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IEEE International conference on Systems, Man, and Cybernetics (SMC)

作者： Okamoto, Takashi Hirata, Hironori Chiba Univ Grad Sch Engn Chiba Japan

ISBN: (纸本)9781479906529

The chaotic optimization method is a global optimization method to solve unconstrained optimization problems. Its superior search capability has been confirmed through applications to optimization problems which have high dimensional and multi-peaked objective functions. However, the chaotic optimization method has a drawback in that a parameter called initial sampling parameter has to be tuned on each optimization problem. In this study, we consider two approaches to perform the chaotic optimization method without the initial sampling parameter tuning. One is the initial sampling estimation method based on the characteristics of the chaotic optimization method. In this method, the sampling parameter at which the search trajectory bifurcates from the fixed point convergence to the two periodic solution trajectory, which is called the first bifurcating parameter, is computed from the approximation matrix to Hessian obtained by the quasi-Newton method. Then, suitable initial sampling parameter is estimated using this information. The other is the descent sign vector method. In this method, a descent sign vector extracted from a gradient vector is used as a search direction of a search point. Then, its moving distance is determined so that its search process shifts from the global search to the local search. We compare two methods and their hybrid method through numerical experiments for various optimization problems.

关键词： Globa Optimization Nonlinear Optimization Chaos Parameter Tuning linear Stability theory

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Approximation Algorithms for Complex Systems: Proceedings of the 6th International conference on Algorithms for Approximation, Ambleside, UK, 31st ... 2009

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2013年

作者： Emmanuil H Georgoulis Armin Iske Jeremy Levesley

ISBN: (纸本)9783642266652

This book collects up-to-date papers from world experts in a broad variety of relevant applications of approximation theory, including dynamical systems, multiscale modelling of fluid flow, metrology, and geometric modelling to mention a few. The 14 papers in this volume document modern trends in approximation through recent theoretical developments, important computational aspects and multidisciplinary applications. The book is arranged in seven invited surveys, followed by seven contributed research papers. The surveys of the first seven chapters are addressing the following relevant topics: emergent behaviour in large electrical networks, algorithms for multivariate piecewise constant approximation, anisotropic triangulation methods in adaptive image approximation, form assessment in coordinate metrology, discontinuous Galerkin methods for linear problems, a numerical analyst's view of the lattice Boltzmann method, approximation of probability measures on manifolds. Moreover, the diverse contributed papers of the remaining seven chapters reflect recent developments in approximation theory, approximation practice and their applications. Graduate students who wish to discover the state of the art in a number of important directions of approximation algorithms will find this a valuable volume. Established researchers from statisticians through to fluid modellers will find interesting new approaches to solving familiar but challenging problems. This book grew out of the sixth in the conference series on "Algorithms for Approximation", which took place from 31st August to September 4th 2009 in Ambleside in the Lake District of the United Kingdom.

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The forward stem matrix: An efficient data structure for finding hairpins in RNA secondary structures 13

The forward stem matrix: An efficient data structure for fin...

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2013 4th ACM conference on Bioinformatics, Computational Biology and Biomedical Informatics, ACM-BCB 2013

作者： Beal, Richard Adjeroh, Donald Abbasi, Ahmed West Virginia University Morgantown WV 26506 United States University of Virginia Charlottesville VA 22903 United States

ISBN: (纸本)9781450324342

With the rapid growth in available genomic data, robust and efficient methods for identifying RNA secondary structure elements, such as hairpins, have become a significant challenge in computational biology, with potential applications in prediction of RNA secondary and tertiary structures, functional classification of RNA structures, micro RNA target prediction, and discovery of RNA structure motifs. In this work, we propose the Forward Stem Matrix (FSM), a data structure to efficiently represent all k-length stem options, for k 2 K, within an n-length RNA sequence T. We show that the FSM structure is of size O(njKj) and still permits efficient access to stems. In this paper, we provide a linear O(njKj) construction for the FSM using suffix arrays and data structures related to the Longest Previous Factor (LPF), namely, the Furthest Previous Non- Overlapping Factor (FPnF) and Furthest Previous Factor (FPF) arrays. We also provide new constructions for the FPnF and FPF via a novel application of parameterized string (p-string) theory and suffix trees. As an application of the FSM, we show how to efficiently find all hairpin structures in an RNA sequence. Experimental results show the practical performance of the proposed data structures. Copyright © 2007 by the Association for Computing Machinery.

关键词： Matrix algebra

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Energy-efficient sparse matrix autotuning with CSX-A trade-off study

Energy-efficient sparse matrix autotuning with CSX-A trade-o...

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2013 IEEE 37th Annual Computer Software and applications conference, COMPSAC 2013

作者： Meyer, Jan Christian Cebrian, Juan Manuel Natvig, Lasse Karakasis, Vasileios Siakavaras, Dimitris Nikas, Konstantinos HPC Section IT Dept. NTNU Norway NTNU Norway School of ECE NTUA Greece

ISBN: (纸本)9780769549798

In this paper, we apply a method for extracting a running power estimate of applications from hardware performance counters, producing power/time curves which can be integrated over particular intervals to estimate the energy consumption of individual application stages. We use this method to instrument executions of a conjugate gradient solver, to examine the energy and performance impacts of applying the Compressed Sparse eXtended (CSX) and classic Compressed Sparse Row (CSR) matrix compression methods to sparse linear systems from different application areas. The CSX format requires a preprocessing stage which identifies and exploits a range of matrix substructures, incurring a one-time cost which can facilitate more effective sparse matrix-vector multiplication (SpMV). As this numerical kernel is the primary performance bottleneck of conjugate gradient solvers, we take the approach of isolating the energy cost of preprocessing from a short sample of application iterations, obtaining measurements which enlighten the choice of which compression scheme is more appropriate to the input data. We examine the impact variable degrees of parallelism, processor clock frequency, and Hyper threading have on this trade-off. Our results include comparisons of empirically obtained results from all combinations of up to 8 threads on 4 hyper threaded cores, 3 clock frequencies, and 5 sample application matrices. We assess program-hardware interactions with views to structural properties of the data and hardware architectural features, and evaluate the approach with respect to integrating the energy instrumentation with present automatic performance tuning. Results show that our method is sufficiently precise to identify non-trivial tradeoffs in the parameter space, and may become suitable for a run-time automatic tuning scheme by applying a faster preprocessing mode of CSX. © 2013 IEEE.

关键词： Matrix algebra

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Analyzing Hogwild Parallel Gaussian Gibbs Sampling 13

Analyzing Hogwild Parallel Gaussian Gibbs Sampling

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Annual conference on Neural Information Processing Systems

作者： Matthew J. Johnson James Saunderson Alan S. Willsky EECS MIT

ISBN: (纸本)9781632660244

Sampling inference methods are computationally difficult to scale for many models in part because global dependencies can reduce opportunities for parallel computation. Without strict conditional independence structure among variables, standard Gibbs sampling theory requires sample updates to be performed sequentially, even if dependence between most variables is not strong. Empirical work has shown that some models can be sampled effectively by going "Hogwild" and simply running Gibbs updates in parallel with only periodic global communication, but the successes and limitations of such a strategy are not well understood. As a step towards such an understanding, we study the Hogwild Gibbs sampling strategy in the context of Gaussian distributions. We develop a framework which provides convergence conditions and error bounds along with simple proofs and connections to methods in numerical linear algebra. In particular, we show that if the Gaussian precision matrix is generalized diagonally dominant, then any Hogwild Gibbs sampler, with any update schedule or allocation of variables to processors, yields a stable sampling process with the correct sample mean.

关键词： Gibbs Sampling Variable Gaussian distribution Sample mean linear algebra PARALLEL PROCESSING (COMPUTERS) Parallel Lines PROCESSOR Gauss Connections Steps

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Control of linear discrete-time systems by quantised feedback

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IET CONTROL theory AND applications 2012年第13期6卷 2095-2102页

作者： Mahmoud, M. S. King Fahd Univ Petr & Minerals Dept Syst Engn Dhahran 31261 Saudi Arabia

In this study the authors, we investigate the problem of designing quantised H-infinity feedback control for a class of discrete-time systems with and without bounded uncertainties. The quantiser has arbitrary form. A linear matrix inequality-based method using static and dynamic quantised output-feedback controller are designed to render the closed-loop system asymptotically stable with guaranteed gamma-level. The authors illustrate the theoretical developments by numerical simulations.

关键词： H∞ control linear matrix inequality-based method numerical simulations linear discrete-time systems Stability in control theory control system synthesis Control system analysis and synthesis methods static quantised output-feedback controller numerical analysis closed loop systems linear systems feedback linear algebra (numerical analysis) quantised H∞ feedback control dynamic quantised output-feedback controller γ-level Optimal control linear matrix inequalities Discrete control systems closed-loop system bounded uncertainties discrete time systems asymptotic stability

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Computing the Camion's multivariate BCH bound

Computing the Camion's multivariate BCH bound

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IEEE (first ITW conference of the year) Information theory Workshop (ITW)

作者： José Joaquín Bernal Diana H. Bueno-Carreño Juan Jacobo Simón Departamento de Matemáticas Universidad de Murcia Murcia Spain Departamento de CN y Matemáticas Pontificia Universidad Javeriana-Cali Cali Colombia

The P. Camion's apparent distance of an abelian code is a generalization of the notion of the BCH bound of cyclic codes [3]. In this work, we present a method of computation of the apparent distance in multivariate abelian codes, based on manipulations of hypermatrices. Our algorithm needs fewer computations than any other, up to our knowledge; in fact, in the case of two dimensional abelian codes it has linear complexity. We give two applications. First, we construct abelian codes that multiply the dimension of a given cyclic code and equal its BCH bound. The second one is an approximation to a notion of BCH multivariate code.

关键词： Zirconium Polynomials Frequency modulation Complexity theory Approximation methods algebra Fourier transforms

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Finite-time stochastic stability and stabilisation of linear discrete-time Markovian jump systems with partly unknown transition probabilities

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IET CONTROL theory AND applications 2012年第10期6卷 1522-1526页

作者： Zuo, Z. Liu, Y. Wang, Y. Li, H. Tianjin Univ Tianjin Key Lab Proc Measurement & Control Sch Elect Engn & Automat Tianjin 300072 Peoples R China

This study deals with the problems of finite-time stochastic stability and stabilisation with partly unknown transition probabilities for linear discrete-time Markovian jump systems (MJP). The definition of finite-time stochastic stability for discrete-time MJP is firstly given. Based on it, a sufficient condition is proposed to guarantee that the state of the system does not exceed a certain threshold in mean-square sense during a specified time interval. The above result is then extended to the finite-time stochastic stabilisation case. By introducing some free-weighting matrices, the proposed method leads to less conservatism compared with the existing ones. Two numerical examples are given to illustrate the effectiveness of the proposed methods.

关键词： stochastic systems probability mean-square sense linear discrete-time Markovian jump system stabilization Time-varying control systems stability Stability in control theory matrix algebra free-weighting matrices linear systems linear algebra (numerical analysis) partly unknown transition probabilities Interpolation and function approximation (numerical analysis) mean square error methods discrete-time MJP finite-time stochastic stability Discrete control systems Markov processes discrete time systems

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Consensus of second-order multi-agent systems via sampled control: undirected fixed topology case

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IET CONTROL theory AND applications 2012年第7期6卷 893-899页

作者： Wang, S. Xie, D. Tianjin Univ Dept Math Sch Sci Tianjin 300072 Peoples R China

This article studies the average-consensus problem of second-order multi-agent sampled control system with time-varying sampling interval. The sampled control protocol is induced from continuous-time linear consensus protocol by using sampling technology and zero-order hold circuit. First, by the iterative method, the continuous-time multi-agent system can be equivalently transformed into a linear discrete-time system. Then, by algebraic graph theory and matrix theory, the authors analyse the convergence of the system and not only prove the existence of the scaling parameter and time-varying sampling interval but also give the approach of how to choose them. In their study, the eigenvalues of the corresponding Laplacian matrix play a key role in reaching consensus. Finally, an example is provided to illustrate the effectiveness of the theoretical results.

关键词： continuous-time multiagent system Robotics multi-agent systems algebraic graph theory iterative method Combinatorial mathematics time-varying systems Time-varying control systems matrix algebra sampling technology linear systems multi-robot systems second-order multiagent sampled control system eigenvalues and eigenfunctions graph theory zero-order hold circuit linear algebra (numerical analysis) second-order multiagent system consensus Interpolation and function approximation (numerical analysis) continuous time systems Discrete control systems iterative methods continuous-time linear consensus protocol time-varying sampling interval average-consensus problem linear discrete-time system matrix theory sampled control protocol Laplacian matrix eigenvalues sampled data systems undirected fixed topology case discrete time systems

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Solving periodic Lyapunov matrix equations via finite steps iteration

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IET CONTROL theory AND applications 2012年第13期6卷 2111-2119页

作者： Cai, G. -B. Hu, C. -H. Xian Res Inst High Tech Dept Automat Xian 710025 Shaanxi Peoples R China

Periodic Lyapunov matrix equations play important roles in stability analysis and stabilisation of discrete-time periodic systems. In this paper, an iterative algorithm for solving periodic Lyapunov matrix equations is established. It is shown that the proposed iteration converges to the unique solution of the considered matrix equations at finite steps with arbitrary initial condition despite the stability of the associated discrete-time periodic linear systems. Both the reverse-time and forward-time discrete-time periodic Lyapunov matrix equations are considered. numerical examples are worked out to illustrate the effectiveness of the proposed algorithm.

关键词： reverse-time discrete-time periodic Lyapunov matrix equations Time-varying control systems stability Stability in control theory stability analysis iteration convergence Lyapunov matrix equations linear systems discrete-time periodic linear system linear algebra (numerical analysis) convergence arbitrary initial condition Interpolation and function approximation (numerical analysis) forward-time discrete-time periodic Lyapunov matrix equations discrete-time periodic system Optimal control iterative methods Discrete control systems stabilisation iterative algorithm finite steps iteration numerical example discrete time systems periodic control

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