关于随机矩阵Kronecker积的谱半径的不等式

研究了随机矩阵的Kronecker积的数学期望的性质,得到了随机矩阵的Kronecker积的谱半径的几个不等式.     

对称双随机矩阵逆特征值问题

主要研究随机矩阵逆特征值问题．特别是对称双随机矩阵和列随机矩阵逆特征值问题．对参考文献[1]与[2]的结论作了一些推广．并给出了—个数值例子．     

既约随机矩阵的一些性质

本文讨论了既约广义随机矩阵特征值的性质,得到了双随机矩阵的益为既约矩阵的充要条件,以及P类矩阵的一些性质．     

广义双随机矩阵反问题及其应用

研究广义双随机矩阵反问题.给出广义双随机矩阵的最小二乘解,得到了解的具体表达形式.并讨论了用广义双随机矩阵构造给定矩阵的最佳逼近问题,给出该问题有解的充分必要条件和解的表达形式.包括算法及数值例子.     

矩阵之和的加权Moore—Penrose逆

§1.引言 Cline在[2]中讨论了矩阵UU~* VV~*的广义逆,并当UV~*=0(或U~*V=0)时给出了矩阵之和U V的广义逆的表达式。本文讨论矩阵UW_1U~* VW_2V~* VT~*U~* UTV~*的加权广义逆,其中[_(T~*W_2)~(W_1T)]是正定矩阵。并当UN~(-1)V~*=0(或U~*MV=0)时给出了矩阵之和U V的加权广义逆的表示。作为特例,给出了不同于[2]的UU~* VV~*及U V的广义逆的表达式。     

关于双随机循环矩阵中的素元(英文)

非负矩阵中的素元分类问题在控制和系统论中有重要的应用.本文将研究由G.Picci等所提出的关于双随机循环矩阵中素元的一个问题和一个猜想,得到了一个判别具有位数5的n阶双随机循环矩阵不是素元的充要条件,给出了猜想成立的一些充分条件.     

实随机矩阵的Jordan标准型

“正好有非线性初等因子的矩阵在实际工作中几乎是不存在的 .…… ,舍入误差通常将导致一个已经不再有非线性初等因子的矩阵”[3 ] ,根据 J.H.Wilkinson揭示的这些客观规律 ,以及 G.H.Golub给出的结论 :“Rn× n中的可对角化矩阵在 Rn× n中是稠密的”[1 ] ,使我们联想到以下命题成立的可能性 :“在 Rn× n中具有重特征值的矩阵集合的 L ebesgue测度为零”.本文的主题就是证明该命题成立 .引理　设 F(x1 ,… ,xm)为变元 x1 ,… ,xm的实系数多项式 ,那么μm{ x|F(x) =0 ,‖ x‖ 相似文献   

大规模矩阵降维的随机逼近方法

大规模矩阵降维和分解是数据分析的核心问题之一,在工程领域应用广泛,如图像分割、文本分类、数据挖掘,然而,传统的矩阵分解方法(如SVD、谱分解)计算复杂度高,不适用于大规模矩阵处理.近些年来,随机逼近方法用来发现大规模矩阵的低维近似,有效地降低了计算复杂度,是当今的研究热点.围绕基于随机逼近的大矩阵降维方法展开论述,介绍了矩阵降维中的抽样策略、CUR分解、Nystrom方法、随机逼近方法,比较研究了这些方法的优缺点.对重要的随机逼近方法开展了一些图像试验分析.最后,进行了总结并讨论了一些方向的可行性.     

随机矩阵的平均极限矩阵的几个定理

本文引入了随机矩阵的平均投限矩阵的概念，并讨论它与原矩阵之同的关系。给出了几个很好的结果．     

具有位数3和4的双随机循环矩阵中的素元

本文研究了双随机循环矩阵中素元的分类问题．由于任一n阶双随机循环矩阵都可以唯一地表示为移位的n-1次一元多项式,从而可把双随机循环矩阵中素元的分类问题简化为解双随机循环矩阵上的一个方程．应用此原理,本文完全解决了判别具有位数3的n阶双随机循环矩阵是否为素元的问题,并给出了n阶双随机循环矩阵中一类具有位数4的素元．     

一类随机线性互补问题的求法

考虑一类随机互线性补问题的求解方法,目的是通过定义NCP函数来使正则化期望残差最小化.通过拟蒙洛包洛方法产生一系列观察值并且证得离散近似问题最小值解的聚点就是相应随机线性互补问题的期望残差最小值ERM,同时得到利用ERM到解为有界的充分条件.进一步证明ERM法能够得到具有稳定性和最小灵敏度的稳健解.     

Robust solution of monotone stochastic linear complementarity problems

We consider the stochastic linear complementarity problem (SLCP) involving a random matrix whose expectation matrix is positive semi-definite. We show that the expected residual minimization (ERM) formulation of this problem has a nonempty and bounded solution set if the expected value (EV) formulation, which reduces to the LCP with the positive semi-definite expectation matrix, has a nonempty and bounded solution set. We give a new error bound for the monotone LCP and use it to show that solutions of the ERM formulation are robust in the sense that they may have a minimum sensitivity with respect to random parameter variations in SLCP. Numerical examples including a stochastic traffic equilibrium problem are given to illustrate the characteristics of the solutions.     

A Barzilai–Borwein type method for stochastic linear complementarity problems

We consider the expected residual minimization (ERM) formulation of stochastic linear complementarity problem (SLCP). By employing the Barzilai–Borwein (BB) stepsize and active set strategy, we present a BB type method for solving the ERM problem. The global convergence of the proposed method is proved under mild conditions. Preliminary numerical results show that the method is promising.     

Feasible smooth method based on Barzilai–Borwein method for stochastic linear complementarity problem

In this paper, we propose a feasible smooth method based on Barzilai–Borwein (BB) for stochastic linear complementarity problem. It is based on the expected residual minimization (ERM) formulation for the stochastic linear complementarity problem. Numerical experiments show that the method is efficient.     

On the ERM formulation and a stochastic approximation algorithm of the stochastic-R_0 EVLCP

In this paper, a class of stochastic extended vertical linear complementarity problems is studied as an extension of the stochastic linear complementarity problem. The expected residual minimization (ERM) formulation of this stochastic extended vertical complementarity problem is proposed based on an NCP function. We study the corresponding properties of the ERM problem, such as existence of solutions, coercive property and differentiability. Finally, we propose a descent stochastic approximation method for solving this problem. A comprehensive convergence analysis is given. A number of test examples are constructed and the numerical results are presented.     

Existence of optimal solutions for general stochastic linear complementarity problems

In this paper we show the solvability of the expected residual minimization (ERM) formulation for the general stochastic linear complementarity problem (SLCP) under mild assumptions. The properties of the ERM formulation are dependent on the choice of NCP functions. We focus on the ERM formulations defined by the “min” NCP function and the penalized FB function, both of which are nonconvex programs on the nonnegative orthant.     

Regularizations for Stochastic Linear Variational Inequalities

This paper applies the Moreau–Yosida regularization to a convex expected residual minimization (ERM) formulation for a class of stochastic linear variational inequalities. To have the convexity of the corresponding sample average approximation (SAA) problem, we adopt the Tikhonov regularization. We show that any cluster point of minimizers of the Tikhonov regularization for the SAA problem is a minimizer of the ERM formulation with probability one as the sample size goes to infinity and the Tikhonov regularization parameter goes to zero. Moreover, we prove that the minimizer is the least \(l_2\) -norm solution of the ERM formulation. We also prove the semismoothness of the gradient of the Moreau–Yosida and Tikhonov regularizations for the SAA problem.     

Stochastic second-order-cone complementarity problems: expected residual minimization formulation and its applications

This paper considers a class of stochastic second-order-cone complementarity problems (SSOCCP), which are generalizations of the noticeable stochastic complementarity problems and can be regarded as the Karush–Kuhn–Tucker conditions of some stochastic second-order-cone programming problems. Due to the existence of random variables, the SSOCCP may not have a common solution for almost every realization . In this paper, motivated by the works on stochastic complementarity problems, we present a deterministic formulation called the expected residual minimization formulation for SSOCCP. We present an approximation method based on the Monte Carlo approximation techniques and investigate some properties related to existence of solutions of the ERM formulation. Furthermore, we experiment some practical applications, which include a stochastic natural gas transmission problem and a stochastic optimal power flow problem in radial network.     

Stochastic Nonlinear Complementarity Problem and?Applications to?Traffic Equilibrium under?Uncertainty

The expected residual minimization (ERM) formulation for the stochastic nonlinear complementarity problem (SNCP) is studied in this paper. We show that the involved function is a stochastic R ₀ function if and only if the objective function in the ERM formulation is coercive under a mild assumption. Moreover, we model the traffic equilibrium problem (TEP) under uncertainty as SNCP and show that the objective function in the ERM formulation is a stochastic R ₀ function. Numerical experiments show that the ERM-SNCP model for TEP under uncertainty has various desirable properties. This work was partially supported by a Grant-in-Aid from the Japan Society for the Promotion of Science. The authors thank Professor Guihua Lin for pointing out an error in Proposition 2.1 on an earlier version of this paper. The authors are also grateful to the referees for their insightful comments.     

Expected Residual Minimization Formulation for a Class of Stochastic Vector Variational Inequalities

This paper considers a class of vector variational inequalities. First, we present an equivalent formulation, which is a scalar variational inequality, for the deterministic vector variational inequality. Then we concentrate on the stochastic circumstance. By noting that the stochastic vector variational inequality may not have a solution feasible for all realizations of the random variable in general, for tractability, we employ the expected residual minimization approach, which aims at minimizing the expected residual of the so-called regularized gap function. We investigate the properties of the expected residual minimization problem, and furthermore, we propose a sample average approximation method for solving the expected residual minimization problem. Comprehensive convergence analysis for the approximation approach is established as well.