Modified multiscale permutation entropy algorithm and its application for multiscroll chaotic systems

To analyze the complexity of continuous chaotic systems better, the modified multiscale permutation entropy (MMPE) algorithm is proposed. Characteristics and parameter choices of the MMPE algorithm are investigated. The comparative study between MPE and MMPE shows that MMPE has better robustness for identifying different chaotic systems when the scale factor τ takes large values. Compared with MPE, MMPE algorithm is more suitable for analyzing the complexity of time series as it has τ time series. For its application, MMPE algorithm is used to calculate the complexity of multiscroll chaotic systems. Results show that complexity of multiscroll chaotic systems does not increase as scroll number increases. Discussions based on first‐order difference operation present a reasonable explanation on why the complexity does not increase. This complexity analysis method lays a theoretical as well as experimental basis for the applications of multiscroll chaotic systems. © 2014 Wiley Periodicals, Inc. Complexity 21: 52–58, 2016     

A general projection algorithm for solving systems of linear equations

In this paper, we give a general projection algorithm for implementing some known extrapolation methods such as the MPE, the RRE, the MMPE and others. We apply this algorithm to vectors generated linearly and derive new algorithms for solving systems of linear equations. We will show that these algorithms allow us to obtain known projection methods such as the Orthodir or the GCR.     

An Incomplete Cholesky Factorization for Dense Symmetric Positive Definite Matrices

In this paper, we study the use of an incomplete Cholesky factorization (ICF) as a preconditioner for solving dense symmetric positive definite linear systems. This method is suitable for situations where matrices cannot be explicitly stored but each column can be easily computed. Analysis and implementation of this preconditioner are discussed. We test the proposed ICF on randomly generated systems and large matrices from two practical applications: semidefinite programming and support vector machines. Numerical comparison with the diagonal preconditioner is also presented.     

Algorithms for computing the distributions of sums of discrete random variables

We present algorithms for computing the probablity density function of the sum of two independent discrete random variables, along with an implementation of the algorithm in a computer algebra system. Some examples illustrate the utility of this algorithm.     

On a Chebyshev accelerated splitting iteration method with application to two‐by‐two block linear systems

The Chebyshev accelerated preconditioned modified Hermitian and skew‐Hermitian splitting (CAPMHSS) iteration method is presented for solving the linear systems of equations, which have two‐by‐two block coefficient matrices. We derive an iteration error bound to show that the new method is convergent as long as the eigenvalue bounds are not underestimated. Even when the spectral information is lacking, the CAPMHSS iteration method could be considered as an exponentially converging iterative scheme for certain choices of the method parameters. In this case, the convergence rate is independent of the parameters. Besides, the linear subsystems in each iteration can be solved inexactly, which leads to the inexact CAPMHSS iteration method. The iteration error bound of the inexact method is derived also. We discuss in detail the implementation of CAPMHSS for solving two models arising from the Galerkin finite‐element discretizations of distributed control problems and complex symmetric linear systems. The numerical results show the robustness and the efficiency of the new methods.     

A trading mechanism contingent on several indices

We introduce a trading mechanism where the execution of an order on a security can be made contingent on the relation between the clearing price of the security and the clearing price of one or several indices. A mechanism similar to ours, but limited to only one index, was implemented on the Tel Aviv Stock Exchange. We argue that it is in some cases crucial to make the execution of an order contingent on several indices. Our mechanism consists of a particular implementation of a double-sided multi-unit combinatorial auction with substitutes (or DMCS auction), which we introduced in an earlier article.     

Local and global optimization by parallel algorithms for MIMD systems

This paper describes an implementation on the Neptune system at Loughborough University of Sutti's parallel (MIMD) algorithm [1–3] and an analysis of its performance. Parallel asynchronous versions of Powell's method [6] and Price's algorithm [7] are proposed, designed for efficient implementation on MIMD systems.This work has been developed during the author's stay at the Numerical Optimization Centre, Hatfield Polytechnic, England.     

Real-time calculation of the current state estimates for a class of delay systems

The linear optimal observation problem is examined for one type of nonstationary delay system with an uncertainty in the initial state. A fast implementation of the dual method is proposed for calculating estimates of the initial state. This implementation is based on the quasi-reduction of the fundamental matrix of solutions to the mathematical model of delay systems. It is shown that an iteration step of the dual method only requires that auxiliary systems of ordinary differential equations be integrated on small time intervals. An algorithm is described for the real-time calculation of current state estimates. The results are illustrated by the optimal observation problem for a third-order stationary delay system.     

A parameterization method for the computation of invariant tori and their whiskers in quasi-periodic maps: Rigorous results

In this paper we prove rigorous results on persistence of invariant tori and their whiskers. The proofs are based on the parameterization method of [X. Cabré, E. Fontich, R. de la Llave, The parameterization method for invariant manifolds. I. Manifolds associated to non-resonant subspaces, Indiana Univ. Math. J. 52 (2) (2003) 283-328; X. Cabré, E. Fontich, R. de la Llave, The parameterization method for invariant manifolds. II. Regularity with respect to parameters, Indiana Univ. Math. J. 52 (2) (2003) 329-360]. The invariant manifolds results proved here include as particular cases of the usual (strong) stable and (strong) unstable manifolds, but also include other non-resonant manifolds. The method lends itself to numerical implementations whose analysis and implementation is studied in [A. Haro, R. de la Llave, A parameterization method for the computation of invariant tori and their whiskers in quasi-periodic maps: Numerical algorithms, preprint, 2005; A. Haro, R. de la Llave, A parameterization method for the computation of invariant tori and their whiskers in quasi-periodic maps: Numerical implementation and examples, preprint, 2005]. The results are stated as a posteriori results. Namely, that if one has an approximate solution which is not degenerate, then, one has a true solution not too far from the approximate one. This can be used to validate the results of numerical computations.     

An Adaptive CGNR Algorithm for Solving Large Linear Systems

In this paper, an adaptive algorithm based on the normal equations for solving large nonsymmetric linear systems is presented. The new algorithm is a hybrid method combining polynomial preconditioning with the CGNR method. Residual polynomial is used in the preconditioning to estimate the eigenvalues of the s.p.d. matrix A ^T A, and the residual polynomial is generated from several steps of CGNR by recurrence. The algorithm is adaptive during its implementation. The robustness is maintained, and the iteration convergence is speeded up. A numerical test result is also reported.     

Algorithm for determining the reachability set of a linear control system

We present in this paper algorithms for calculating the reachability set of a linear control system with a bounded closed control set and a finite time interval. We also present algorithms for the time-optimal problem of the linear control that yields an approximation to the optimal time and the corresponding control function. We give numerical examples of the computer implementation of these algorithms.This research was supported by the National Science Foundation of China.The authors are grateful to an anonymous referee for constructive suggestions that greatly helped to improve the presentation.     

IMinpert： An Incomplete Minimum Perturbation Algorithm for Large Unsymmetric Linear Systems

This paper gives the truncated version of the Minpert method:the incomplete minimum perturbation algorithm(IMinpert).It is based on an incomplete orthogonal- ization of the Krylov vectors in question,and gives a quasi-minimum backward error solution over the Krylov subspace.In order to make the practical implementation of IMinpert easy and convenient,we give another approximate version of the IMinpert method:A-IMinpert.Theoretical properties of the latter algorithm are discussed.Nu- merical experiments are reported to show the proposed method is effective in practice and is competitive with the Minpert algorithm.     

Global Optimization using a Dynamical Systems Approach

We develop new algorithms for global optimization by combining well known branch and bound methods with multilevel subdivision techniques for the computation of invariant sets of dynamical systems. The basic idea is to view iteration schemes for local optimization problems – e.g. Newton’s method or conjugate gradient methods – as dynamical systems and to compute set coverings of their fixed points. The combination with bounding techniques allow for the computation of coverings of the global optima only. We show convergence of the new algorithms and present a particular implementation. Michael Dellnitz - Research of the authors is partially supported by the Deutsche Forschungsgemeinschaft within the Sonderforschungsbereich 376     

Gearhart–Koshy acceleration for affine subspaces

The method of cyclic projections finds nearest points in the intersection of finitely many affine subspaces. To accelerate convergence, Gearhart & Koshy proposed a modification which, in each iteration, performs an exact line search based on minimising the distance to the solution. When the subspaces are linear, the procedure can be made explicit using feasibility of the zero vector. This work studies an alternative approach which does not rely on this fact, thus providing an efficient implementation in the affine setting.     

A path following method for box-constrained multiobjective optimization with applications to goal programming problems

We propose a path following method to find the Pareto optimal solutions of a box-constrained multiobjective optimization problem. Under the assumption that the objective functions are Lipschitz continuously differentiable we prove some necessary conditions for Pareto optimal points and we give a necessary condition for the existence of a feasible point that minimizes all given objective functions at once. We develop a method that looks for the Pareto optimal points as limit points of the trajectories solutions of suitable initial value problems for a system of ordinary differential equations. These trajectories belong to the feasible region and their computation is well suited for a parallel implementation. Moreover the method does not use any scalarization of the multiobjective optimization problem and does not require any ordering information for the components of the vector objective function. We show a numerical experience on some test problems and we apply the method to solve a goal programming problem.     

Bundle-based decomposition for large-scale convex optimization: Error estimate and application to block-angular linear programs

Robinson has proposed the bundle-based decomposition algorithm to solve a class of structured large-scale convex optimization problems. In this method, the original problem is transformed (by dualization) to an unconstrained nonsmooth concave optimization problem which is in turn solved by using a modified bundle method. In this paper, we give a posteriori error estimates on the approximate primal optimal solution and on the duality gap. We describe implementation and present computational experience with a special case of this class of problems, namely, block-angular linear programming problems. We observe that the method is efficient in obtaining the approximate optimal solution and compares favorably with MINOS and an advanced implementation of the Dantzig—Wolfe decomposition method.     

Time-triggered and event-triggered control of switched affine systems via a hybrid dynamical approach

This paper focuses on the design of both periodic time- and event-triggered control laws of switched affine systems using a hybrid dynamical system approach. The novelties of this paper rely on the hybrid dynamical representation of this class of systems and on a free-matrix min-projection control, which relaxes the structure of the usual Lyapunov matrix-based min-projection control. This contribution also presents an extension of the usual periodic time-triggered implementation to the event-triggered one, where the control input updates are permitted only when a particular event is detected. Together with the definition of an appropriate optimization problem, a stabilization result is formulated to ensure the uniform global asymptotic stability of an attractor for both types of controllers, which is a neighborhood of the desired operating point. Finally, the proposed method is evaluated through a numerical example.     

Convergence properties of the cross-entropy method for discrete optimization

We present new theoretical convergence results on the cross-entropy (CE) method for discrete optimization. We show that a popular implementation of the method converges, and finds an optimal solution with probability arbitrarily close to 1. We also give conditions under which an optimal solution is generated eventually with probability 1.     

Query Optimization in Distributed Relational Databases

The query optimizer is the DBMS (data base management system) component whose task is to find an optimal execution plan for a given input query. Typically, optimization is performed using dynamic programming. However, in distributed execution environments, this approach becomes intractable, due to the increase in the search space incurred by distribution. We propose the use of the tabu search metaheuristic for distributed query optimization. A hashing-based data structure is used to keep track of the search memory, simplifying significantly the implementation of tabu search. To validate this proposal, we implemented the tabu search strategy in the scope of an existing optimizer, which runs several search strategies. We focus our attention on the more difficult problems in terms of the query execution space, in which the solution space includes bushy execution plans and Cartesian products, which are not dealt with very often in the literature. Using a real-life application, we show the effectiveness of tabu search when compared to other strategies.