SOLUTION OF AND BOUNDING IN A LINEARLY CONSTRAINED OPTIMIZATION PROBLEM WITH CONVEX, POLYHEDRAL OBJECTIVE FUNCTION

被引：2
|
作者：
PREKOPA, A
LI, WZ
机构：
[1] RUTCOR - Rutgers Center for Operations Research, Rutgers University, New Brunswick, 08903, NJ
来源：
MATHEMATICAL PROGRAMMING | 1995年 / 70卷 / 01期
基金：
美国国家科学基金会;
关键词：
STOCHASTIC PROGRAMMING; POLYHEDRAL FUNCTIONS; SIMPLICIAL FUNCTIONS;
D O I：
10.1007/BF01585925
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
A dual method is presented to solve a linearly constrained optimization problem with convex, polyhedral objective function, along with a fast bounding technique, for the optimum value. The method can be used to solve problems, obtained from LPs, where some of the constraints are not required to be exactly satisfied but are penalized by piecewise linear functions, which are added to the objective function of the original problem. The method generalizes an earlier solution technique developed by Prekopa (1990). Applications to stochastic programming are also presented.
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页码：1 / 16
页数：16
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