A Scalable Algorithm for Two-Stage Adaptive Linear Optimization

Dimitris Bertsimas; Shimrit Shtern

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arXiv · 1807.02812

A Scalable Algorithm for Two-Stage Adaptive Linear Optimization

Dimitris Bertsimas·Shimrit Shtern

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Abstract

The column-and-constraint generation (CCG) method was introduced by \citet{Zeng2013} for solving two-stage adaptive optimization. We found that the CCG method is quite scalable, but sometimes, and in some applications often, produces infeasible first-stage solutions, even though the problem is feasible. In this research, we extend the CCG method in a way that (a) maintains scalability and (b) always produces feasible first-stage decisions if they exist. We compare our method to several recently proposed methods and find that it reaches high accuracies faster and solves significantly larger problems.

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Dimitris Bertsimas, Shimrit Shtern. 2018-07-08. A Scalable Algorithm for Two-Stage Adaptive Linear Optimization. https://arxiv.org/abs/1807.02812

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