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.
Highlights
The stochastic programming with recourse, as an important method for solving optimization problems with uncertain parameters, was first proposed by G
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)
Considering the discreteness of the random variables and the uncertainty of the probability distribution in the model, this paper introduces the complex method and designs a stochastic programming algorithm based on the complex method
Summary
How to cite this paper: Luo, Y.P. and Ma, X.S. (2020) A Complex Algorithm for Solving a Kind of Stochastic Programming. Journal of Applied Mathematics and Physics, 8, 1016-1030.
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