长方矩阵加权群逆的迭代法

1引 言与引理 最近,文[1]定义了长方矩阵的一种加权群逆:设A∈Cm×n,W∈Cn×m.称满足下列矩阵方程组的矩阵X∈Cm×n为A的加W权群逆:(W1)AWXWA=A, (W2)XWAWX=X, (W3)AWX=XWA通常记A的加W权群逆为A#W.若A#W存在,则它是唯一的.     

AOR迭代法的收敛性

1.引言 [1]定义了解线性方程组A_x=b的AOR迭代法,它以SOR迭代为特例,而且适当选取参数,有可能比SOR方法收敛快(见[2]).众所周知,使 AOR方法有意义的最基本条件是A的对角元素都不为零.然而,在实际计算中,有时需要求解的线性方程组其系数矩阵存在零对角元素.例如[3]中研究的线性方程组的系数矩阵具有如下形式:     

关于复方阵的平方根

本刊文 [1]中提出如何判断一个方阵是否存在平方根的问题 .这里 ,我们就 n阶复方阵情形给出三个判别准则 .设 A是 n阶复方阵 ,JA 表示它的若当标准形 ,则存在相似变换矩阵 P,使得 A=PJAP-1 .有关复方阵 A的若当标准形 JA 以及相似变换矩阵P的求法 ,见本刊文 [2 ]或 [3 ] ,本文不再赘述 .定义 1　设 A是 n阶复方阵 ,若存在 n阶复方阵 B,使得 B2 =A,则称 B为 A的平方根 .为书写简便 ,我们用记号 Jr( x) ( r≥ 1)与diag[B1 ,B2 ,… ,Bs]分别表示 r阶若当矩阵和对角块矩阵 :x 1 x 1x∈ Mr( C) ,B1 B2 Bs.用文 [2 ]中给出的计算复…     

(n_1,n_2,…,n_k)型k重(r_1,r_2,…,r_k)-循环矩阵逆矩阵的插值求法

1 引言 关于(n_1,n_2,…,n_k)型k重(r_1,r_2,…,r_k)-循环矩阵的某些性质及其广义逆阵,文[1]曾作过探讨,由于在理论物理、固态物理、编码理论及石油勘探等许多大型计算实例中常常遇到这类循环系统的数值计算问题,因而探求这类矩阵的求逆问题就显得非常重要。 受文[2]启示,本文用插值法推出了(n_1,n_2,…,n_k)型k重(r_1,r_2,…,r_k)-循环矩阵逆矩阵的一个显式计算公式及其证明。 2 预备知识 定义称下列矩阵为(n_1,n_2,…,n_k)型k重(r_1,r_2,…,r_k)-循环矩阵 n_1—1 其中表示矩阵的Kronecker,是n_1阶r_1-循环矩阵,A_J_1是(n_2,n_3,…,n_k)型k-1重(r_2,r_3,…,r_k)-循环矩阵,它由递推关系(2)和(3)确定: 这里; 这里j_1=0,n_1—1,j_2=0,n_2—1,…,j_i=0,n_i—1,i=k-2,k—3,…,2,1. 由于A决定于它的第一行元素和参数r_1,r_2,…,r_k,故A可记为     

A类零壹矩阵的基的一些定理

定义1 令n≥3,A=(a_(ij))_(n×n),i=1或0,对任固定的i(1≤i≤n)存在唯一的一个j_o(1≤j_o≤h)使得a(ij)_o=1,其余的a(ij)=0(j jo,1≤j≤n),则称(0,1)一矩阵A为A型的矩阵。 显然A型矩阵在矩阵乘法运算下成为一个具有单位元的半群。 定理2 令A={A:A是n级的A型矩阵},B A,若对任A A总存在有B_1,B_2,…B_K B使得A=B_1B_2…B_K,则称S为A的一个基。     

一类块三对角阵特征值的求解及应用

提出了求一类块三对角矩阵A的特征值和特征向量的方法,求得了该类矩阵的特征值和特征向量的表达式,并写出了用迭代法解该类方程组Au=f时迭代矩阵的特征值.     

争鸣

问题问题二阶逆矩阵为什么不能这样定义?人教版是这样定义二阶逆矩阵:设A是一个二阶矩阵,如果存在二阶矩阵B,使得BA=AB=E2,则称矩阵A可逆,或称矩阵A是可逆矩阵,并且称B是A的逆矩阵.有一种观点认为可以作如下定义:设A是一个二阶矩阵,如果存在二阶矩阵B,使得AB=E2,则称矩阵A可逆,或称矩阵A是可逆矩阵,并且称B     

异步并行矩阵多分裂块松弛迭代算法

1 引言 众所周知,许多微分方程经过差分或有限元离散,即可归结为线性代数方程组 Ax=b,A∈L(R~n)非奇异,x,b∈R~n.(1.1)缘于原问题的物理特性,系数矩阵A∈L(R~n)通常是大型稀疏的,并且具有规则的分块结构。鉴此,文[1]基于矩阵多重分裂的概念,并运用线性迭代法的松弛加速技巧,提出了求解这类大型稀疏分块线性代数方程组的并行矩阵多分裂块松弛迭代算法,并在适当的条件下建立了算法的收敛理论。对于SIMD多处理机系统,这类算法是颇为适用和行之有效的。     

二阶线性系统稳定性判定的数值方法

A为N阶方阵,N=2n,设A的特征值为ξ_1,ξ_2,…,ξ_N,根据[3]有如下定义: 定义1.若所有的 Reξ_i<0,则称系统(1.1)是渐近稳定的,相应的A阵叫渐近稳定阵;若所有的Reξ_i≤0且对所有的Reξ_k=0的ξ_k对应的初级因子均是线性的,则称系     

拓宽知识 积累方法 解决问题

<正>1题目已知无穷集合A,B,且A?N,B?N,记A+B={a+b|a∈A,b∈B},定义:满足N*?(A+B)时,则称集合A,B互为"完美加法补集".(Ⅰ)已知集合A={a|a=2m+1,m∈N},B={b|b=2n,n∈N}.判断2019和2020是否属于集合A+B,并说明理由;     

一类大系统目标规划问题分解算法中最优解之间的关系

将一类大系统目标规划问题分解为若干个子问题,研究了原问题的最优解和各个子问题最优解之间的关系,并讨论了原问题最优解的判别条件。     

Capacitance matrix method using strips with alternating neumann and dirichlet boundary conditions

A method for solving numerically second-order elliptic equations in rectangular regions is discussed. The rectangle is divided into strips. The original problem is replaced by subproblems with alternating Neumann and Dirichlet boundary conditions, and an auxiliary problem. The latter, called the capacitance matrix system, is solved by an efficient numerical procedure using the conjugate gradient method.     

An Integral Simplex Algorithm for Solving Combinatorial Optimization Problems

In this paper a local integral simplex algorithm will be described which, starting with the initial tableau of a set partitioning problem, makes pivots using the pivot on one rule until no more such pivots are possible because a local optimum has been found. If the local optimum is also a global optimum the process stops. Otherwise, a global integral simplex algorithm creates and solves the problems in a search tree consisting of a polynomial number of subproblems, subproblems of subproblems, etc. The solution to at least one of these subproblems is guaranteed to be an optimal solution to the original problem. If that solution has a bounded objective then it is an optimal set partitioning solution of the original problem, but if it has an unbounded objective then the original problem has no feasible solution. It will be shown that the total number of pivots required for the global integral simplex method to solve a set partitioning problem having m rows, where m is an arbitrary but fixed positive integer, is bounded by a polynomial function of n.A method for programming the algorithms in this paper to run on parallel computers is discussed briefly.     

SUBSPACE SEARCH METHOD FOR A CLASS OF LEAST SQUARES PROBLEM

1. IntroductionNow the least squares problem is considered as follows:1Mid r(x,y) ~ SllAx By ~ bll' s.t. x 2 0 (1.1)where A E Rm",, B E R"q, and b E Re are given constant matrices and vectors,respectively.These problems arise in many areas of applications, such as scientific and engineering computing, physics, statistics, flited curve, economic, mathematical programming,social science, and as a component part of some large computation problem, as anexample, a nonlinear least squares pr…     

一类大系统目标规划问题最优解的判别准则

本文将一类大系统目标规划问题分解为若干个子问题,研究了原问题的最优解和各个子问题最优解之间的关系,并讨论了原问题最优解的判别条件．     

正定二次规划的一个对偶算法

给出了一个正定二次规划的对偶算法．算法把原问题分解为一系列子问题,在保持原问题的Wolfe对偶可行的前提下,通过迭代计算,由这一系列子问题的最优解向原问题的最优解逼近．同时给出了算法的有限收敛性．     

A Dual Simplex Implementation of a Constraint Selection Algorithm for Linear Programming

The constraint selection approach to linear programming begins by solving a relaxed version of the problem using only a few of the original constraints. If the solution obtained to this relaxation satisfies the remaining constraints it is optimal for the original LP. Otherwise, additional constraints must be incorporated in a larger relaxation. The procedure successively generates larger subproblems until an optimal solution is obtained which satisfies all of the original constraints. Computational results for a dual simplex implementation of this technique indicate that solving several small subproblems in this manner is more computationally efficient than solving the original LP using the revised simplex method.     

Global optimization for a class of fractional programming problems

This paper presents a canonical dual approach to minimizing the sum of a quadratic function and the ratio of two quadratic functions, which is a type of non-convex optimization problem subject to an elliptic constraint. We first relax the fractional structure by introducing a family of parametric subproblems. Under proper conditions on the “problem-defining” matrices associated with the three quadratic functions, we show that the canonical dual of each subproblem becomes a one-dimensional concave maximization problem that exhibits no duality gap. Since the infimum of the optima of the parameterized subproblems leads to a solution to the original problem, we then derive some optimality conditions and existence conditions for finding a global minimizer of the original problem. Some numerical results using the quasi-Newton and line search methods are presented to illustrate our approach.     

A brain storm optimization approach for the cumulative capacitated vehicle routing problem

The cumulative capacitated vehicle routing problem (CCVRP) is a combinatorial optimization problem which aims to minimize the sum of arrival times at customers. This paper presents a brain storm optimization algorithm to solve the CCVRP. Based on the characteristics of the CCVRP, we design new convergent and divergent operations. The convergent operation picks up and perturbs the best-so-far solution. It decomposes the resulting solution into a set of independent partial solutions and then determines a set of subproblems which are smaller CCVRPs. Instead of directly generating solutions for the original problem, the divergent operation selects one of three operators to generate new solutions for subproblems and then assembles a solution to the original problem by using those new solutions to the subproblems. The proposed algorithm was tested on benchmark instances, some of which have more than 560 nodes. The results show that our algorithm is very effective in contrast to the existing algorithms. Most notably, the proposed algorithm can find new best solutions for 8 medium instances and 7 large instances within short time.     

关于单调变分不等式的不精确邻近点算法的收敛性分析

We consider a proximal point algorithm(PPA) for solving monotone variational inequalities. PPA generates a sequence by solving a sequence of strongly monotone subproblems .However,solving the subproblems is either expensive or impossible. Some inexact proximal point algorithms(IPPA) have been developed in many literatures. In this paper, we present a criterion for approximately solving subproblems. It only needs one simple additional work on the basis of original algorithm, and the convergence criterion becomes milder. We show that this method converges globally under new criterion provided that the solution set of the problem is nonempty.