参量离散代数Riccati方程对称解的两类迭代算法

基于求线性矩阵方程约束解的修正共轭梯度法,针对源于低增益反馈设计和时滞控制系统中的一类参量离散代数Riccati方程,建立求其非零对称解的Newton-MCG算法和非精确Newton-MCG算法以及求其可逆对称解的T-MCG算法.(非精确)Newton-MCG算法仅要求Riccati方程存在非零对称解,对系数矩阵等没有附加限定,但所得对称解不能保证可逆性或正定性;在系数矩阵满足可控性等条件下,由T-MCG算法所得对称解是正定的.数值算例表明,两类迭代算法是有效的.     

参量连续代数Riccati方程对称解的两种迭代算法

基于求线性矩阵方程约束解的修正共轭梯度法,针对源于低增益反馈设计中的一类参量连续代数Riccati方程,建立求其非零对称解的两种互为补充的迭代算法,称之为变换-MCG算法和牛顿-MCG算法.在一定条件下,当Riccati方程存在可逆对称解或唯一对称正定解时,由变换-MCG算法所得对称解具备可逆性或正定性.牛顿-MCG算法仅要求Riccati方程存在非零对称解,对系数矩阵等没有附加限定,但所得对称解不能保证可逆性或正定性.数值算例表明,两种迭代算法是有效的.     

矩阵方程X-A*X~(-1)A+B*X~(-2)B=I正定解存在的充分条件

通过构造单调有界迭代序列,研究矩阵方程X-A~*X~(-1)A+B~*X~(-2)B=I的艾米特正定解.给出了方程正定解存在的充分条件及正定解的范围.     

Delta算子Riccati方程研究的新结果

基于Delta算子描述，统一研究连续时间代数Riccati方程(CARE)和离散时间代数Riccati方程(DARE)的定界估计问题，提出了统一代数Riccati方程(UARE)解矩阵的上下界，给出UARE中P与R和Q的几个基本关系．     

矩阵方程X+A^{*}X^{-q}A=Q(q\geq 1)的Hermitian正定解

本文研究矩阵方程X A~*X~(-q)A=Q(q≥1)的Hermitian正定解,给出了存在正定解的充分条件和必要条件,构造了求解的迭代方法.最后还用数值例子验证了迭代方法的可行性和有效性.     

矩阵方程X+A*X-qA=Q(q≥1)的 Hermitian正定解

本文研究矩阵方程X+A*X-qA=Q(q≥1)的Hermitian正定解,给出了存在正定解的充分条件和必要条件,构造了求解的迭代方法.最后还用数值例子验证了迭代方法的可行性和有效性.     

关于矩阵方程X+A*X-αA+B*X-βB=I的正定解

本文研究了矩阵方程X+A*X-αA+B*X-βB=I在α,β∈(0,1]时的正定解.利用单调有界极限存在准则,构造三种迭代算法,获得了方程的正定解,拓宽了此类方程的求解方法.数值算例说明算法的可行性.     

矩阵方程X-A*XqA=I(0＜q＜1)Hermite正定解的扰动分析

首先证明了非线性矩阵方程X-A~*X~qA=I(0相似文献   

矩阵方程X-A*X-2A=Q的正定解及其扰动分析

研究非线性矩阵方程X-A*X-2A=Q(Q>0)的Hermite正定解及其扰动问题.给出了该方程存在唯-Hermite正定解的充分条件及解的迭代计算公式.在此条件下,给出了该唯一解的扰动界及正定解条件数的一种表达式,并用数值例子对所得结果进行了说明.     

离散对偶代数Riccati方程异类约束解的双迭代算法

利用逆矩阵的Neumann级数形式,将在离散时间跳跃线性二次控制问题中遇到的含未知矩阵之逆的离散对偶代数Riccati方程(DCARE)转化为高次多项式矩阵方程组,然后采用牛顿算法求高次多项式矩阵方程组的异类约束解,并采用修正共轭梯度法求由牛顿算法每一步迭代计算导出的线性矩阵方程组的异类约束解或者异类约束最小二乘解,建立求DCARE的异类约束解的双迭代算法.双迭代算法仅要求DCARE有异类约束解,不要求它的异类约束解唯一,也不对它的系数矩阵做附加限定.数值算例表明,双迭代算法是有效的.     

Using dual network bounds in algorithms for solving generalized set packing partitioning problems

This article deals with a method to compute bounds in algorithms for solving the generalized set packing/partitioning problems. The problems under investigation can be solved by the branch and bound method. Linear bounds computed by the simplex method are usually used. It is well known that this method breaks down on some occasions because the corresponding linear programming problems are degenerate. However, it is possible to use the dual (Lagrange) bounds instead of the linear bounds. A partial realization of this approach is described that uses a network relaxation of the initial problem. The possibilities for using the dual network bounds in the approximation techniques to solve the problems under investigation are described.     

Copula界在多元VaR中的应用

首先介绍了多元随机变量和的边界,以及由此导出的VaR边界,然后全面总结了有关Copula下界的公式,而Copula下界及其对偶函数分别构成计算VaR边界的依据.最后根据VaR边界的数值算法,针对不同的Copula下界,分多种情景详细分析了VaR的边界范围.关于上证指数和深成指数收益率序列的实证分析发现.Spearman相关系数和正象限相依对VaR界的收窄作用最强.     

Bounds to eigenvalues of the Laplacian on L-shaped domain by variational methods

In this study, the bounds for eigenvalues of the Laplacian operator on an L-shaped domain are determined. By adopting some special functions in Goerisch method for lower bounds and in traditional Rayleigh–Ritz method for upper bounds, very accurate bounds to eigenvalues for the problem are obtained. Numerical results show that these functions can also be successfully used to solve the problem on the region with other reentrant angle.     

Calculation of bounds on variables satisfying nonlinear inequality constraints

Existing techniques for solving nonconvex programming problems often rely on the availability of lower and upper bounds on the problem variables. This paper develops a method for obtaining these bounds when not all of them are availablea priori. The method is a generalization of the method of Fourier which finds bounds on variables satisfying linear inequality constraints. First, nonlinear inequality constraints are converted to equivalent sets of separable constraints. Generalized variable elimination techniques are used to reduce these to constraints in one variable. Bounds on that variable are obtained and an inductive process yields bounds on the others.Research Sponsored by the Office of Naval Research Grant N00014-89-J-1537.     

Sharp Bounds for Symmetric and Asymmetric Diophantine Approximation

In 2004, Tong found bounds for the approximation quality of a regular continued fraction convergent to a rational number, expressed in bounds for both the previous and next approximation. The authors sharpen his results with a geometric method and give both sharp upper and lower bounds. The asymptotic frequencies that these bounds occur are also calculated.     

Upper and lower bounds for the solutions of Markov renewal equations

Upper and lower bounds are studied for the solutions of Markov renewal equations. Some of their special cases are derived under specific marginal conditons and in an alternating environment. The method to construct the bounds is also explained in detail. At the end, these bounds are applied to a shock model and an age-dependent branching process under Markovian environment.     

Evaluating Improvements for Spacings of Order Statistics

We evaluate sharp upper bounds for the consecutive spacings of order statistics from an i.i.d. sample, measured in scale units generated by various central absolute moments of the parent distribution. The bounds are based on the projection method combined with the Hölder inequalities. We characterize the probability distributions for which the bounds are attained. We also evaluate the so obtained bounds numerically and compare them with other existing bounds.     

Sharp bounds on the volume fractions of two materials in a two-dimensional body from electrical boundary measurements: the translation method

We deal with the problem of estimating the volume of inclusions using a small number of boundary measurements in electrical impedance tomography. We derive upper and lower bounds on the volume fractions of inclusions, or more generally two phase mixtures, using two boundary measurements in two dimensions. These bounds are optimal in the sense that they are attained by certain configurations with some boundary data. We derive the bounds using the translation method which uses classical variational principles with a null Lagrangian. We then obtain necessary conditions for the bounds to be attained and prove that these bounds are attained by inclusions inside which the field is uniform. When special boundary conditions are imposed the bounds reduce to those obtained by Milton and these in turn are shown here to reduce to those of Capdeboscq–Vogelius in the limit when the volume fraction tends to zero. The bounds of this article, and those of Milton, work for inclusions of arbitrary volume fractions. We then perform some numerical experiments to demonstrate how good these bounds are.     

Reformulation and a Lagrangian heuristic for lot sizing problem on parallel machines

We consider the capacitated lot sizing problem with multiple items, setup time and unrelated parallel machines. The aim of the article is to develop a Lagrangian heuristic to obtain good solutions to this problem and good lower bounds to certify the quality of solutions. Based on a strong reformulation of the problem as a shortest path problem, the Lagrangian relaxation is applied to the demand constraints (flow constraint) and the relaxed problem is decomposed per period and per machine. The subgradient optimization method is used to update the Lagrangian multipliers. A primal heuristic, based on transfers of production, is designed to generate feasible solutions (upper bounds). Computational results using data from the literature are presented and show that our method is efficient, produces lower bounds of good quality and competitive upper bounds, when compared with the bounds produced by another method from the literature and by high-performance MIP software.     

Influence of the Eigenvalue Spectrum on the Convergence Rate of the Conjugate Gradient Method

The analogy between the conjugate gradient method for the solutionof linear simultaneous equations and a polynomial curve fittingproblem provides a means of determining bounds for the convergencerate of the conjugate gradient method. Chebyshev polynomialsare used to give bounds for the convergence rate associatedwith the main group of eigenvalues, assuming that the eigenvaluesare closely spaced within the group. Additional penalty functionsare developed to correct the convergence rate bounds when outlyingeigenvalues are present.