TITLE:
A Complex Algorithm for Solving a Kind of Stochastic Programming
AUTHORS:
Yunpeng Luo, Xinshun Ma
KEYWORDS:
Stochastic Programming with Recourse, Probability Distribution with Linear Partial Information, Maximized Minimum Expectation, Complex Algorithm
JOURNAL NAME:
Journal of Applied Mathematics and Physics,
Vol.8 No.6,
June
4,
2020
ABSTRACT: Considering that the probability distribution of random variables in stochastic programming usually has incomplete information due to a perfect sample data in many real applications, this paper discusses a class of two-stage stochastic programming problems modeling with maximum minimum expectation compensation criterion (MaxEMin) under the probability distribution having linear partial information (LPI). In view of the nondifferentiability of this kind of stochastic programming modeling, an improved complex algorithm is designed and analyzed. This algorithm can effectively solve the nondifferentiable stochastic programming problem under LPI through the variable polyhedron iteration. The calculation and discussion of numerical examples show the effectiveness of the proposed algorithm.