Approximation and regularization properties of augmented penalty functions in convex programming

被引：0

作者：

Skarin, V. D. ^{[1
]}

机构：

[1] Russian Acad Sci, Ural Branch, Inst Math & Mech, Moscow, Russia

来源：

TRUDY INSTITUTA MATEMATIKI I MEKHANIKI URO RAN | 2009年 / 15卷 / 04期

关键词：

convex programming; augmented penalty functions; improper problem; ill-posed problem; optimal correction; regularization method;

D O I：

暂无

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

The possibilities of using augmented penalty functions for the regularization and optimal correction of convex programming problems are investigated. Convergence conditions are formulated for the corresponding methods and an iteration algorithm for a linear optimization problem is proposed.

引用

页码：234 / 250

页数：17