A GLOBALLY CONVERGENT NEWTON METHOD FOR CONVEX SC1 MINIMIZATION PROBLEMS

被引：60

作者：

PANG, JS ^{[1
]}

QI, L ^{[1
]}

机构：

[1] UNIV NEW S WALES,DEPT MATH APPL,SYDNEY,NSW,AUSTRALIA

来源：

JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS | 1995年 / 85卷 / 03期

关键词：

NONSMOOTH OPTIMIZATION; NEWTON METHOD;

D O I：

10.1007/BF02193060

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：

This paper presents a globally convergent and locally superlinearly convergent method for solving a convex minimization problem whose objective function has a semismooth but nondifferentiable gradient. Applications to nonlinear minimax problems, stochastic programs with recourse, and their extensions are discussed.

引用

页码：633 / 648

页数：16

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