Theory and Applications of Robust Optimization
Optimization and Control
2010-10-27 v1 Computational Engineering, Finance, and Science
Abstract
In this paper we survey the primary research, both theoretical and applied, in the area of Robust Optimization (RO). Our focus is on the computational attractiveness of RO approaches, as well as the modeling power and broad applicability of the methodology. In addition to surveying prominent theoretical results of RO, we also present some recent results linking RO to adaptable models for multi-stage decision-making problems. Finally, we highlight applications of RO across a wide spectrum of domains, including finance, statistics, learning, and various areas of engineering.
Cite
@article{arxiv.1010.5445,
title = {Theory and Applications of Robust Optimization},
author = {Dimitris Bertsimas and David B. Brown and Constantine Caramanis},
journal= {arXiv preprint arXiv:1010.5445},
year = {2010}
}
Comments
50 pages