Balinski—Tucker simplex tableaus: Dimensions, degeneracy degrees, and interior points of optimal faces

This paper shows the relationship between degeneracy degrees and multiple solutions in linear programming (LP) models. The usual definition of degeneracy is restricted to vertices of a polyhedron. We introduce degeneracy for nonempty subsets of polyhedra and show that for LP-models for which the feasible region contains at least one vertex it holds that the dimension of the optimal face is equal to the degeneracy degree of the optimal face of the corresponding dual model. This result is obtained by means of the so-called Balinski—Tucker (B—T) Simplex Tableaus. Furthermore, we give a strong polynomial algorithm for constructing such a B—T Simplex Tableau when a solution in the relative interior of the optimal face is known. © 1998 The Mathematical Programming Society, Inc. Published by Elsevier Science B.V.     

Zero duality gap for a class of nonconvex optimization problems

By an equivalent transformation using thepth power of the objective function and the constraint, a saddle point can be generated for a general class of nonconvex optimization problems. Zero duality gap is thus guaranteed when the primal-dual method is applied to the constructed equivalent form.The author very much appreciates the comments from Prof. Douglas J. White.     

A Multi-Stage Stochastic Integer Programming Approach for Capacity Expansion under Uncertainty

This paper addresses a multi-period investment model for capacity expansion in an uncertain environment. Using a scenario tree approach to model the evolution of uncertain demand and cost parameters, and fixed-charge cost functions to model the economies of scale in expansion costs, we develop a multi-stage stochastic integer programming formulation for the problem. A reformulation of the problem is proposed using variable disaggregation to exploit the lot-sizing substructure of the problem. The reformulation significantly reduces the LP relaxation gap of this large scale integer program. A heuristic scheme is presented to perturb the LP relaxation solutions to produce good quality integer solutions. Finally, we outline a branch and bound algorithm that makes use of the reformulation strategy as a lower bounding scheme, and the heuristic as an upper bounding scheme, to solve the problem to global optimality. Our preliminary computational results indicate that the proposed strategy has significant advantages over straightforward use of commercial solvers.     

A class of methods for solving large,convex quadratic programs subject to box constraints

In this paper we analyze conjugate gradient-type algorithms for solving convex quadratic programs subject only to box constraints (i.e., lower and upper bounds on the variables). Programs of this type, which we denote by BQP, play an important role in many optimization models and algorithms. We propose a new class of finite algorithms based on a nonfinite heuristic for solving a large, sparse BQP. The numerical results suggest that these algorithms are competitive with Dembo and Tulowitzski's (1983) CRGP algorithm in general, and perform better than CRGP for problems that have a low percentage of free variables at optimality, and for problems with only nonnegativity constraints.     

A Convex Envelope Formula for Multilinear Functions

Convex envelopes of multilinear functions on a unit hypercube arepolyhedral. This well-known fact makes the convex envelopeapproximation very useful in the linearization of non-linear 0–1programming problems and in global bilinear optimization. This paperpresents necessary and sufficient conditions for a convex envelope to be apolyhedral function and illustrates how these conditions may be used inconstructing of convex envelopes. The main result of the paper is a simpleanalytical formula, which defines some faces of the convex envelope of amultilinear function. This formula proves to be a generalization of the wellknown convex envelope formula for multilinear monomial functions.     

求解单阶段随机规划的一种光滑逼近法

本文借助某种离散方式把单阶段随机规划问题转化为具有多个约束的确定性非线性规划，然后利用极大熵函数方法，把此确定性规划转化为只带简单约束的非线性规划，由此提出了求解这种随机规划的光滑逼近法，同时给出了该法的收敛性分析，较好地克服了因提高离散精度导致约束函数个数迅速增大所带来的求解困难．     

Optimization with additional variables and constraints

Norton, Plotkin and Tardos proved that—loosely spoken, an LP problem is solvable in time O(Tq^k+1) if deleting k fixed columns or rows, we obtain a problem which can be solved by an algorithm that makes at most T steps and q comparisons. This paper improves this running time to O(Tq^k).     

Semiconcavity results for constrained optimal control problems in a half-space

In this paper we give semiconcavity results for the value function of some constrained optimal control problems with infinite horizon in a half-space. In particular, we assume that the control space is the l¹-ball or the l^∞-ball in Rn.     

线性规划在最优火力分配辅助决策中的应用

采用层次分析法和线性规划法对坦克连冲击时目标价值和火力分配问题进行了分析.在理论分析的基础上,用C++Builder开发火力分配辅助决策软件,该软件以报告文件的形式显示目标价值结果,以报告文件或图形的方式显示火力分配结果.     

多属性决策的组合赋权法

鉴于多属性决策问题中主观赋权法和客观赋权法各有其自身固有的特点，本文依据优化理论提出一种集成方法确定属性权重，该方法既充分利用客观信息，又反映决策者的主观偏好，从而使确定的属性权重更加合理、实用．