

  15. Two Aspects of Evolutionary Algorithms


    In this paper we discuss the paradigm of evolutionary algorithms (Eas). We argue about the need for new heuristics in real-world problem solving, discussing reasons why some problems are difficult tosolve. After introducing the main concepts of evolutionary algorithms, we concentrate on two issues:(1)self-adaptation of the parameters of EA, and (2) handling constraints.

  16. A distributed spanning tree algorithm

    Johansen, Karl Erik; Jørgensen, Ulla Lundin; Nielsen, Svend Hauge;


    We present a distributed algorithm for constructing a spanning tree for connected undirected graphs. Nodes correspond to processors and edges correspond to two way channels. Each processor has initially a distinct identity and all processors perform the same algorithm. Computation as well as...

  17. Ensemble algorithms in reinforcement learning

    Wiering, Marco A; van Hasselt, Hado


    This paper describes several ensemble methods that combine multiple different reinforcement learning (RL) algorithms in a single agent. The aim is to enhance learning speed and final performance by combining the chosen actions or action probabilities of different RL algorithms. We designed and imple

  18. Cascade Error Projection Learning Algorithm

    Duong, T. A.; Stubberud, A. R.; Daud, T.


    A detailed mathematical analysis is presented for a new learning algorithm termed cascade error projection (CEP) and a general learning frame work. This frame work can be used to obtain the cascade correlation learning algorithm by choosing a particular set of parameters.

  19. Algorithms in combinatorial design theory

    Colbourn, CJ


    The scope of the volume includes all algorithmic and computational aspects of research on combinatorial designs. Algorithmic aspects include generation, isomorphism and analysis techniques - both heuristic methods used in practice, and the computational complexity of these operations. The scope within design theory includes all aspects of block designs, Latin squares and their variants, pairwise balanced designs and projective planes and related geometries.

  20. Hirschberg's Algorithm for Approximate Matching

    Adam Drozdek


    Full Text Available The Hirschberg algorithm was devised to solve the longest common subsequence problem. The paper discusses the way of adopting the algorithm to solve the string matching problem in linear space to determine edit distance for two strings and their alignment.

  1. Penalty Algorithms in Hilbert Spaces

    Jean Pierre DUSSAULT; Hai SHEN; André BANDRAUK


    We analyze the classical penalty algorithm for nonlinear programming in HUbert spaces and obtain global convergence results, as well as asymptotic superlinear convergence order. These convergence results generalize similar results obtained for finite-dimensional problems. Moreover, the nature of the algorithms allows us to solve the unconstrained subproblems in finite-dimensional spaces.

  2. A Fast Fractional Difference Algorithm

    Jensen, Andreas Noack; Nielsen, Morten Ørregaard

    We provide a fast algorithm for calculating the fractional difference of a time series. In standard implementations, the calculation speed (number of arithmetic operations) is of order T 2, where T is the length of the time series. Our algorithm allows calculation speed of order T logT . For...

  3. A fast fractional difference algorithm

    Jensen, Andreas Noack; Nielsen, Morten Ørregaard


    We provide a fast algorithm for calculating the fractional difference of a time series. In standard implementations, the calculation speed (number of arithmetic operations) is of order T 2, where T is the length of the time series. Our algorithm allows calculation speed of order T logT . For...

  4. Sliding Window Orthonormal PAST Algorithm

    Badeau, Roland; Abed-Meraim, Karim; Richard, Gaël; David, Bertrand


    This paper introduces an orthonormal version of the sliding-window Projection Approximation Subspace Tracker (PAST). The new algorithm guarantees the orthonormality of the signal subspace basis at each iteration. Moreover, it has the same complexity as the original PAST algorithm, and like the more computationally demanding natural power (NP) method, it satisfies a global convergence property, and reaches an excellent tracking performance.

  5. A Distributed Spanning Tree Algorithm

    Johansen, Karl Erik; Jørgensen, Ulla Lundin; Nielsen, Sven Hauge; Nielsen, Søren Erik; Skyum, Sven

    We present a distributed algorithm for constructing a spanning tree for connected undirected graphs. Nodes correspond to processors and edges correspond to two-way channels. Each processor has initially a distinct identity and all processors perform the same algorithm. Computation as well as...

  6. Echo Cancellation I: Algorithms Simulation

    P. Sovka


    Full Text Available Echo cancellation system used in mobile communications is analyzed.Convergence behavior and misadjustment of several LMS algorithms arecompared. The misadjustment means errors in filter weight estimation.The resulting echo suppression for discussed algorithms with simulatedas well as rela speech signals is evaluated. The optional echocancellation configuration is suggested.

  7. Streaming Algorithms for Line Simplification

    Abam, Mohammad; de Berg, Mark; Hachenberger, Peter;


    problem in a streaming setting, where we only have a limited amount of storage, so that we cannot store all the points. We analyze the competitive ratio of our algorithms, allowing resource augmentation: we let our algorithm maintain a simplification with 2k (internal) points and compare the error of our...... simplification to the error of the optimal simplification with k points. We obtain the algorithms with O(1) competitive ratio for three cases: convex paths, where the error is measured using the Hausdorff distance (or Fréchet distance), xy-monotone paths, where the error is measured using the Hausdorff distance...... (or Fréchet distance), and general paths, where the error is measured using the Fréchet distance. In the first case the algorithm needs O(k) additional storage, and in the latter two cases the algorithm needs O(k 2) additional storage....

  8. Incremental PCA-LDA Algorithm

    Issam Dagher


    Full Text Available In this paper a recursive algorithm of calculating the discriminant features of thePCA-LDA procedure is introduced. This algorithm computes the principalcomponents of a sequence of vectors incrementally without estimating thecovariance matrix (so covariance-free and at the same time computing the lineardiscriminant directions along which the classes are well separated. Two majortechniques are used sequentially in a real time fashion in order to obtain the mostefficient and linearly discriminative components. This procedure is done bymerging the runs of two algorithms based on principal component analysis (PCAand linear discriminant analysis (LDA running sequentially. This algorithm isapplied to face recognition problem. Simulation results on different databasesshowed high average success rate of this algorithm compared to PCA and LDAalgorithms. The advantage of the incremental property of this algorithmcompared to the batch PCA-LDA is also shown.

  9. The Chopthin Algorithm for Resampling

    Gandy, Axel; Lau, F. Din-Houn


    Resampling is a standard step in particle filters and more generally sequential Monte Carlo methods. We present an algorithm, called chopthin, for resampling weighted particles. In contrast to standard resampling methods the algorithm does not produce a set of equally weighted particles; instead it merely enforces an upper bound on the ratio between the weights. Simulation studies show that the chopthin algorithm consistently outperforms standard resampling methods. The algorithms chops up particles with large weight and thins out particles with low weight, hence its name. It implicitly guarantees a lower bound on the effective sample size. The algorithm can be implemented efficiently, making it practically useful. We show that the expected computational effort is linear in the number of particles. Implementations for C++, R (on CRAN), Python and Matlab are available.

  10. A secured Cryptographic Hashing Algorithm

    Mohanty, Rakesh; Bishi, Sukant kumar


    Cryptographic hash functions for calculating the message digest of a message has been in practical use as an effective measure to maintain message integrity since a few decades. This message digest is unique, irreversible and avoids all types of collisions for any given input string. The message digest calculated from this algorithm is propagated in the communication medium along with the original message from the sender side and on the receiver side integrity of the message can be verified by recalculating the message digest of the received message and comparing the two digest values. In this paper we have designed and developed a new algorithm for calculating the message digest of any message and implemented t using a high level programming language. An experimental analysis and comparison with the existing MD5 hashing algorithm, which is predominantly being used as a cryptographic hashing tool, shows this algorithm to provide more randomness and greater strength from intrusion attacks. In this algorithm th...

  11. A Review on Quantum Search Algorithms

    Giri, Pulak Ranjan; Korepin, Vladimir E.


    The use of superposition of states in quantum computation, known as quantum parallelism, has significant advantage in terms of speed over the classical computation. It can be understood from the early invented quantum algorithms such as Deutsch's algorithm, Deutsch-Jozsa algorithm and its variation as Bernstein-Vazirani algorithm, Simon algorithm, Shor's algorithms etc. Quantum parallelism also significantly speeds up the database search algorithm, which is important in computer science becau...

  12. An Improved K-means Clustering Algorithm

    Xiuchang Huang; Wei Su


    An improved k-means clustering algorithm based on K-MEANS algorithm is proposed. This paper gives an improved traditional algorithm by analyzing the statistical data. After a comparison between the actual data and the simulation data, this paper safely shows that the improved algorithm significantly reduce classification error on the simulation data set and the quality of the improved algorithm is much better than K-MEANS algorithm. Such comparative results confirm that the improved algorithm...

  13. A New Page Ranking Algorithm Based On WPRVOL Algorithm

    Roja Javadian Kootenae


    Full Text Available The amount of information on the web is always growing, thus powerful search tools are needed to search for such a large collection. Search engines in this direction help users so they can find their desirable information among the massive volume of information in an easier way. But what is important in the search engines and causes a distinction between them is page ranking algorithm used in them. In this paper a new page ranking algorithm based on "Weighted Page Ranking based on Visits of Links (WPRVOL Algorithm" for search engines is being proposed which is called WPR'VOL for short. The proposed algorithm considers the number of visits of first and second level in-links. The original WPRVOL algorithm takes into account the number of visits of first level in-links of the pages and distributes rank scores based on the popularity of the pages whereas the proposed algorithm considers both in-links of that page (first level in-links and in-links of the pages that point to it (second level in-links in order to calculation of rank of the page, hence more related pages are displayed at the top of search result list. In the summary it is said that the proposed algorithm assigns higher rank to pages that both themselves and pages that point to them be important.

  14. Algorithmic advances in stochastic programming

    Morton, D.P.


    Practical planning problems with deterministic forecasts of inherently uncertain parameters often yield unsatisfactory solutions. Stochastic programming formulations allow uncertain parameters to be modeled as random variables with known distributions, but the size of the resulting mathematical programs can be formidable. Decomposition-based algorithms take advantage of special structure and provide an attractive approach to such problems. We consider two classes of decomposition-based stochastic programming algorithms. The first type of algorithm addresses problems with a ``manageable`` number of scenarios. The second class incorporates Monte Carlo sampling within a decomposition algorithm. We develop and empirically study an enhanced Benders decomposition algorithm for solving multistage stochastic linear programs within a prespecified tolerance. The enhancements include warm start basis selection, preliminary cut generation, the multicut procedure, and decision tree traversing strategies. Computational results are presented for a collection of ``real-world`` multistage stochastic hydroelectric scheduling problems. Recently, there has been an increased focus on decomposition-based algorithms that use sampling within the optimization framework. These approaches hold much promise for solving stochastic programs with many scenarios. A critical component of such algorithms is a stopping criterion to ensure the quality of the solution. With this as motivation, we develop a stopping rule theory for algorithms in which bounds on the optimal objective function value are estimated by sampling. Rules are provided for selecting sample sizes and terminating the algorithm under which asymptotic validity of confidence interval statements for the quality of the proposed solution can be verified. Issues associated with the application of this theory to two sampling-based algorithms are considered, and preliminary empirical coverage results are presented.

  15. Cloud-based Evolutionary Algorithms: An algorithmic study

    Merelo, Juan-J; Mora, Antonio M; Castillo, Pedro; Romero, Gustavo; Laredo, JLJ


    After a proof of concept using Dropbox(tm), a free storage and synchronization service, showed that an evolutionary algorithm using several dissimilar computers connected via WiFi or Ethernet had a good scaling behavior in terms of evaluations per second, it remains to be proved whether that effect also translates to the algorithmic performance of the algorithm. In this paper we will check several different, and difficult, problems, and see what effects the automatic load-balancing and asynchrony have on the speed of resolution of problems.

  16. Scheduling with genetic algorithms

    Fennel, Theron R.; Underbrink, A. J., Jr.; Williams, George P. W., Jr.


    In many domains, scheduling a sequence of jobs is an important function contributing to the overall efficiency of the operation. At Boeing, we develop schedules for many different domains, including assembly of military and commercial aircraft, weapons systems, and space vehicles. Boeing is under contract to develop scheduling systems for the Space Station Payload Planning System (PPS) and Payload Operations and Integration Center (POIC). These applications require that we respect certain sequencing restrictions among the jobs to be scheduled while at the same time assigning resources to the jobs. We call this general problem scheduling and resource allocation. Genetic algorithms (GA's) offer a search method that uses a population of solutions and benefits from intrinsic parallelism to search the problem space rapidly, producing near-optimal solutions. Good intermediate solutions are probabalistically recombined to produce better offspring (based upon some application specific measure of solution fitness, e.g., minimum flowtime, or schedule completeness). Also, at any point in the search, any intermediate solution can be accepted as a final solution; allowing the search to proceed longer usually produces a better solution while terminating the search at virtually any time may yield an acceptable solution. Many processes are constrained by restrictions of sequence among the individual jobs. For a specific job, other jobs must be completed beforehand. While there are obviously many other constraints on processes, it is these on which we focussed for this research: how to allocate crews to jobs while satisfying job precedence requirements and personnel, and tooling and fixture (or, more generally, resource) requirements.

  17. Neighborhood-following algorithms for linear programming

    AI Wenbao


    In this paper, we present neighborhood-following algorithms for linear programming. When the neighborhood is a wide neighborhood, our algorithms are wide neighborhood primal-dual interior point algorithms. If the neighborhood degenerates into the central path, our algorithms also degenerate into path-following algorithms. We prove that our algorithms maintain the O(√nL)-iteration complexity still, while the classical wide neighborhood primal-dual interior point algorithms have only the O(nL)-iteration complexity. We also proved that the algorithms are quadratic convergence if the optimal vertex is nondegenerate. Finally, we show some computational results of our algorithms.

  18. An improved form of the ELMS algorithm

    Gao Ying; Xie Shengli


    ELMS algorithm is the first two-channel adaptive filtering algorithm that takes into account the cross-correlation between the two input signals. The algorithm does not preprocess input signals, so it does not degrade the quality of the speech. However, a lot of computer simulation results show that ELMS algorithm has a bad performance. The ELMS algorithm is analyzed firstly, then a new algorithm is presented by modifying the block matrix used in ELMS algorithm to approximate input signals self-correlation matrix. The computer simulation results indicate that the improved algorithm has a better behavior than the ELMS algorithm.

  19. Multi-Swarm Bat Algorithm

    Ahmed Majid Taha


    Full Text Available In this study a new Bat Algorithm (BA based on multi-swarm technique called the Multi-Swarm Bat Algorithm (MSBA is proposed to address the problem of premature convergence phenomenon. The problem happens when search process converges to non-optimal solution due to the loss of diversity during the evolution process. MSBA was designed with improved ability in exploring new solutions, which was essential in reducing premature convergence. The exploration ability was improved by having a number of sub-swarms watching over the best local optima. In MSBA, when the quality of best local optima does not improve after a pre-defined number of iterations, the population is split equally into several smaller sub-swarms, with one of them remains close to the current best local optima for further exploitation while the other sub-swarms continue to explore for new local optima. The proposed algorithm has been applied in feature selection problem and the results were compared against eight algorithms, which are Ant Colony Optimization (ACO, Genetic Algorithm (GA, Tabu Search (TS, Scatter Search (SS, Great Deluge Algorithm (GDA and stander BA. The results showed that the MSBA is much more effective that it is able to find new best solutions at times when the rest of other algorithms are not able to.

  20. Application of detecting algorithm based on network

    张凤斌; 杨永田; 江子扬; 孙冰心


    Because currently intrusion detection systems cannot detect undefined intrusion behavior effectively,according to the robustness and adaptability of the genetic algorithms, this paper integrates the genetic algorithms into an intrusion detection system, and a detection algorithm based on network traffic is proposed. This algorithm is a real-time and self-study algorithm and can detect undefined intrusion behaviors effectively.

  1. An efficient algorithm for linear programming

    Venkaiah, CH V


    A simple but efficient algorithm is presented for linear programming. The algorithm computes the projection matrix exactly once throughout the computation unlike that of Karmarkar’s algorithm where in the projection matrix is computed at each and every iteration. The algorithm is best suitable to be implemented on a parallel architecture. Complexity of the algorithm is being studied.

  2. A general framework for shortest path algorithms

    W.H.L.M. Pijls (Wim); A.W.J. Kolen


    textabstractIn this paper we present a general framework for shortest path algorithms, including amongst others Dijkstra's algorithm and the A* algorithm. By showing that all algorithms are special cases of one algorithm in which some of the nondeterministic choices are made deterministic, terminati

  3. Solving Scheduling problems using Selective Breeding Algorithm and Hybrid Algorithm

    P.Sriramya; B. Parvathavarthini; M. Chandrasekaran


    The n-job, m-machine scheduling problem is one of the general scheduling problems in a system. Scheduling problems vary widely according to specific production tasks but most are NP-hard problems.Scheduling problems are usually solved using heuristics to get optimal or near optimal solutions because problems found in practical applications cannot be solved to optimality using reasonable resources in many cases. In this paper, Selective Breeding Algorithm (SBA) and Hybrid Algorithm (HA) are us...

  4. Instance-specific algorithm configuration

    Malitsky, Yuri


    This book presents a modular and expandable technique in the rapidly emerging research area of automatic configuration and selection of the best algorithm for the instance at hand. The author presents the basic model behind ISAC and then details a number of modifications and practical applications. In particular, he addresses automated feature generation, offline algorithm configuration for portfolio generation, algorithm selection, adaptive solvers, online tuning, and parallelization.    The author's related thesis was honorably mentioned (runner-up) for the ACP Dissertation Award in 2014,

  5. Complex networks an algorithmic perspective

    Erciyes, Kayhan


    Network science is a rapidly emerging field of study that encompasses mathematics, computer science, physics, and engineering. A key issue in the study of complex networks is to understand the collective behavior of the various elements of these networks.Although the results from graph theory have proven to be powerful in investigating the structures of complex networks, few books focus on the algorithmic aspects of complex network analysis. Filling this need, Complex Networks: An Algorithmic Perspective supplies the basic theoretical algorithmic and graph theoretic knowledge needed by every r



    Tornado codes have been used in the error control of data transmission in IP network. The efficiency of this erasure codes is critically affected by the short cycles in its bipartite graph. To remove this effect,two algorithms are introduced: (1) while generating the graph, the cycle eliminating algorithm is used to reduce the number of the short cycles in it; (2) in the decoding algorithm, cycles that are inevitably in the graph are used to remove decoding efficiency degradation. The simulation results show that they have a better performance than that of general tornado codes.



    This paper presents a new arc flow model for the one-dimensional bin covering problem and an algorithm to solve the problem exactly through a branch-and-bound procedure and the technique of column generation. The subproblems occuring in the procedure of branch-and-bound have the same structure and therefore can be solved by the same algorithm. In order to solve effectively the subproblems which are generally large scale, a column generation algorithm is employed. Many rules found in this paper can improve the performance of the methods.

  8. Cluster algorithms and computational complexity

    Li, Xuenan

    Cluster algorithms for the 2D Ising model with a staggered field have been studied and a new cluster algorithm for path sampling has been worked out. The complexity properties of Bak-Seppen model and the Growing network model have been studied by using the Computational Complexity Theory. The dynamic critical behavior of the two-replica cluster algorithm is studied. Several versions of the algorithm are applied to the two-dimensional, square lattice Ising model with a staggered field. The dynamic exponent for the full algorithm is found to be less than 0.5. It is found that odd translations of one replica with respect to the other together with global flips are essential for obtaining a small value of the dynamic exponent. The path sampling problem for the 1D Ising model is studied using both a local algorithm and a novel cluster algorithm. The local algorithm is extremely inefficient at low temperature, where the integrated autocorrelation time is found to be proportional to the fourth power of correlation length. The dynamic exponent of the cluster algorithm is found to be zero and therefore proved to be much more efficient than the local algorithm. The parallel computational complexity of the Bak-Sneppen evolution model is studied. It is shown that Bak-Sneppen histories can be generated by a massively parallel computer in a time that is polylog in the length of the history, which means that the logical depth of producing a Bak-Sneppen history is exponentially less than the length of the history. The parallel dynamics for generating Bak-Sneppen histories is contrasted to standard Bak-Sneppen dynamics. The parallel computational complexity of the Growing Network model is studied. The growth of the network with linear kernels is shown to be not complex and an algorithm with polylog parallel running time is found. The growth of the network with gamma ≥ 2 super-linear kernels can be realized by a randomized parallel algorithm with polylog expected running time.

  9. An investigation of genetic algorithms

    Genetic algorithms mimic biological evolution by natural selection in their search for better individuals within a changing population. they can be used as efficient optimizers. This report discusses the developing field of genetic algorithms. It gives a simple example of the search process and introduces the concept of schema. It also discusses modifications to the basic genetic algorithm that result in species and niche formation, in machine learning and artificial evolution of computer programs, and in the streamlining of human-computer interaction. (author). 3 refs., 1 tab., 2 figs

  10. Subcubic Control Flow Analysis Algorithms

    Midtgaard, Jan; Van Horn, David

    We give the first direct subcubic algorithm for performing control flow analysis of higher-order functional programs. Despite the long held belief that inclusion-based flow analysis could not surpass the ``cubic bottleneck, '' we apply known set compression techniques to obtain an algorithm...... that runs in time O(n^3/log n) on a unit cost random-access memory model machine. Moreover, we refine the initial flow analysis into two more precise analyses incorporating notions of reachability. We give subcubic algorithms for these more precise analyses and relate them to an existing analysis from...

  11. A filtered backprojection algorithm with characteristics of the iterative landweber algorithm

    L. Zeng, Gengsheng


    Purpose: In order to eventually develop an analytical algorithm with noise characteristics of an iterative algorithm, this technical note develops a window function for the filtered backprojection (FBP) algorithm in tomography that behaves as an iterative Landweber algorithm.

  12. Enhanced Segment Compression Steganographic Algorithm



    Full Text Available Steganography is the science and art of concealing messages using techniques that allow only the sender and receiver to know of the message?s existence and be able to decipher it. In this article, we would like to present a new steganographic technique for concealing digital images: the Enhanced Segment Compression Steganographic Algorithm (ESCSA. We start by mentioning several desired properties that we have taken into consideration for our algorithm. Next, we define some quality metrics with which we can measure how well / to what extent those properties are achieved. A detailed presentation of the component parts of the algorithm follows, accompanied by quantitative analyses of parameters of interest. Finally, we discuss the strengths and weaknesses of our algorithm. In addition, we make a few suggestions regarding possible further refinements of the ESCSA.

  13. Algorithmic Cheminformatics (Dagstuhl Seminar 14452)

    Banzhaf, Wolfgang; Flamm, Christoph; Merkle, Daniel;


    Dagstuhl Seminar 14452 “Algorithmic Cheminformatics” brought together leading researchers from both chemistry and computer science. The meeting successfully aimed at bridging in the apparent gap between the two disciplines. The participants surveyed areas of overlapping interests and identified...

  14. Fluid-structure-coupling algorithm

    A fluid-structure-interaction algorithm has been developed and incorporated into the two dimensional code PELE-IC. This code combines an Eulerian incompressible fluid algorithm with a Lagrangian finite element shell algorithm and incorporates the treatment of complex free surfaces. The fluid structure, and coupling algorithms have been verified by the calculation of solved problems from the literature and from air and steam blowdown experiments. The code has been used to calculate loads and structural response from air blowdown and the oscillatory condensation of steam bubbles in water suppression pools typical of boiling water reactors. The techniques developed here have been extended to three dimensions and implemented in the computer code PELE-3D

  15. Planar graphs theory and algorithms

    Nishizeki, T


    Collected in this volume are most of the important theorems and algorithms currently known for planar graphs, together with constructive proofs for the theorems. Many of the algorithms are written in Pidgin PASCAL, and are the best-known ones; the complexities are linear or 0(nlogn). The first two chapters provide the foundations of graph theoretic notions and algorithmic techniques. The remaining chapters discuss the topics of planarity testing, embedding, drawing, vertex- or edge-coloring, maximum independence set, subgraph listing, planar separator theorem, Hamiltonian cycles, and single- or multicommodity flows. Suitable for a course on algorithms, graph theory, or planar graphs, the volume will also be useful for computer scientists and graph theorists at the research level. An extensive reference section is included.

  16. Fibonacci Numbers and Computer Algorithms.

    Atkins, John; Geist, Robert


    The Fibonacci Sequence describes a vast array of phenomena from nature. Computer scientists have discovered and used many algorithms which can be classified as applications of Fibonacci's sequence. In this article, several of these applications are considered. (PK)

  17. Aggregation Algorithms in Heterogeneous Tables

    Titus Felix FURTUNA; Ivan, Ion; Marian DARDALA


    The heterogeneous tables are most used in the problem of aggregation. A solution for this problem is to standardize these tables of figures. In this paper, we proposed some methods of aggregation based on the hierarchical algorithms.

  18. Efficient Algorithms for Subgraph Listing

    Niklas Zechner


    Full Text Available Subgraph isomorphism is a fundamental problem in graph theory. In this paper we focus on listing subgraphs isomorphic to a given pattern graph. First, we look at the algorithm due to Chiba and Nishizeki for listing complete subgraphs of fixed size, and show that it cannot be extended to general subgraphs of fixed size. Then, we consider the algorithm due to Ga̧sieniec et al. for finding multiple witnesses of a Boolean matrix product, and use it to design a new output-sensitive algorithm for listing all triangles in a graph. As a corollary, we obtain an output-sensitive algorithm for listing subgraphs and induced subgraphs isomorphic to an arbitrary fixed pattern graph.

  19. Quasigroup based crypto-algorithms

    Shcherbacov, Victor


    Modifications of Markovski quasigroup based crypto-algorithm have been proposed. Some of these modifications are based on the systems of orthogonal n-ary groupoids. T-quasigroups based stream ciphers have been constructed.

  20. Designing algorithms using CAD technologies



    Full Text Available A representative example of eLearning-platform modular application, ‘Logical diagrams’, is intended to be a useful learning and testing tool for the beginner programmer, but also for the more experienced one. The problem this application is trying to solve concerns young programmers who forget about the fundamentals of this domain, algorithmic. Logical diagrams are a graphic representation of an algorithm, which uses different geometrical figures (parallelograms, rectangles, rhombuses, circles with particular meaning that are called blocks and connected between them to reveal the flow of the algorithm. The role of this application is to help the user build the diagram for the algorithm and then automatically generate the C code and test it.

  1. Kernel Generalized Noise Clustering Algorithm

    WU Xiao-hong; ZHOU Jian-jiang


    To deal with the nonlinear separable problem, the generalized noise clustering (GNC) algorithm is extended to a kernel generalized noise clustering (KGNC) model. Different from the fuzzy c-means (FCM) model and the GNC model which are based on Euclidean distance, the presented model is based on kernel-induced distance by using kernel method. By kernel method the input data are nonlinearly and implicitly mapped into a high-dimensional feature space, where the nonlinear pattern appears linear and the GNC algorithm is performed. It is unnecessary to calculate in high-dimensional feature space because the kernel function can do itjust in input space. The effectiveness of the proposed algorithm is verified by experiments on three data sets. It is concluded that the KGNC algorithm has better clustering accuracy than FCM and GNC in clustering data sets containing noisy data.

  2. Cascade Error Projection: A New Learning Algorithm

    Duong, T. A.; Stubberud, A. R.; Daud, T.; Thakoor, A. P.


    A new neural network architecture and a hardware implementable learning algorithm is proposed. The algorithm, called cascade error projection (CEP), handles lack of precision and circuit noise better than existing algorithms.

  3. Multicore Processing for Clustering Algorithms



    Full Text Available Data Mining algorithms such as classification and clustering are the future of computation, though multidimensional data-processing is required. People are using multicore processors with GPU’s. Most of the programming languages doesn’t provide multiprocessing facilities and hence wastage of processing resources. Clustering and classification algorithms are more resource consuming. In this paper we have shown strategies to overcome such deficiencies using multicore processing platform OpelCL.

  4. Subsampling algorithms for semidefinite programming

    Alexandre W. d'Aspremont


    Full Text Available We derive a stochastic gradient algorithm for semidefinite optimization using randomization techniques. The algorithm uses subsampling to reduce the computational cost of each iteration and the subsampling ratio explicitly controls granularity, i.e. the tradeoff between cost per iteration and total number of iterations. Furthermore, the total computational cost is directly proportional to the complexity (i.e. rank of the solution. We study numerical performance on some large-scale problems arising in statistical learning.

  5. A Survey on Mining Algorithms

    Patel Nimisha; Prof. Sheetal Mehta


    Data mining is a process that discover the knowledge or hidden pattern from large databases. In the large database using association rules throughfind meaningful relationship between large amount of itemsets and this itemset through create frequent itemset. Association rule mining is the most paramount application in the large database. Most of the Association rule mining algorithm are improved and derivative. The traditional algorithms scan databases many times so, time complexity and space ...

  6. Neutronic rebalance algorithms for SIMMER

    Four algorithms to solve the two-dimensional neutronic rebalance equations in SIMMER are investigated. Results of the study are presented and indicate that a matrix decomposition technique with a variable convergence criterion is the best solution algorithm in terms of accuracy and calculational speed. Rebalance numerical stability problems are examined. The results of the study can be applied to other neutron transport codes which use discrete ordinates techniques

  7. Algorithms to identify failure pattern

    Poudel, Bhuwan Krishna Som


    This project report was written for ?Algorithms to Identify Failure Pattern? at NTNU (Norwegian University of Science and Technology), IME (Faculty of Information Technology, Mathematics and Electrical Engineering) and IDI (Department of Computer Science).In software application, there are three types of failure pattern: point pattern, block pattern and stripe pattern. The purpose of the report is to prepare an algorithm that identifies the pattern in a software application. Only theoretical ...

  8. Behavior Classification Algorithms at Intersections

    Aoude, Georges; Desaraju, Vishnu Rajeswar; Stephens, Lauren H.; How, Jonathan P.


    The ability to classify driver behavior lays the foundation for more advanced driver assistance systems. Improving safety at intersections has also been identified as high priority due to the large number of intersection related fatalities. This paper focuses on developing algorithms for estimating driver behavior at road intersections. It introduces two classes of algorithms that can classify drivers as compliant or violating. They are based on 1) Support Vector Machines (SVM) and 2) Hidden ...

  9. Stochastic approximation algorithms and applications

    Kushner, Harold J


    In recent years algorithms of the stochastic approximation type have found applications in new and diverse areas, and new techniques have been developed for proofs of convergence and rate of convergence. The actual and potential applications in signal processing have exploded. New challenges have arisen in applications to adaptive control. This book presents a thorough coverage of the ODE method used to analyze these algorithms.

  10. A memetic fingerprint matching algorithm

    Sheng, Weiguo; Howells, Gareth; Fairhurst, Michael; Deravi, Farzin


    Minutiae point pattern matching is the most common approach for fingerprint verification. Although many minutiae point pattern matching algorithms have been proposed, reliable automatic fingerprint verification remains as a challenging problem, both with respect to recovering the optimal alignment and the construction of an adequate matching function. In this paper, we develop a memetic fingerprint matching algorithm (MFMA) which aims to identify the optimal or near optimal global matching, b...

  11. Multicore Processing for Clustering Algorithms

    RekhanshRao; Kapil Kumar Nagwanshi; SipiDubey


    Data Mining algorithms such as classification and clustering are the future of computation, though multidimensional data-processing is required. People are using multicore processors with GPU’s. Most of the programming languages doesn’t provide multiprocessing facilities and hence wastage of processing resources. Clustering and classification algorithms are more resource consuming. In this paper we have shown strategies to overcome such deficiencies using multicore processing platform OpelCL....

  12. CPU Scheduling Algorithms: A Survey

    Imran Qureshi


    Scheduling is the fundamental function of operating system. For scheduling, resources of system shared among processes which are going to be executed. CPU scheduling is a technique by which processes are allocating to the CPU for a specific time quantum. In this paper the review of different scheduling algorithms are perform with different parameters, such as running time, burst time and waiting times etc. The reviews algorithms are first come first serve, Shortest Job First, Round Robin, ...

  13. Communication Complexity (for Algorithm Designers)

    Roughgarden, Tim


    This document collects the lecture notes from my course "Communication Complexity (for Algorithm Designers),'' taught at Stanford in the winter quarter of 2015. The two primary goals of the course are: 1. Learn several canonical problems that have proved the most useful for proving lower bounds (Disjointness, Index, Gap-Hamming, etc.). 2. Learn how to reduce lower bounds for fundamental algorithmic problems to communication complexity lower bounds. Along the way, we'll also: 3. Get exposure t...

  14. Simple Algorithm Portfolio for SAT

    Nikolic, Mladen; Maric, Filip; Janicic, Predrag


    The importance of algorithm portfolio techniques for SAT has long been noted, and a number of very successful systems have been devised, including the most successful one --- SATzilla. However, all these systems are quite complex (to understand, reimplement, or modify). In this paper we propose a new algorithm portfolio for SAT that is extremely simple, but in the same time so efficient that it outperforms SATzilla. For a new SAT instance to be solved, our portfolio finds its k-nearest neighb...

  15. Distributed systems an algorithmic approach

    Ghosh, Sukumar


    Most applications in distributed computing center around a set of common subproblems. Distributed Systems: An Algorithmic Approach presents the algorithmic issues and necessary background theory that are needed to properly understand these challenges.   Achieving a balance between theory and practice, this book bridges the gap between theoreticians and practitioners. With a set of exercises featured in each chapter, the book begins with background information that contains various interprocess communication techniques and middleware services, followed by foundational topics that cover system

  16. Atrial Fibrillation and Pacing Algorithms

    Terranova, Paolo; Severgnini, Barbara; Valli, Paolo; Dell'Orto, Simonetta; Greco, Enrico Maria


    Pacing prevention algorithms have been introduced in order to maximize the benefits of atrial pacing in atrial fibrillation prevention. It has been demonstrated that algorithms actually keep overdrive atrial pacing, reduce atrial premature contractions, and prevent short-long atrial cycle phenomenon, with good patient tolerance. However, clinical studies showed inconsistent benefits on clinical endpoints such as atrial fibrillation burden. Factors which may be responsible for neutral results ...

  17. Evolutionary Algorithms in Astronautic Applications

    Maiwald, Volker


    Evolutionary algorithms (EA) are a computation tool that utilizes biological principles found in the evolution theory. One major difference to other optimization methods is the fact that a large group of solutions is evaluated, not a single one. Combination of various solutions from such a group, called population, allows improvement of the solutions. Overall several terms in usage in the field of evolutionary algorithms have their origin in genetics or biology, especially the ...

  18. Evolutionary Algorithms for Reinforcement Learning

    Grefenstette, J. J.; Moriarty, D. E.; Schultz, A.C.


    There are two distinct approaches to solving reinforcement learning problems, namely, searching in value function space and searching in policy space. Temporal difference methods and evolutionary algorithms are well-known examples of these approaches. Kaelbling, Littman and Moore recently provided an informative survey of temporal difference methods. This article focuses on the application of evolutionary algorithms to the reinforcement learning problem, emphasizing alternative policy represe...

  19. Evolutionary algorithms in multiobjective problems

    Syomkin, A. M.; Zmitrovich, A. I.


    Many real-world problems involve two types of difficulties: 1) multiple, conflicting objectives and 2) a highly complex search space. Efficient evolutionary strategies have been developed to deal with both types of difficulties. Evolutionary algorithms possess several characteristics such as parallelism and robustness that make them preferable to classical optimization methods. In this work 1 conducted comparative studies among the well-known evolutionary algorithms based on NP-hard 0-1 multi...

  20. The CEMS IV OAP algorithm

    Larson, Harold J.; Jayachandran, Toke.


    The Comprehensive Engine Management System (CEMS) Phase IV, will provide real time data analysis capability for all Air Force oil analysis laboratories. This paper describes the statistical algorithm used by this system to aid the oil analysis technician in making his recommendations. The algorithm incorporates usage and oil consumption variables, and employs least squares to minimize the effects of the random errors in the spectrometer readings. Prepared for: Directorate of M...

  1. Clustering algorithm incorporating density and direction

    Song, Yu-Chen; O'Grady, Michael J; O'Hare, G. M. P.; Wang, Wei


    This paper analyses the advantages and disadvantages of the K-means algorithm and the DENCLUE algorithm. In order to realise the automation of clustering analysis and eliminate human factors, both partitioning and density-based methods were adopted, resulting in a new algorithm – Clustering Algorithm based on object Density and Direction (CADD). This paper discusses the theory and algorithm design of the CADD algorithm. As an illustration of its applicability, CADD was used to cluster r...

  2. United assembly algorithm for optical burst switching

    Jinhui Yu(于金辉); Yijun Yang(杨教军); Yuehua Chen(陈月华); Ge Fan(范戈)


    Optical burst switching (OBS) is a promising optical switching technology. The burst assembly algorithm controls burst assembly, which significantly impacts performance of OBS network. This paper provides a new assembly algorithm, united assembly algorithm, which has more practicability than conventional algorithms. In addition, some factors impacting selections of parameters of this algorithm are discussed and the performance of this algorithm is studied by computer simulation.

  3. Constructing a Scheduling Algorithm For Multidirectional Elevators

    Edlund, Joakim; Berntsson, Fredrik


    With this thesis we aim to create an efficient scheduling algorithm for elevators that can move in multiple directions and establish if and when the algorithm is efficient in comparison to algorithms constructed for traditional elevator algorithms. To measure efficiency, a simulator is constructed to simulate an elevator system implementing different algorithms. Because of the challenge of constructing a simulator and since we did not find either a simulator nor any algorithms for use in mult...

  4. Improved Apriori Algorithm for Mining Association Rules

    Darshan M. Tank


    Association rules are the main technique for data mining. Apriori algorithm is a classical algorithm of association rule mining. Lots of algorithms for mining association rules and their mutations are proposed on basis of Apriori algorithm, but traditional algorithms are not efficient. For the two bottlenecks of frequent itemsets mining: the large multitude of candidate 2- itemsets, the poor efficiency of counting their support. Proposed algorithm reduces one redundant pruning operations of C...

  5. Some tactical algorithms for spherical geometry

    Shudde, Rex H.


    This report presents two great circle navigation algorithms, a closest point of approach algorithm and intercept algorithm. It also presents an implementation program for the algorithms that is written in the BASIC language for an IBM PC. Instead of using classical spherical geometry or the general spherical triangle, these algorithms incorporate rectangular coordinates and vectors on the surface of the spherical. The intent of the report is to provide algorithms for spherical earth models th...

  6. Efficient iterative adaptive pole placement algorithm

    李俊民; 李靖; 杨磊


    An iterative adaptive pole placement algorithm is presented. The stability and the convergence of the algorithm are respectively established. Since one-step iterative formulation in computing controller's parameters is used, the on-line computation cost is greatly reduced with respected to the traditional algorithm. The algorithm with the feed-forward can follow arbitrarily bounded output. The algorithm is also extended to multivariate case. Simulation examples show the efficiency and robustness of the algorithm.

  7. Swarm Intelligence Based Algorithms: A Critical Analysis

    Yang, Xin-She


    Many optimization algorithms have been developed by drawing inspiration from swarm intelligence (SI). These SI-based algorithms can have some advantages over traditional algorithms. In this paper, we carry out a critical analysis of these SI-based algorithms by analyzing their ways to mimic evolutionary operators. We also analyze the ways of achieving exploration and exploitation in algorithms by using mutation, crossover and selection. In addition, we also look at algorithms using dynamic sy...

  8. Multithreaded Algorithms for Graph Coloring

    Catalyurek, Umit V.; Feo, John T.; Gebremedhin, Assefaw H.; Halappanavar, Mahantesh; Pothen, Alex


    Graph algorithms are challenging to parallelize when high performance and scalability are primary goals. Low concurrency, poor data locality, irregular access pattern, and high data access to computation ratio are among the chief reasons for the challenge. The performance implication of these features is exasperated on distributed memory machines. More success is being achieved on shared-memory, multi-core architectures supporting multithreading. We consider a prototypical graph problem, coloring, and show how a greedy algorithm for solving it can be e*ectively parallelized on multithreaded architectures. We present in particular two di*erent parallel algorithms. The first relies on speculation and iteration, and is suitable for any shared-memory, multithreaded system. The second uses data ow principles and is targeted at the massively multithreaded Cray XMT system. We benchmark the algorithms on three di*erent platforms and demonstrate scalable runtime performance. In terms of quality of solution, both algorithms use nearly the same number of colors as the serial algorithm.


    Coimbatore Ganeshsankar Balaji


    Full Text Available There are many applications which require complete coverage and obstacle avoidance. The classical A* algorithm provides the user a shortest path by avoiding the obstacle. As well, the Dijkstra’s algorithm finds the shortest path between the source and destination. But in many applications we require complete coverage of the proposed area with obstacle avoidance. There are LSP, LSSP, BSA, spiral-STC and Complete Coverage D* algorithms which do not realize complete (100% coverage. The complete coverage using a critical point algorithm assures complete coverage, but it is not well suited for applications like mine detection. Also for covering the missed region it keeps the obstacle as a critical point which is not advisable in critical applications where obstacle may be a dangerous one. To overcome this and to achieve the complete coverage we propose a novel graph traversal algorithm Traversal Algorithm for Complete Coverage (TRACC. Here the area to be scanned is decomposed into a finite number of cells. The traversal is done through all the cells after making sure the next cell has no obstacle. TRACC assures thorough coverage of the proposed area and ensuring that all the obstacles are avoided. Hence the TRACC always have the safer path while covering the entire area. It also reports the obstacle placed or blocked cell.

  10. Recursive Branching Simulated Annealing Algorithm

    Bolcar, Matthew; Smith, J. Scott; Aronstein, David


    This innovation is a variation of a simulated-annealing optimization algorithm that uses a recursive-branching structure to parallelize the search of a parameter space for the globally optimal solution to an objective. The algorithm has been demonstrated to be more effective at searching a parameter space than traditional simulated-annealing methods for a particular problem of interest, and it can readily be applied to a wide variety of optimization problems, including those with a parameter space having both discrete-value parameters (combinatorial) and continuous-variable parameters. It can take the place of a conventional simulated- annealing, Monte-Carlo, or random- walk algorithm. In a conventional simulated-annealing (SA) algorithm, a starting configuration is randomly selected within the parameter space. The algorithm randomly selects another configuration from the parameter space and evaluates the objective function for that configuration. If the objective function value is better than the previous value, the new configuration is adopted as the new point of interest in the parameter space. If the objective function value is worse than the previous value, the new configuration may be adopted, with a probability determined by a temperature parameter, used in analogy to annealing in metals. As the optimization continues, the region of the parameter space from which new configurations can be selected shrinks, and in conjunction with lowering the annealing temperature (and thus lowering the probability for adopting configurations in parameter space with worse objective functions), the algorithm can converge on the globally optimal configuration. The Recursive Branching Simulated Annealing (RBSA) algorithm shares some features with the SA algorithm, notably including the basic principles that a starting configuration is randomly selected from within the parameter space, the algorithm tests other configurations with the goal of finding the globally optimal