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Improved Decision Rule Approximations for Multi-Stage Robust Optimization via Copositive Programming

Guanglin Xu (gxu***at***umn.edu)
Grani Hanasusanto (grani.hanasusanto***at***utexas.edu)

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.

Keywords: Multi-stage robust optimization; decision rules; piecewise decision rules; conservative approximation; copositive programming; semidefinite programming

Category 1: Robust Optimization

Category 2: Linear, Cone and Semidefinite Programming

Category 3: Other Topics (Dynamic Programming )

Citation:

Download: [PDF]

Entry Submitted: 08/19/2018
Entry Accepted: 08/19/2018
Entry Last Modified: 09/08/2018

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