A subgradient method with non-monotone line search

被引：0
|
作者：
Ferreira, O. P. [1 ]
Grapiglia, G. N. [2 ]
Santos, E. M. [3 ]
Souza, J. C. O. [4 ,5 ]
机构：
[1] Univ Fed Goias, Inst Matemat & Estat, BR-74001970 Goiania, Go, Brazil
[2] Catholic Univ Louvain, ICTEAM INMA, Ave Georges Lemaitre,4-6-L4 05-01, B-1348 Louvain La Neuve, Belgium
[3] Inst Fed Educ Ciencia & Tecnol Maranhao, BR-65930000 Acailandia, MA, Brazil
[4] Aix Marseille Univ, AMSE, CNRS, Marseille, France
[5] Univ Fed Piaui, Dept Math, Teresina, PI, Brazil
来源：
COMPUTATIONAL OPTIMIZATION AND APPLICATIONS | 2023年 / 84卷 / 02期
关键词：
Subgradient method; Non-monotone line search; Convex function; CONVERGENCE;
D O I：
10.1007/s10589-022-00438-z
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：
In this paper we present a subgradient method with non-monotone line search for the minimization of convex functions with simple convex constraints. Different from the standard subgradient method with prefixed step sizes, the new method selects the step sizes in an adaptive way. Under mild conditions asymptotic convergence results and iteration-complexity bounds are obtained. Preliminary numerical results illustrate the relative efficiency of the proposed method.
引用
收藏
页码：397 / 420
页数：24
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