Quasi-monotone Subgradient Methods for Nonsmooth Convex Minimization

被引：1
|
作者：
Yu. Nesterov
V. Shikhman
机构：
[1] Center for Operations Research and Econometrics (CORE),
来源：
Journal of Optimization Theory and Applications | 2015年 / 165卷
关键词：
Convex optimization; Nonsmooth optimzation; Subgradient methods; Rate of convergence; Primal-dual methods; 90C25; 90C47; 68Q25;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this paper, we develop new subgradient methods for solving nonsmooth convex optimization problems. These methods guarantee the best possible rate of convergence for the whole sequence of test points. Our methods are applicable as efficient real-time stabilization tools for potential systems with infinite horizon. Preliminary numerical experiments confirm a high efficiency of the new schemes.
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页码：917 / 940
页数：23
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