On the convergence of cutting-plane methods for robust optimization with ellipsoidal uncertainty sets
Optimization and Control
2019-04-03 v1 Computational Engineering, Finance, and Science
Numerical Analysis
Abstract
Recent advances in cutting-plane strategies applied to robust optimization problems show that they are competitive with respect to problem reformulations and interior-point algorithms. However, although its application with polyhedral uncertainty sets guarantees convergence, finite termination when using ellipsoidal uncertainty sets is not theoretically guaranteed. This paper demonstrates that the cutting-plane algorithm set out for ellipsoidal uncertainty sets in its more general form also converges in a finite number of steps.
Cite
@article{arxiv.1904.01244,
title = {On the convergence of cutting-plane methods for robust optimization with ellipsoidal uncertainty sets},
author = {Roberto Mínguez and Víctor Casero-Alonso},
journal= {arXiv preprint arXiv:1904.01244},
year = {2019}
}
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6 pages