An Inexact Accelerated Proximal Gradient Method and a Dual Newton-CG Method for the Maximal Entropy Problem

被引：7

作者：

Wang, Chengjing ^{[1
,2
]}

Xu, Aimin ^{[3
]}

机构：

[1] Southwest Jiaotong Univ, Sch Math, Chengdu 610031, Peoples R China

[2] Southwest Jiaotong Univ, Inst Appl Math, Chengdu 610031, Peoples R China

[3] Zhejiang Wanli Univ, Inst Math, Ningbo 315100, Zhejiang, Peoples R China

来源：

JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS | 2013年 / 157卷 / 02期

基金：

中国国家自然科学基金;

关键词：

Maximal entropy problem; Inexact accelerated proximal gradient method; Newton-CG method; Semi-smoothness; IMAGE-RECONSTRUCTION; ALGORITHM; EQUATIONS;

D O I：

10.1007/s10957-012-0150-2

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 describes an algorithm to solve large-scale maximal entropy problems. The algorithm employs an inexact accelerated proximal gradient method to generate an initial iteration point which is important; then it applies the Newton-CG method to the dual problem. Numerical experiments illustrate that the algorithm can supply an acceptable and even highly accurate solution, while algorithms without generating a good initial point may probably fail.

引用

页码：436 / 450

页数：15

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