Descent algorithm for nonsmooth stochastic multiobjective optimization

被引：15
|
作者：
Poirion, Fabrice [1 ]
Mercier, Quentin [1 ]
Desideri, Jean-Antoine [2 ]
机构：
[1] Off Natl Etud & Rech Aerosp, Palaiseau, France
[2] INRIA, Nice, France
来源：
COMPUTATIONAL OPTIMIZATION AND APPLICATIONS | 2017年 / 68卷 / 02期
关键词：
Multiobjective optimization; Stochastic; Nonsmooth; Almost sure convergence;
D O I：
10.1007/s10589-017-9921-x
中图分类号：
C93 [管理学]; O22 [运筹学];
学科分类号：
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要：
An algorithm for solving the expectation formulation of stochastic nonsmooth multiobjective optimization problems is proposed. The proposed method is an extension of the classical stochastic gradient algorithm to multiobjective optimization using the properties of a common descent vector defined in the deterministic context. The mean square and the almost sure convergence of the algorithm are proven. The algorithm efficiency is illustrated and assessed on an academic example.
引用
收藏
页码：317 / 331
页数：15
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