A Non-monotone Conjugate Subgradient Type Method for Minimization of Convex Functions

被引：1

作者：

Konnov, Igor ^{[1
]}

机构：

[1] Kazan Fed Univ, Dept Syst Anal & Informat Technol, Ul Kremlevskaya 18, Kazan 420008, Russia

来源：

JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS | 2020年 / 184卷 / 02期

基金：

俄罗斯基础研究基金会;

关键词：

Convex minimization problems; Non-differentiable functions; Conjugate subgradient method; Simple step-size choice; Convergence properties;

D O I：

10.1007/s10957-019-01589-6

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：

We suggest a conjugate subgradient type method without any line search for minimization of convex non-differentiable functions. Unlike the custom methods of this class, it does not require monotone decrease in the goal function and reduces the implementation cost of each iteration essentially. At the same time, its step-size procedure takes into account behavior of the method along the iteration points. The preliminary results of computational experiments confirm the efficiency of the proposed modification.

引用

页码：534 / 546

页数：13

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