Technical Note—Minimax Procedure for a Class of Linear Programs under Uncertainty

Raj Jagannathan

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Risk and Portfolio Optimization Advanced Optimization Algorithms Research Optimization and Mathematical Programming

Abstract

We consider a linear programming problem with random aij and bi elements that have known (finite) mean and variance, but whose distribution functions are otherwise unspecified. A minimax solution of the stochastic programming model is obtained by solving an equivalent deterministic convex programming problem. We derive these deterministic equivalents under different assumptions regarding the stochastic nature of the random parameters.

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