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Probabilistic optimization via approximate p-efficient points and bundle methods

W. van vAckooij(wim.van-ackooij***at***edf.fr )
V. Berge(violette.berge***at***gmail.com )
W. de Oliveira(welington***at***ime.uerj.br)
C. Sagastizábal(sagastiz***at***impa.br)

Abstract: For problems when decisions are taken prior to observing the realization of underlying random events, probabilistic constraints are an important modelling 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 probabilistically constrained problems confirm the interest of the approach.


Category 1: Convex and Nonsmooth Optimization

Category 2: Stochastic Programming

Citation: Preprint

Download: [PDF]

Entry Submitted: 05/27/2015
Entry Accepted: 05/27/2015
Entry Last Modified: 05/27/2015

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