A globally and superlinearly convergent algorithm for nonsmooth convex minimization

被引：72

作者：

Fukushima, M ^{[1
]}

Qi, LQ ^{[1
]}

机构：

[1] UNIV NEW S WALES,SCH MATH,SYDNEY,NSW 2052,AUSTRALIA

来源：

SIAM JOURNAL ON OPTIMIZATION | 1996年 / 6卷 / 04期

关键词：

nonsmooth convex optimization; Moreau-Yosida regularization; global convergence; superlinear convergence; semismoothness;

D O I：

10.1137/S1052623494278839

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

It is well known that a possibly nondifferentiable convex minimization problem can be transformed into a differentiable convex minimization problem by way of the Moreau-Yosida regularization. This paper presents a globally convergent algorithm that is designed to solve the latter problem. Under additional semismoothness and regularity assumptions, the proposed algorithm is shown to have a Q-superlinear rate of convergence.

引用

页码：1106 / 1120

页数：15

共 24 条

[1]

AUSLENDER A, 1987, MATH PROGRAM STUD, V30, P102, DOI 10.1007/BFb0121157

[2] CONVERGENCE OF SOME ALGORITHMS FOR CONVEX MINIMIZATION [J].