Solving Lagrangian variational inequalities with applications to stochastic programming
成果类型：
Article
署名作者：
Rockafellar, R. Tyrrell; Sun, Jie
署名单位：
University of Washington; University of Washington Seattle; Curtin University
刊物名称：
MATHEMATICAL PROGRAMMING
ISSN/ISSBN：
0025-5610
DOI：
10.1007/s10107-019-01458-0
发表日期：
2020
页码：
435-451
关键词：
摘要：
Lagrangian variational inequalities feature both primal and dual elements in expressing first-order conditions for optimality in a wide variety of settings where multipliers in a very general sense need to be brought in. Their stochastic version relates to problems of stochastic programming and covers not only classical formats with inequality constraints but also composite models with nonsmooth objectives. The progressive hedging algorithm, as a means of solving stochastic programming problems, has however focused so far only on optimality conditions that correspond to variational inequalities in primal variables alone. Here that limitation is removed by appealing to a recent extension of progressive hedging to multistage stochastic variational inequalities in general.