基于运输问题悖论的最大运量问题以及运价合理性的研究

该文提出了判断运输问题悖论是否存在的对偶规划条件以及目标函数差值模型,并针对运输悖论中存在的两个颇有实际意义的问题:增加运量而总运费不增的最大调整量问题以及产销地的单位运价不合理问题,给出了用以获得最大运量调整方案的最大供需量模型以及通过改变不合理运价来消除悖论的合理定价法,并辅以实例加以验证。     

基于部分基变量的LP问题矩阵算法

基于部分基变量提出了LP问题的矩阵算法. 该算法以最优基矩阵的一个充分必要条件为基础,首先将一个初始矩阵转化为右端项和检验数均满足要求的矩阵,再转为检验数满足要求的基矩阵,最后转化为最优基矩阵.该算法具有使用范围广、计算规模小、计算过程简化、计算机易于实现的优势.矩阵算法的核心运算是求逆矩阵的运算,提出了矩阵算法的求逆问题,讨论并给出了求逆快速算法,该算法充分利用了矩阵算法迭代过程中提供的原来的逆矩阵的信息经过简单的变换得到新的逆矩阵,该算法比直接求逆法计算效率更高.     

区间二次双层规划的最好最优解

双层规划问题是一类具有递阶结构的优化问题.在不确定的双层规划优化问题中,目标函数系数或约束条件系数为区间数的双层规划模型在实际问题中有着广泛的应用.在二次-线性双层规划模型的基础上,提出了上、下层目标函数以及约束条件系数均具有区间系数的二次-线性双层规划模型,给出了求解其最好最优解的方法.首先,通过选取约束条件中不同的基矩阵,求得区间二次-线性双层规划的可能最优解.再比较求得的全部可能最优解,便可得到区间二次-线性双层规划模型的最好最优解.最后给出数值算例验证该方法的有效性.     

伪逆矩阵及其在改进型BP神经网络中的应用

基于伪逆矩阵理论,将sigmoid型激励函数与隶属函数相结合,构造出了改进型三层BP神经网络模型。该网络模型可以确定隐含层神经元个数,并给出了权值直接确定算法下的最优权值矩阵,最优权值矩阵就是计算输入受激励矩阵的伪逆矩阵与输出向量的乘积,突出了改进型BP神经网络就是基于训练数据的矩阵方程求解的特殊表示。仿真实验验证了该网络具有极高的逼近精度,且运行时间较短。     

线性规划消耗系数矩阵灵敏度分析的某些探讨

讨论了线性规划模型中 ,消耗系数矩阵 A中某个基变量或某个约束方程的系数向量变化以及增减约束方程时 ,对最优基、最优解、目标函数值和影子价格的影响 .     

一个有经济意义的多目标规划模型的调整

本文在线性约束条件下 ,同时考虑三个目标函数的最优化 ,即线性函数、二次函数、分式函数 .对于已知的线性规划的最优基可行解 ,通过调整二次函数和分式函数中的系数向量和系数矩阵 ,使其成为这两个规划的最优解 .模型的改进有经济意义的解释     

具有带状系数矩阵的无限维线性规划的解法

研究一类每个约束条件有两个变量且每个变量出现在两个约束条件中的无限维线性规划.引入松弛变量后,得到约束方程组的系数矩阵为无限阶带状矩阵,用它的左逆以及属于零的特征向量可以表示这类问题的最优解.获得目标函数值收敛的一个充分条件.     

模糊线性系统的扰动分析

使用谱范数分析了模糊线性系统在三种情形下的扰动: (1)右端模糊向量有扰动, 系数矩阵不变; (2)系数矩阵有扰动,右端模糊向量不变; (3)系数矩阵和右端模糊向量都有扰动,并通过数值实例验证给出的扰动界的估计.     

目标函数系数与约束右端项同时变化的灵敏度分析

灵敏度分析应用很广,但是人们对灵敏度的讨论往往局限于单个参数发生变化对求解结果的影响.本文主要讨论目标函数系数C和约束右端项b两个参数同时改变对最优基、最优解及目标值的影响以及最优解发生变化时,如何求出新的最优解.     

基于一致性逼近的三角模糊数互补判断矩阵的排序方法

研究了元素为三角模糊数形式的互补判断矩阵的一致性和排序问题.分析了三角模糊数互补判断矩阵和三角模糊数互反判断矩阵之间的相互转换关系,提出了这两类判断矩阵完全一致性的概念并得到了三角模糊数互补判断矩阵的元素和排序权值之间的关系,在此基础上建立了一个多目标优化模型,通过求解该模型得到三角模糊数互补判断矩阵的排序向量,利用已有的模糊数比较大小公式得到方案的排序,最后给出了一个算例.     

On the Optimal Correction of Infeasible Systems of Linear Inequalities

We study the optimum correction of infeasible systems of linear inequalities through making minimal changes in the coefficient matrix and the right-hand side vector by using the Frobenius norm. It leads to a special structured unconstrained nonlinear and nonconvex problem, which can be reformulated as a one-dimensional parametric minimization problem such that each objective function corresponds to a trust region subproblem. We show that, under some assumptions, the parametric function is differentiable and strictly unimodal. We present optimally conditions, propose lower and upper bounds on the optimal value and discuss attainability of the optimal value. To solve the original problem, we propose a binary search method accompanied by a type of Newton–Lagrange method for solving the subproblem. The numerical results illustrate the effectiveness of the suggested method.

     

Inverse optimization: towards the optimal parameter set of inverse LP with interval coefficients

We study the inverse optimization problem in the following formulation: given a family of parametrized optimization problems and a real number called demand, determine for which values of parameters the optimal value of the objective function equals to the demand. We formulate general questions and problems about the optimal parameter set and the optimal value function. Then we turn our attention to the case of linear programming, when parameters can be selected from given intervals (“inverse interval LP”). We prove that the problem is NP-hard not only in general, but even in a very special case. We inspect three special cases—the case when parameters appear in the right-hand sides, the case when parameters appear in the objective function, and the case when parameters appear in both the right-hand sides and the objective function. We design a technique based on parametric programming, which allows us to inspect the optimal parameter set. We illustrate the theory by examples.     

Tikhonov regularization for infeasible absolute value equations

We investigate the optimum correction of an absolute value equation by minimally changing the coefficient matrix and right-hand side vector using Tikhonov regularization. Solving this problem is equivalent to minimizing the sum of fractional quadratic and quadratic functions. The primary difficulty with this problem is its nonconvexity. Nonetheless, we show that a global optimal solution to this problem can be found by solving an equation on a closed interval using the subgradient method. Some examples are provided to illustrate the efficiency and validity of the proposed method.     

The Inverse Problem for Numerical Determination of the Nonlinear Right-Hand Side in a Parabolic Equation

The article considers the inverse problem of determining the nonlinear right-hand side of a quasi-linear parabolic equation and proves a uniqueness theorem. A method is proposed for numerical solution of the inverse problem based on parametric representation of the sought coefficient. The inverse problem thus reduces to finding the error-minimizing vector of unknown coefficients of the parametric representation of the sought function.     

Inverse problem of simultaneously determining the right-hand side and the coefficient of a lower order derivative for a parabolic equation on the plane

We obtain existence and uniqueness theorems for the solution of the inverse problem of simultaneously determining the right-hand side and the coefficient of a lower-order derivative in a parabolic equation under an integral observation condition. We give explicit estimates for the maximum absolute value of the unknown right-hand side and the unknown coefficient of the equation with constants expressed via the input data of the problem. We present a nontrivial example of an inverse problem to which our theorems apply.     

一类不可微二次规划逆问题

本文求解了一类二次规划的逆问题,具体为目标函数是矩阵谱范数与向量无穷范数之和的最小化问题.首先将该问题转化为目标函数可分离变量的凸优化问题,提出用G-ADMM法求解.并结合奇异值阈值算法,Moreau-Yosida正则化算法,matlab优化工具箱的quadprog函数来精确求解相应的子问题.而对于其中一个子问题的精确求解过程中发现其仍是目标函数可分离变量的凸优化问题,由于其变量都是矩阵,所以采用适合多个矩阵变量的交替方向法求解,通过引入新的变量,使其每个子问题的解都具有显示表达式.最后给出采用的G-ADMM法求解本文问题的数值实验.数据表明,本文所采用的方法能够高效快速地解决该二次规划逆问题.     

Uniqueness in One Inverse Problem for the Elasticity System

We consider an inverse problem for the stationary elasticity system with constant Lame coefficients and a variable matrix coefficient depending on the spatial variables and frequency. The right-hand side contains a delta-function whose support (source) varies in some domain disjoint from the support of the variable coefficient. The inverse problem is to find the coefficient from the scattered wave measured at the same point at which the perturbation originates. A uniqueness theorem is proven. The proof bases on reduction of the inverse problem to a family of equations with the M. Riesz potential.     

Quantitative Stability Analysis of Two-Stage Stochastic Linear Programs with Full Random Recourse

Abstract

In this paper, we apply the parametric linear programing technique and pseudo metrics to study the quantitative stability of the two-stage stochastic linear programing problem with full random recourse. Under the simultaneous perturbation of the cost vector, coefficient matrix, and right-hand side vector, we first establish the locally Lipschitz continuity of the optimal value function and the boundedness of optimal solutions of parametric linear programs. On the basis of these results, we deduce the locally Lipschitz continuity and the upper bound estimation of the objective function of the two-stage stochastic linear programing problem with full random recourse. Then by adopting different pseudo metrics, we obtain the quantitative stability results of two-stage stochastic linear programs with full random recourse which improve the current results under the partial randomness in the second stage problem. Finally, we apply these stability results to the empirical approximation of the two-stage stochastic programing model, and the rate of convergence is presented.     

目标函数系数、约束右端项及系数矩阵A同时变化的灵敏度分析

本文将文献[1]中提出的对目标函数系数与约束右端项同时变化的灵敏度分析中的单一分量变化,改进为向量整体变化的一般性处理;并将系数矩阵A的同时变化一起考虑在内,重点针对可行性和对偶可行性都不满足情况下,将文献[1]采取的引入人工变量及参数大M的传统方法,改用联合算法进行处理,简单方便多了.     

Polyhedral functions and multiparametric linear programming

In a linear programming problem with a vector parameter appearing on the right-hand side, the minimum value of the objective is a polyhedral function of this parameter. We show how different characterizations of a polyhedral function correspond to different ways of solving the right-hand side multiparameteric linear programming problem.