子矩阵约束下的对称正交反对称矩阵反问题

利用矩阵对的广义奇异值分解,讨论矩阵方程AX=B在子矩阵约束下有对称正交反对称解的充要条件以及解的表达式,另外,给出了在矩阵方程的解集合中与给定矩阵的最佳逼近解的表达式.     

求解子矩阵约束条件下四元数矩阵方程AXB=C的矩阵半张量积方法

1引言子矩阵约束下的矩阵方程问题是指限定矩阵方程的解X的一个子矩阵X_(0),然后在某个约束集合中求解矩阵方程.如求满足X([1:q])=X_(0)的对称解,这里X([1:q])表示矩阵X的q阶顺序主子阵.子矩阵约束下的矩阵方程问题来源于实际中的系统扩张问题[1],有一定的实际意义和重要性,受到了许多学者的关注,如[2-4]中,彭分别研究了子矩阵约束条件下实矩阵方程AX=B的实矩阵解,中心对称解和双对称解.     

中心主子矩阵约束下矩阵反问题X~TAX=B的双对称解及其最佳逼近

文章研究了中心主子矩阵约束下矩阵方程X~TAX=B的双对称解.利用子空间的基将约束问题转化为非约束问题的方法,得到了有解的充分必要条件及解的一般表达式.进而,考虑了与之相关的任意给定矩阵的最佳逼近问题.     

子矩阵约束下的Hermite-Hamilton矩阵反问题

该文讨论了子矩阵约束下矩阵反问题$AX=B$的Hermite-Hamilton矩阵解.给出了解存在的充要条件和通解的一般表达式.且对任一给定矩阵,在解集合中求出了其最佳逼近解.     

一类矩阵方程异类约束解与Ls解的迭代算法

当多矩阵变量线性矩阵方程(LME)相容时,通过修改共轭梯度法的下降方向及其有关系数,建立求LME的一种异类约束解的迭代算法.当LME不相容时,先通过构造等价的线性矩阵方程组(LMEs),将不相容的LME异类约束最小二乘解(Ls解)问题转化为相容的LMEs异类约束解问题,然后参照求LME的异类约束解的迭代算法,建立求LME的一种异类约束Ls解的迭代算法.不考虑舍入误差时,迭代算法可在有限步计算后求得LME的一组异类约束解或者异类约束Ls解;选取特殊的初始矩阵时,可求得LME的极小范数异类约束解或者异类约束Ls解.此外,还可在LME的异类约束解或者异类约束Ls解集合中给出指定矩阵的最佳逼近矩阵.算例表明,迭代算法是有效的.     

双矩阵变量Riccati矩阵方程对称解的迭代算法

研究一类双矩阵变量Riccati矩阵方程(R-ME)对称解的数值计算问题.运用牛顿算法求R-ME的对称解时,会导出求双矩阵变量线性矩阵方程的对称解或者对称最小二乘解的问题,采用修正共轭梯度法解决导出的线性矩阵方程约束解问题,可建立求R-ME的对称解的迭代算法.数值算例表明,迭代算法是有效的.     

一类高阶矩阵差分方程的解及渐近稳定性

讨论了一类高阶矩阵差分方程的解及渐近稳定性问题.利用特征子空间的维数得到了特征方程存在可对角化解的一个充要条件;然后利用特征方程的相异解刻划出该矩阵差分方程的通解,并给出其解渐近稳定的两个充分条件.推广了相关文献的结果.     

四元数体上矩阵方程的自共轭解

利用矩阵的M-P逆和矩阵分块,给出了四元数体上矩阵方程XB=D在子空间上有自共轭解的充要条件以及解的一般形式,并由此给出了矩阵方程AXB=D有自共轭解的充要条件和解的一般形式.     

Hermite-广义反Hamilton矩阵的一类反问题

给定矩阵X和B,利用矩阵的广义奇异值分解,得到了矩阵方程X~HAX=B有Hermite-广义反Hamiton解的充分必要条件及有解时解的—般表达式.用S_E表示此矩阵方程的解集合,证明了S_E中存在唯一的矩阵(?),使得(?)与给定矩阵A的差的Frobenius范数最小,并且给出了矩阵(?)的表达式;同时也证明了S_E中存在唯一的矩阵A_o,使得A_o是此矩阵方程的极小Frobenius范数Hermite-广义反Hamilton解,并且给出了矩阵A_o的表达式.     

特殊双变量矩阵方程组异类约束解的MCG算法

本文研究了约束矩阵方程问题中异类约束解的迭代算法.利用修正共轭梯度法,求得了特殊双变量线性矩阵方程组的异类约束解,选取特殊的初始矩阵,得到唯一极小范数异类约束解.理论证明和数值算例验证了该方法的有限步收敛性,推广了修正共轭梯度法在求约束矩阵方程问题中的应用范围.     

Computational Discretization Algorithms for Functional Inequality Constrained Optimization

In this paper, a functional inequality constrained optimization problem is studied using a discretization method and an adaptive scheme. The problem is discretized by partitioning the interval of the independent parameter. Two methods are investigated as to how to treat the discretized optimization problem. The discretization problem is firstly converted into an optimization problem with a single nonsmooth equality constraint. Since the obtained equality constraint is nonsmooth and does not satisfy the usual constraint qualification condition, relaxation and smoothing techniques are used to approximate the equality constraint via a smooth inequality constraint. This leads to a sequence of approximate smooth optimization problems with one constraint. An adaptive scheme is incorporated into the method to facilitate the computation of the sum in the inequality constraint. The second method is to apply an adaptive scheme directly to the discretization problem. Thus a sequence of optimization problems with a small number of inequality constraints are obtained. Convergence analysis for both methods is established. Numerical examples show that each of the two proposed methods has its own advantages and disadvantages over the other.     

调度问题中两类分离约束传播算法的比较及一种改进算法

约束传播算法是求解约束满足问题的一种重要方法。调度问题是一种特殊的约束满足问题。本介绍了调度问题中的Edge-Finding和Energy-Reasoning两种分离约束传播算法，并对它们进行了比较，中最后给出了一种结合Energy-Reasoning的Edge-Finding改进算法。     

Distributionally robust parameter identification of a time-delay dynamical system with stochastic measurements

In this paper, we consider a parameter identification problem involving a time-delay dynamical system, in which the measured data are stochastic variable. However, the probability distribution of this stochastic variable is not available and the only information we have is its first moment. This problem is formulated as a distributionally robust parameter identification problem governed by a time-delay dynamical system. Using duality theory of linear optimization in a probability space, the distributionally robust parameter identification problem, which is a bi-level optimization problem, is transformed into a single-level optimization problem with a semi-infinite constraint. By applying problem transformation and smoothing techniques, the semi-infinite constraint is approximated by a smooth constraint and the convergence of the smooth approximation method is established. Then, the gradients of the cost and constraint functions with respect to time-delay and parameters are derived. On this basis, a gradient-based optimization method for solving the transformed problem is developed. Finally, we present an example, arising in practical fermentation process, to illustrate the applicability of the proposed method.     

A generalized inverse eigenvalue problem in structural dynamic model updating

This paper is concerned with the problem of the best approximation for a given matrix pencil under a given spectral constraint and a submatrix pencil constraint. Such a problem arises in structural dynamic model updating. By using the Moore–Penrose generalized inverse and the singular value decomposition (SVD) matrices, the solvability condition and the expression for the solution of the problem are presented. A numerical algorithm for solving the problem is developed.     

A General Approach for Constraint Solving by Local Search

In this paper, we present a general approach for solving constraint problems by local search. The proposed approach is based on a set of high-level constraint primitives motivated by constraint programming systems. These constraints constitute the basic bricks to formulate a given combinatorial problem. A tabu search engine ensures the resolution of the problem so formulated. Experimental results are shown to validate the proposed approach. This revised version was published online in August 2006 with corrections to the Cover Date.     

多目标最优控制的小波逼近解

基于小波多尺度逼近特性,提出了一种求解线性时变系统中多目标最优控制的新方法.该法避免求解带附加积分约束的R iccati微分方程而只需求解一个代数二次约束规划问题,适合于计算机求解.数值研究表明,所提算法是精确可行的.     

A Global Optimization Method for Solving Convex Quadratic Bilevel Programming Problems

We use the merit function technique to formulate a linearly constrained bilevel convex quadratic problem as a convex program with an additional convex-d.c. constraint. To solve the latter problem we approximate it by convex programs with an additional convex-concave constraint using an adaptive simplicial subdivision. This approximation leads to a branch-and-bound algorithm for finding a global optimal solution to the bilevel convex quadratic problem. We illustrate our approach with an optimization problem over the equilibrium points of an n-person parametric noncooperative game.     

一类受时间约束的广义运输问题的求解

研究一类受时间约束的广义运输问题,将时间约束转化为容量约束,并将该问题转化为标准的最小费用流问题进而求解.该方法能够较快地找到最优运输方案.     

The zero-one knapsack problem with equality constraint

The zero-one knapsack problem is a linear zero-one programming problem with a single inequality constraint. This problem has been extensively studied and many applications and efficient algorithms have been published. In this paper we consider a similar problem, one with an equality instead of the inequality constraint. By replacing the equality by two inequalities one of which is placed in the economic function, a Lagrangean relaxation of the problem is obtained. The relation between the relaxed problem and the original problem is examined and it is shown how the optimal value of the relaxed problem varies with increasing values of the Lagrangean multiplier. Using these results an algorithm for solving the problem is proposed.The paper concludes with a discussion of computational experience.     

The complexity of recursive constraint satisfaction problems

We investigate the complexity of finding solutions to infinite recursive constraint satisfaction problems. We show that, in general, the problem of finding a solution to an infinite recursive constraint satisfaction problem is equivalent to the problem of finding an infinite path through a recursive tree. We also identify natural classes of infinite recursive constraint satisfaction problems where the problem of finding a solution to the infinite recursive constraint satisfaction problem is equivalent to the problem of finding an infinite path through finitely branching recursive trees or recursive binary trees. There are a large number of results in the literature on the complexity of the problem of finding an infinite path through a recursive tree. Our main result allows us to automatically transfer such results to give equivalent results about the complexity of the problem of finding a solution to a recursive constraint satisfaction problem.