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Probabilistic optimization via approximate p-efficient points and bundle methods
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DOI：10.1016/j.cor.2016.08.002.png)
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Abstract
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For problems when decisions are taken prior to observing the realization of underlying random events, probabilistic constraints are an important modeling tool if reliability is a concern. A key concept to numerically dealing with probabilistic constraints is that of p-efficient points. By adopting a dual point of view, we develop a solution framework that includes and extends various existing formulations. The unifying approach is built on the basis of a recent generation of bundle methods called with on-demand accuracy, characterized by its versatility and flexibility. Numerical results for several difficult problems confirm the interest of the approach. (C) 2016 Elsevier Ltd. All rights reserved.
Keywords:
Probabilistic constraints
Stochastic programming
Duality
Bundle methods
p-efficient points
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