Stochastic programming is used to solve optimization problems in which the majority of the parameters are probabilistic. Probabilistic optimization can make efficient use of information, both in terms of selecting evaluation points and the message they convey. It can handle many sorts of noise and adapts to various aspects of optimization issues. Unlike deterministic optimization, probabilistic optimization techniques discover the best solution for data with randomness.
(1) Moment-based approach: The ambiguity set in moment based approach is the set of all probability distributions whose moments satisfy certain constraints [
,
].
(2) Dissimilarity-based approach: The ambiguity set in this case is the set of all probability distributions whose dissimilarity to a nominal distribution is lower than or equal to a given value. In this category, the choice of the dissimilarity function leads to couple of different variants which are as follows [
].
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References | Applications | LP | NLP | MILP | MISOCP | MIQP |
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