Author:
Lin Fengming,Fang Xiaolei,Gao Zheming
Abstract
<p style='text-indent:20px;'>In this paper, we survey the primary research on the theory and applications of distributionally robust optimization (DRO). We start with reviewing the modeling power and computational attractiveness of DRO approaches, induced by the ambiguity sets structure and tractable robust counterpart reformulations. Next, we summarize the efficient solution methods, out-of-sample performance guarantee, and convergence analysis. Then, we illustrate some applications of DRO in machine learning and operations research, and finally, we discuss the future research directions.</p>
Publisher
American Institute of Mathematical Sciences (AIMS)
Subject
Applied Mathematics,Control and Optimization,Algebra and Number Theory,Applied Mathematics,Control and Optimization,Algebra and Number Theory
Reference205 articles.
1. F. Alizadeh, D. Goldfarb.Second-order cone programming, Mathematical Programming, 95 (2003), 3-51.
2. K. B. Athreya and S. N. Lahiri, Measure Theory and Probability Theory, Springer Science & Business Media, 2006.
3. I. E. Bardakci and C. M. Lagoa, Distributionally robust portfolio optimization, in 2019 IEEE 58th Conference on Decision and Control (CDC), IEEE, (2019), 1526–1531.
4. G. Bayraksan and D. K. Love, Data-driven stochastic programming using phi-divergences, in The Operations Research Revolution, INFORMS, (2015), 1–19.
5. A. Ben-Tal, D. den Hertog, A. De Waegenaere, B. Melenberg, G. Rennen.Robust solutions of optimization problems affected by uncertain probabilities, Management Science, 59 (2013), 341-357.
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