full
search.png

Search

close.png

Cancel

arrow
Volume 24, Issue 6
An Adaptive Nonmonotonic Trust Region Method with Curvilinear Searches

Qun-yan Zhou & Wen-yu Sun

J. Comp. Math., 24 (2006), pp. 761-770.

Published online: 2006-12

Preview Full PDF 2514 24171

Cited by

google scholar semantic scholar
Export citation
  • Abstract

In this paper, an algorithm for unconstrained optimization that employs both trust region techniques and curvilinear searches is proposed. At every iteration, we solve the trust region subproblem whose radius is generated adaptively only once. Nonmonotonic backtracking curvilinear searches are performed when the solution of the subproblem is unacceptable. The global convergence and fast local convergence rate of the proposed algorithms are established under some reasonable conditions. The results of numerical experiments are reported to show the effectiveness of the proposed algorithms.

  • Keywords

Unconstrained optimization, Preconditioned gradient path, Trust region method, Curvilinear search.

  • AMS Subject Headings

  • Copyright

COPYRIGHT: © Global Science Press

  • Email address
  • BibTex
  • RIS
  • TXT
@Article{JCM-24-761, author = {Qun-yan Zhou and Wen-yu Sun}, title = {An Adaptive Nonmonotonic Trust Region Method with Curvilinear Searches}, journal = {Journal of Computational Mathematics}, year = {2006}, volume = {24}, number = {6}, pages = {761--770}, abstract = {

In this paper, an algorithm for unconstrained optimization that employs both trust region techniques and curvilinear searches is proposed. At every iteration, we solve the trust region subproblem whose radius is generated adaptively only once. Nonmonotonic backtracking curvilinear searches are performed when the solution of the subproblem is unacceptable. The global convergence and fast local convergence rate of the proposed algorithms are established under some reasonable conditions. The results of numerical experiments are reported to show the effectiveness of the proposed algorithms.

}, issn = {1991-7139}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/jcm/8789.html} }
TY - JOUR T1 - An Adaptive Nonmonotonic Trust Region Method with Curvilinear Searches AU - Qun-yan Zhou & Wen-yu Sun JO - Journal of Computational Mathematics VL - 6 SP - 761 EP - 770 PY - 2006 DA - 2006/12 SN - 24 DO - http://doi.org/ UR - https://global-sci.org/intro/article_detail/jcm/8789.html KW - Unconstrained optimization, Preconditioned gradient path, Trust region method, Curvilinear search. AB -

In this paper, an algorithm for unconstrained optimization that employs both trust region techniques and curvilinear searches is proposed. At every iteration, we solve the trust region subproblem whose radius is generated adaptively only once. Nonmonotonic backtracking curvilinear searches are performed when the solution of the subproblem is unacceptable. The global convergence and fast local convergence rate of the proposed algorithms are established under some reasonable conditions. The results of numerical experiments are reported to show the effectiveness of the proposed algorithms.

Qun-yan Zhou and Wen-yu Sun. (2006). An Adaptive Nonmonotonic Trust Region Method with Curvilinear Searches. Journal of Computational Mathematics. 24 (6). 761-770. doi:
Copy to clipboard
BibteX RIS TXT
The citation has been copied to your clipboard