Improved Decision Rule Approximations for Multistage Robust Optimization via Copositive Programming

Abstract

Improved decision rule approximations for multistage robust optimization via copositive programming Previous research in the field has proposed several approaches to tackle multistage robust optimization problems, but they are often limited in their applicability. These existing methods either fail to handle cases where recourse matrices are uncertain or struggle to handle large-scale problems effectively. In their paper titled “Improved decision rule approximations for multistage robust optimization via copositive programming,” Guanglin Xu and Grani A. Hanasusanto contribute to the robust optimization literature by presenting a novel solution method. Their approach utilizes convex conic techniques and aims to address the general case of multistage robust optimization, where uncertainty exists in the recourse matrices. One significant advantage of their proposed method is its ability to scale well with large-sized instances, overcoming a common limitation faced by previous methods. Through numerical experiments on various simulated applications, Xu and Hanasusanto demonstrate the superiority of their algorithm over existing state-of-the-art methods.