Robust optimization: Difference between revisions

Revision as of 09:47, 7 November 2017

Revision as of 15:29, 10 December 2017

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:<math>\max_{x\in X,v\in \mathbb{R}} \ \{v: v\le f(x,u), g(x,u)\le b, \forall u\in U(x)\}</math>

===ProbabilisticProbabilistically robust optimization models===

These models quantify the uncertainty in the "true" value of the parameter of interest by probability distribution functions. They have been traditionally classified as [[stochastic programming]] and [[stochastic optimization]] models. Recently, probabilistically robust optimization has gained popularity by the introduction of rigorous theories such as [[scenario optimization]] able to quantify the robustness level of solutions obtained by randomized solutions. These methods are also relevant to data-driven optimization methods.

===Robust counterpart===