Bärmann A, Heidt A, Martin A, Pokutta S, Thurner C (2015)
Publication Language: English
Publication Type: Journal article
Publication year: 2015
Publisher: Springer Verlag
Book Volume: 13
Pages Range: 151-193
Journal Issue: 2
DOI: 10.1007/s10287-015-0243-0
Robust optimization is an important technique to immunize optimization problems against data uncertainty. In the case of a linear program and an ellipsoidal uncertainty set, the robust counterpart turns into a second-order cone program. In this work, we investigate the efficiency of linearizing the second-order cone constraints of the latter. This is done using the optimal linear outer-approximation approach by Ben-Tal and Nemirovski (Math Oper Res 26:193--205, 2001) from which we derive an optimal inner approximation of the second-order cone. We examine the performance of this approach on various benchmark sets including portfolio optimization instances as well as (robustified versions of) the MIPLIB and the SNDlib.
APA:
Bärmann, A., Heidt, A., Martin, A., Pokutta, S., & Thurner, C. (2015). Polyhedral approximation of ellipsoidal uncertainty sets via extended formulations: a computational case study. Computational Management Science, 13(2), 151-193. https://doi.org/10.1007/s10287-015-0243-0
MLA:
Bärmann, Andreas, et al. "Polyhedral approximation of ellipsoidal uncertainty sets via extended formulations: a computational case study." Computational Management Science 13.2 (2015): 151-193.
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