Diagonal Bundle Method for Nonsmooth Sparse Optimization

被引：7
作者：
Karmitsa, Napsu [1 ]
机构：
[1] Univ Turku, Dept Math & Stat, Turku 20014, Finland
来源：
JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS | 2015年 / 166卷 / 03期
关键词：
Nondifferentiable optimization; Sparse problems; Bundle methods; Diagonal variable metric methods; VARIABLE-METRIC METHOD; QUASI-NEWTON MATRICES; NONCONVEX OPTIMIZATION; CONVEX-OPTIMIZATION; PROXIMITY CONTROL; ALGORITHM; MINIMIZATION; CLASSIFICATION; FORMULATIONS;
D O I：
10.1007/s10957-014-0666-8
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 propose an efficient diagonal bundle method for sparse nonsmooth, possibly nonconvex optimization. The convergence of the proposed method is proved for locally Lipschitz continuous functions, which are not necessary differentiable or convex. The numerical experiments have been made using problems with up to million variables. The results to be presented confirm the usability of the diagonal bundle method especially for extremely large-scale problems.
引用
收藏
页码：889 / 905
页数：17
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