Probabilistic optimization via approximate p-efficient points and bundle methods

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• 作者：W. van Ackooij^a ; ^{wim.van-ackooij@edf.fr" class="auth_mail" title="E-mail the corresponding author} ; V. Berge^b ; W. de Oliveira^c ; ^d ; ^{welington@ime.uerj.br" class="auth_mail" title="E-mail the corresponding author} ; C. Sagastizá ; bal^e ; ^{sagastiz@impa.br" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Probabilistic constraints ; Stochastic programming ; Duality ; Bundle methods ; p-efficient points
• 刊名：Computers and Operations Research
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：77
• 期：Complete
• 页码：177-193
• 全文大小：1282 K

文摘

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.