Improved Decision Rule Approximations for Multi-Stage Robust Optimization via Copositive Programming release_rev_806fe20f-adb5-4317-822d-5b60608ba816

by Guanglin Xu, Grani A. Hanasusanto

Released as a article .

2021  

Abstract

We study decision rule approximations for generic multi-stage robust linear optimization problems. We consider linear decision rules for the case when the objective coefficients, the recourse matrices, and the right-hand sides are uncertain, and consider quadratic decision rules for the case when only the right-hand sides are uncertain. The resulting optimization problems are NP-hard but amenable to copositive programming reformulations that give rise to tight conservative approximations. We further enhance these approximations through new piecewise decision rule schemes. Finally, we prove that our proposed approximations are tighter than the state-of-the-art schemes and demonstrate their superiority through numerical experiments.
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Type  article
Stage   submitted
Date   2021-05-01
Version   v3
Language   en ?
arXiv  1808.06231v3
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