有无穷多最优解线性规划问题

本文给出了线性规划有无穷多最优解的判别条件及其求出所有最优解的具体方法．     

线性规划无穷多最优解的讨论

利用线性规划单纯形表对线性规划原问题存在无穷多最优解和对偶问题存在无穷多最优解的情况进行了讨论,并分析了对偶问题存在无穷多最优解情况下的影子价格的方向性。最后以实例说明了各种情况。对初学者加深理解及决策者决策参考有一定帮助     

线性规划问题退化最优基可行解的性质

本通过分析两用阶段法求解线性规划初始可行解的一个例子,归纳了线性规划问题退化的最优基可行解的性质,包括同一退化最优基可行解不同表示,有无穷多最优解的表示。     

区间数线性规划问题的解法

基于区间数与实数之间的关系,提出了区间数线性规划的激进最优解,保守最优解的定义.利用约束集之间以及目标函数值之间的关系,在原有区间数线性规划的基础之上,给出了两个求解激进最优解、保守最优解的方法.数值例子验证了该方法的有效性和可行性.     

用罚函数求解线性双层规划的全局优化方法

用罚函数法将线性双层规划转化为带罚函数子项的双线性规划问题，由于其全局最优解可在约束域的极点上找到，利用对偶理论给出了一种求解该双线性规划的方法，并证明当罚因子大于某一正数时，双线性规划的解就是原线性双层规划的全局最优解。     

线性规划的对偶理论在图解法中的应用

对于多个变量两个约束的线性规划,首先利用线性规划的对偶理论,写出其对偶问题;其次利用图解法求出对偶问题的最优解,最后利用互补松弛条件求出原问题的最优解.     

带关联的双层多目标决策研究

本文研究是线性的双层多目标决策.根据线性规划的对偶理论证明了双层多目标决策的可行集的连通性;利用s*-最优均衡解的概念,求得双层多目标规划的偏好满意解;最后,我们得到了满意解的有效性,并在极点得到.     

多目标运输问题的Fuzzy线性规划解法

经典运输问题是一类特殊的单目标线性规划问题，可用表上作业法或单纯形法求其最优解。近年来，许多学研究了多目标运输问题，提出了相应的求解算法。本应用Fuzzy线性规划的方法，给出了多目标运输问题的又一求解算法。     

线性分式规划最优解集的求法

本文使用多面集的表示定理，导出了线性分式规划最优解集的结构，并给出确定全部最优解的计算步骤。     

对LP问题有无穷多最优解判别定理证明的补充

本文指出了文献［１］中关于“无穷多最优解判别定理”证明中的不足，并给出了完整的证明     

ON AN EFFICIENT IMPLEMENTATION OF THE FACE ALGORITHM FOR LINEAR PROGRAMMING＊

In this paper, we consider the solution of the standard linear programming [Lt＇）. A remarkable result in LP claims that all optimal solutions form an optimal face of the underlying polyhedron. In practice, many real-world problems have infinitely many optimal solutions and pursuing the optimal face, not just an optimal vertex, is quite desirable. The face algorithm proposed by Pan [19] targets at the optimal face by iterating from face to face, along an orthogonal projection of the negative objective gradient onto a relevant null space. The algorithm exhibits a favorable numerical performance by comparing the simplex method. In this paper, we further investigate the face algorithm by proposing an improved implementation. In exact arithmetic computation, the new algorithm generates the same sequence as Pan＇s face algorithm, but uses less computational costs per iteration, and enjoys favorable properties for sparse problems.     

The multiple-partners assignment game with heterogeneous sales and multi-unit demands: competitive equilibria

A multiple-partners assignment game with heterogeneous sales and multi-unit demands consists of a set of sellers that own a given number of indivisible units of potentially many different goods and a set of buyers who value those units and want to buy at most an exogenously fixed number of units. We define a competitive equilibrium for this generalized assignment game and prove its existence by using only linear programming. In particular, we show how to compute equilibrium price vectors from the solutions of the dual linear program associated to the primal linear program defined to find optimal assignments. Using only linear programming tools, we also show (i) that the set of competitive equilibria (pairs of price vectors and assignments) has a Cartesian product structure: each equilibrium price vector is part of a competitive equilibrium with all optimal assignments, and vice versa; (ii) that the set of (restricted) equilibrium price vectors has a natural lattice structure; and (iii) how this structure is translated into the set of agents?? utilities that are attainable at equilibrium.     

求标准线性规划问题的一种截解法

本提出了求解线性规划问题的一种新思路，就是通过平行移动目标函数等值面，即改变目标函数作为参数的取值来截取基本可行解，甚至最优解。值得注意的是，本算法可能会克服由退化引起的迭代循环。     

ON ALTERNATIVE OPTIMAL SOLUTIONS TO QUASIMONOTONIC PROGRAMMING WITH LINEAR CONSTRAINTS

In this paper, the nonlinear programming problem with quasimonotonic （ both quasiconvex and quasiconcave ）objective function and linear constraints is considered. With the decomposition theorem of polyhedral sets, the structure of optimal solution set for the programming problem is depicted. Based on a simplified version of the convex simplex method, the uniqueness condition of optimal solution and the computational procedures to determine all optimal solutions are given, if the uniqueness condition is not satisfied. An illustrative example is also presented.     

An inexact approach to solving linear semi-infinite programming problems

In this paper, we propose an “inexact solution” approach to deal with linear semi-infinite programming problems with finitely many variables and infinitely many constraints over a compact metric space. A general convergence proof with some numerical examples are given and the advantages of using this approach are discussed     

On the Stability of the Feasible Set in Linear Optimization

This paper deals with the stability of two families of linear optimization problems, each one formed by the dual problems to the members of the other family. We characterize the problems of these families that are stable in the sense that they remain consistent (inconsistent) under sufficiently small arbitrary perturbations of all the data. This characterization is established in terms of the lower semicontinuity property of the feasible set mapping and the boundedness of the optimal set of the corresponding coupled problem. Other continuity properties of the feasible set mapping are also derived. This stability theory extends some well-known theorems of Williams and Robinson on the stability of ordinary linear programming problems to linear optimization problems with infinitely many variables or constraints.     

Hybrid pseudoviscosity approximation schemes for equilibrium problems and fixed point problems of infinitely many nonexpansive mappings

In this paper, we introduce hybrid pseudoviscosity approximation schemes with strongly positive bounded linear operators for finding a common element of the set of solutions of an equilibrium problem and the set of common fixed points of infinitely many nonexpansive mappings in the setting of Hilbert spaces. We prove the strong convergence of the sequences generated by our scheme to a solution of an equilibrium problem which is also a common fixed point of infinitely many nonexpansive mappings. Our results can be treated as extension and improvement of the corresponding results appeared in the literature in the recent past.     

An interval approach based on expectation optimization for fuzzy random bilevel linear programming problems

This paper considers a class of bilevel linear programming problems in which the coefficients of both objective functions are fuzzy random variables. The main idea of this paper is to introduce the Pareto optimal solution in a multi-objective bilevel programming problem as a solution for a fuzzy random bilevel programming problem. To this end, a stochastic interval bilevel linear programming problem is first introduced in terms of α-cuts of fuzzy random variables. On the basis of an order relation of interval numbers and the expectation optimization model, the stochastic interval bilevel linear programming problem can be transformed into a multi-objective bilevel programming problem which is solved by means of weighted linear combination technique. In order to compare different optimal solutions depending on different cuts, two criterions are given to provide the preferable optimal solutions for the upper and lower level decision makers respectively. Finally, a production planning problem is given to demonstrate the feasibility of the proposed approach.     

Duality in infinite dimensional linear programming

We consider the class of linear programs with infinitely many variables and constraints having the property that every constraint contains at most finitely many variables while every variable appears in at most finitely many constraints. Examples include production planning and equipment replacement over an infinite horizon. We form the natural dual linear programming problem and prove strong duality under a transversality condition that dual prices are asymptotically zero. That is, we show, under this transversality condition, that optimal solutions are attained in both primal and dual problems and their optimal values are equal. The transversality condition, and hence strong duality, is established for an infinite horizon production planning problem.This material is based on work supported by the National Science Foundation under Grant No. ECS-8700836.     

Lagrangian duality and saddle points for sparse linear programming

The sparse linear programming(SLP) is a linear programming problem equipped with a sparsity constraint, which is nonconvex, discontinuous and generally NP-hard due to the combinatorial property involved.In this paper, by rewriting the sparsity constraint into a disjunctive form, we present an explicit formula of the Lagrangian dual problem for the SLP, in terms of an unconstrained piecewise-linear convex programming problem which admits a strong duality under bi-dual sparsity consistency. Furthermore, we show a saddle point theorem based on the strong duality and analyze two classes of stationary points for the saddle point problem. At last,we extend these results to SLP with the lower bound zero replaced by a certain negative constant.