A parallel version of the preconditioned conjugate gradient method for boundary element equations

The parallel version of precondition techniques is developed for matrices arising from the Galerkin boundary element method for two-dimensional domains with Dirichlet boundary conditions. Results were obtained for implementations on a transputer network as well as on an nCUBE-2 parallel computer showing that iterative solution methods are very well suited for a MIMD computer. A comparison of numerical results for iterative and direct solution methods is presented and underlines the superiority of iterative methods for large systems.     

单件小批生产系统下模糊优化的动态随机投入产出模型

本文针对单件小批量生产系统 ,建立了模糊优化的动态随机投入产出模型 ,同时给出了该模型的递推解法 ,并用此模型对某单件小批企业在生产计划期的商品量进行了规划     

Analysis and implementation of cross sync period underwater acoustical positioning techniques for underwater targets

I.IlltroductionWhenanactiveunderwateracousticalpositioningsystemissearchingforanunderwatertarget,itisnecessaryforittocompleteatransmittingandreceivingcycIewithineachworkingperiodinordertofindoutthelocationofthetargetcorrectly.Forexample,whenanunder-wateracoustica1synchronouspositioningsystemistrackinganunderwatertargetwithagivensynchr0nizingperiod,thepropagati0ntimeofthepositioningsignaltravellingfr0mthetaJrgettothearrayofthesystemshouldbelessthanthesynchronizingperiod0therwisetargetdis-tance…     

几种约束广义逆矩阵的有限算法

1引言与引理众所周知，关于非奇异方阵的正则逆的有限算法是由Faddeev大给在1949年之前提出的，这就是著名的Faddeev算法[1，P…334-336]。自从五十年代中期广义逆矩阵的研究复兴与发展以来，有不少学者提出了关于广义逆矩阵的有限算法。第一个给出关于广义逆矩     

Ranking Functions Induced by Weighted Average of Fuzzy Numbers

In this paper we present two definitions of possibilistic weighted average of fuzzy numbers, and by them we introduce two different rankings on the set of real fuzzy numbers. The two methods are dependent on several parameters. In the first case, the parameter is constant and the results generalize what Carlsson and Fuller have obtained in (2001). In the second case, the parameter is a function, not fixed a priori by the decision maker, but it depends on the position of the interval on the real axe. In all the two cases we call the parameter degree of risk, which takes into account of a risk-tendency or aversion of the decision maker.     

Application of a Near-Optimal Reinforcement Learning Controller to a Robotics Problem in Manufacturing: A Hybrid Approach

Optimization theory provides a framework for determining the best decisions or actions with respect to some mathematical model of a process. This paper focuses on learning to act in a near-optimal manner through reinforcement learning for problems that either have no model or the model is too complex. One approach to solving this class of problems is via approximate dynamic programming. The application of these methods are established primarily for the case of discrete state and action spaces. In this paper we develop efficient methods of learning which act in complex systems with continuous state and action spaces. Monte-Carlo approaches are employed to estimate function values in an iterative, incremental procedure. Derivative-free line search methods are used to obtain a near-optimal action in the continuous action space for a discrete subset of the state space. This near-optimal control policy is then extended to the entire continuous state space via a fuzzy additive model. To compensate for approximation errors, a modified procedure for perturbing the generated control policy is developed. Convergence results under moderate assumptions and stopping criteria are established.     

Primal-dual proximal point algorithm for linearly constrained convex programming problems

A primal-dual version of the proximal point algorithm is developed for linearly constrained convex programming problems. The algorithm is an iterative method to find a saddle point of the Lagrangian of the problem. At each iteration of the algorithm, we compute an approximate saddle point of the Lagrangian function augmented by quadratic proximal terms of both primal and dual variables. Specifically, we first minimize the function with respect to the primal variables and then approximately maximize the resulting function of the dual variables. The merit of this approach exists in the fact that the latter function is differentiable and the maximization of this function is subject to no constraints. We discuss convergence properties of the algorithm and report some numerical results for network flow problems with separable quadratic costs.     

A floating-point genetic algorithm for solving the unit commitment problem

This paper proposes a floating-point genetic algorithm (FPGA) to solve the unit commitment problem (UCP). Based on the characteristics of typical load demand, a floating-point chromosome representation and an encoding–decoding scheme are designed to reduce the complexities in handling the minimum up/down time limits. Strategic parameters of the FPGA are characterized in detail, i.e., the evaluation function and its constraints, population size, operation styles of selection, crossover operation and probability, mutation operation and probability. A dynamic combination scheme of genetic operators is formulated to explore and exploit the FPGA in the non-convex solution space and multimodal objective function. Experiment results show that the FPGA is a more effective technique among the various styles of genetic algorithms, which can be applied to the practical scheduling tasks in utility power systems.     

关于可达矩阵的求法探讨

在《离散数学》、《图论》课程中 ,用矩阵表示图时 ,涉及到一类重要的矩阵——可达矩阵 ,它是判别图中任意两点是否有通路的重要手段 ,也是求强分图的重要方法 ,但是可达矩阵的求法比较复杂 .本文针对这一问题 ,对可达矩阵的求法进行了改进 ,提出了一种简单可行的算法 .     

A comparison of two algorithms for the assignment problem

State-of-the-art computational results have shown that the shortest augmenting path (SAP) methods are more efficient than other primal-dual and primal-simplex based methods for solving the linear assignment problem on uniprocessor computers. There is, however, some controversy concerning their merits when compared with Bertsekas' auction algorithm on multiprocessor computers. In this study we investigate the performance of these competing methods on the Alliant FX/8. For each method, theoretical motivation, sources of parallelism and computational results are presented.