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:<math>\min f(x; s): x \in X(s),</math>
where S is known as the uncertainty set, which is usually a compact convex object as opposed to a small collection of scenarios (like parameter values).
where S is some set of scenarios (like parameter values). The robust optimization model (according to this more recent definition) is:▼
▲
:<math>\min_x {\max_{s \in S} f(x; s)}\, x \in X(t)\, \forall t \in S,</math>
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* [[Minimax regret]]
* [[Robust statistics]]
==External links==
* [http://www.robustopt.com ROME: Robust Optimization Made Easy]
== References==
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Ben-Tal A., El Ghaoui, L. and Nemirovski, A. (2006). <i>Mathematical Programming, Special issue on Robust Optimization,</i> Volume 107(1-2).
Bertsimas, D. and M. Sim. (2003). Robust Discrete Optimization and Network Flows. <i>Mathematical Programming,</i> 98, 49-71.
Bertsimas, D. and M. Sim. (2004). Price of Robustness. <i>Operations Research,</i> 52(1), 35-53.
Bertsimas, D. and M. Sim. (2006). Tractable Approximations to Robust Conic Optimization Problems Dimitris Bertsimas. <i> Mathematical Programming, <\i> 107(1), 5 – 36.
Chen, W. and M. Sim. (2009). Goal Driven Optimization. <i>Operations Research.</i> 57(2), 342-357.
Chen, X., M. Sim, P. Sun and J. Zhang. (2008). A Linear-Decision Based Approximation Approach to Stochastic Programming. <i> Operations Research <\i> 56(2), 344-357.
Chen, X., M. Sim and P. Sun (2007). A Robust Optimization Perspective on Stochastic Programming. <i> Operations Research, <\i> 55(6), 1058-1071.
Dembo, R. (1991). Scenario optimization, <i>Annals of Operations Research,</i> 30(1), 63-80.
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