logo
CanyamAI summaries for academic research
language
MobileMobile
  • Phone FeatureWeChat Mini Program
EnglishEnglish
noticeNotice
BlogBlog
delete
arrow
Return

Nonmonotone trust region method for solving optimization problems

delete2004-08-01
delete99
PRE
AI
W
Wenyu Sun *
DOI：10.1016/j.amc.2003.07.008delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
In this paper a trust region (TR) method with nonmonotone technique for optimization is proposed. We construct a new ratio of actual descent and predicted descent which is a simple and natural generalization of the modified Armijo line search rule. The paper exposes the relationship between the trust region method and line search approach. Since this method possesses the robust properties of trust region subproblem, it is globally convergent although we employ the nonmonotone sequence of function values instead of the monotone sequence. In addition, the proof of convergence is obviously simpler than one of Newton-type method with nonmonotone line search. Finally, applications of the nonmonotone TR algorithm to some optimization problems are discussed. (C) 2003 Elsevier Inc. All rights reserved.
Keywords:
trust region method
nonlinear programming
quasi-Newton method
nonmonotone optimization method

Journal

Applied Mathematics and Computation cover
Applied Mathematics and Computation
IF:
3.4
Papers:
2.3W
Citations:
3.3W

Organization

No organization information available