Volumetric barrier decomposition algorithms for two-stage stochastic linear semi-infinite programming

In this paper, we study the two-stage stochastic linear semi-infinite programming with recourse to handle uncertainty in data defining (deterministic) linear semi-infinite programming. We develop and analyze volumetric barrier decomposition-based interior point methods for solving this class of optimization problems, and present a complexity analysis of the proposed algorithms. We establish our convergence analysis by … Read more

Mitigating Uncertainty via Compromise Decisions in Two-stage Stochastic Linear Programming

Stochastic Programming (SP) has long been considered as a well-justified yet computationally challenging paradigm for practical applications. Computational studies in the literature often involve approximating a large number of scenarios by using a small number of scenarios to be processed via deterministic solvers, or running Sample Average Approximation on some genre of high performance machines … Read more

A Warm-Start Approach for Large-Scale Stochastic Linear Programs

We describe a method of generating a warm-start point for interior point methods in the context of stochastic programming. Our approach exploits the structural information of the stochastic problem so that it can be seen as a structure-exploiting initial point generator. We solve a small-scale version of the problem corresponding to a reduced event tree … Read more

Smooth minimization of two-stage stochastic linear programs

This note presents an application of the smooth optimization technique of Nesterov for solving two-stage stochastic linear programs. It is shown that the original O(1/e) bound of Nesterov on the number of main iterations required to obtain an e-optimal solution is retained. Citation Technical Report, School of Industrial & Systems Engineering, Georgia Institute of Technology, … Read more