On Solving The Convex Semi-Infinite Minimax Problems Via Superlinear Vu Incremental Bundle Technique With Partial Inexact Oracle

ASIA-PACIFIC JOURNAL OF OPERATIONAL RESEARCH(2021)

引用 0|浏览8
暂无评分
摘要
In this paper, we study convex semi-infinite programming involving minimax problems. One of the difficulties in solving these problems is that the maximum type functions are not differentiable. Due to the nonsmooth nature of the problem, we apply the special proximal bundle scheme on the basis of VU-decomposition theory to solve the nonsmooth convex semi-infinite minimax problems. The proposed scheme requires an evaluation within some accuracy for all the components of the objective function. Regarding the incremental method, we only need one component function value and one subgradient which are estimated to update the bundle information and produce the search direction. Under some mild assumptions, we present global convergence and local superlinear convergence of the proposed bundle method. Numerical results of several example problems are reported to show the effectiveness of the new scheme.
更多
查看译文
关键词
Convex optimization, nonsmooth optimization, VU-decomposition, semi-infinite minimax programming, bundle method, superlinear convergence
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要