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Robust optimization is a tractable and expressive technique for decision-making under uncertainty, but it can lead to overly conservative decisions when pessimistic assumptions are made on the uncertain parameters.
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A class of wasserstein metrics for probability distributions
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Variational analysis
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An exact algorithm for the capacitated facility location problems with single sourcing
K. Holmberg, M. Rönnqvist, and D. Yuan · 1999
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Robust solutions of Linear Programming problems contaminated with uncertain data
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Financial Risk and Heavy Tails , volume 1 of Handbooks in Finance
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Scenario reduction in stochastic programming
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The Price of Robustness
D. Bertsimas and M. Sim · 2004
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Selected topics in robust convex optimization
A. Ben-Tal and A. Nemirovski · 2008
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An elementary proof of the triangle inequality for the wasserstein metric
P. Clement and W. Desch · 2008
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A. Ben-Tal, L. El Ghaoui, and A. Nemirovski · 2009
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Distributionally robust optimization under moment uncertainty with application to data-driven problems
E. Delage and Y. Ye · 2010
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J. Goh and M. Sim · 2010
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D. Bertsimas, D. B. Brown, and C. Caramanis · 2011
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Tractable stochastic analysis in high dimensions via robust optimization
C. Bandi and D. Bertsimas · 2012
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A distributional interpretation of robust optimization
H. Xu, C. Caramanis, and S. Mannor · 2012
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Distributionally robust joint chance constraints with second-order moment information
S. Zymler, D. Kuhn, and B. Rustem · 2013
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A clustering approach for scenario tree reduction: an application to a stochastic programming portfolio optimization problem
P. Beraldi and M. E. Bruni · 2014
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On the rate of convergence of empirical measures in ∞ \infty -transportation distance
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W. Wiesemann, D. Kuhn, and M. Sim · 2014
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Deriving robust counterparts of nonlinear uncertain inequalities
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Finite-sample guarantees for Wasserstein distributionally robust optimization: Breaking the curse of dimensionality
R. Gao · 2020
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Reducing conservatism in robust optimization
E. Roos and D. den Hertog · 2020
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Probabilistic guarantees in robust optimization
D. Bertsimas, den Hertog, D., and Pauphilet, J · 2021
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The optimal transport paradigm enables data compression in data-driven robust control
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Exploring the effect of clustering algorithms on sample average approximation
D. Jacobson, M. Hassan, and Z. S. Dong · 2021
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