A Robust Optimization Perspective on Stochastic Programming

By Xin Chen, Melvyn Sim, Peng Sun. 2007

403 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Management Science and Operations Research, Statistics and Probability

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Citations in the corpus

Certifying Some Distributional Robustness with Principled Adversarial Training (2017, background): “Previous approaches to distributional robustness have considered finitedimensional parametrizations for P , such as constraint sets for moments, support, or directional deviations (Chen et al., 2007; Delage & Ye, 2010; Goh & Sim, 2010), as well as non-parametric distances for probability measures su”
Enterprise-wide optimization for industrial demand side management: Fundamentals, advances, and perspectives (2016, background): “Since then, probabilistic guarantees have been derived for a number of different robust formulations (Bertsimas et al., 2004; Bertsimas & Sim, 2006; Chen et al., 2007).”
Robust stochastic optimization for reservoir operation (2015, background): “Theorem 2 in Goh and Sim [2010] which is generalized from Theorem 1 in Chen et al. [2008] can serve this purpose.”
Distributionally robust joint chance constraints with second-order moment information (2011, neutral): “[10], and Kuhn et al.”
Adaptive Distributionally Robust Optimization (2019, methodology): “…for instance, ˇ Z´aˇckov´a 1966, Dupacova 1987, Breton and EI Hachem 1995, Shapiro and Kleywegt 2002, Shapiro and Ahmed 2004), and recently to distributionally robust optimization models (see, for instance, Chen et al. 2007, Chen and Sim 2009, Popescu 2007, Delage and Ye 2010, Xu and Mannor 2012).”
Theory and Applications of Robust Optimization (2010, background): “See also the work of Chen, Sim, and Sun, in [41], where more general deviation measures are considered, leading to improved probability guarantees.”
Distributionally Robust Optimization: A Review (2019, background): “21Restricting the recourse decision function y(ξ) to the class of functions that are affinelydependent on ξ, referred to as linear decision rules, is an approach to derive computationally tractable problems to approximate stochastic programming and robust optimization models [74, 75, 24].”
Certifiable Distributional Robustness with Principled Adversarial Training (2017, background): “…robustness have considered ﬁnite-dimensional parametrizations for P , such as constraint sets for moments, support, or directional deviations [14, 17, 21], as well as non-parametric distances for probability measures such as f -divergences [4, 5, 32, 29, 18, 35], and Wasserstein distances…”
Infrastructure Planning for Electric Vehicles with Battery Swapping (2013, methodology): “We develop our models by utilizing recent advances in distributionally-robust optimization [20] [21] [29], a framework for making optimal decisions under limited and/or imprecise information.”
Recent advances in robust optimization: An overview (2014, background): “2.3.2 Optimal and approximate policies Chen et al. (2007) suggest a tractable approximation for a class of multistage chance-constrained linear programming problems, which converts the original formulation into a second-order cone programming problem.”
Data-driven robust optimization (2013, methodology): “In [22], the authors propose an uncertainty set based on the forward and backward deviations of a distribution.”
Distributionally Robust Optimization and Its Tractable Approximations (2010, methodology): “The deflected linear decision rule (DLDR) proposed by Chen et al. (2008) exploited the structure of the model constraints (by solving a series of subproblems based on the model parameters) to obtain an even more flexible decision rule. We adapt this idea here, similarly solving a series of subproble”
The Stochastic Vehicle Routing Problem for Minimum Unmet Demand (2009, neutral): “the link between the chance-constrained technique and robust optimization [32, 12, 19, 14].”