摘要
利用变分不等式求解优化问题是一种有效且便利的方法.而随机变分不等式和增广Lagrange变分不等式的概念最近以一种新的形式被阐述,在凸性条件下求解这类问题通常用的方法是逐步对冲算法和分解算法.对于随机优化问题,提出随机增广Lagrange变分不等式.在凸凹鞍点问题中,由随机分解算法求解这类问题.
Variational inequality was an effective and convenient method for solving optimization problems. The concept of a stochastic variational inequality and augmented Lagrange variational inequality has recently been elaborated in a new form. The usual methods for solving these problems were progressive hedging and decomposition algorithms. For stochastic optimization problems stochastic augmented variational inequalities were proposed. In convex and concave saddle point problems stochastic decomposition algorithm was used to solve these problems.
作者
王炜
刘玉兵
毕天骄
WANG Wei;LIU Yu-bing;BI Tian-jiao(School of Mathematics,Liaoning Normal University,Dalian 116029,China)
出处
《吉林师范大学学报（自然科学版）》
2019年第2期53-57,共5页
Journal of Jilin Normal University:Natural Science Edition
基金
国家自然科学基金项目(11671184)
关键词
随机增广Lagrange变分不等式
凸凹鞍点
随机分解算法
stochastic augmented Lagrange variational inequality
convex and concave saddle point
stochastic decomposition algorithm
