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

Qun-yan Zhou & Wen-yu Sun

DOI:

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

Published online: 2006-12

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  • 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

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@Article{JCM-24-761, author = {}, 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 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 KW - Preconditioned gradient path KW - Trust region method KW - 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 & Wen-yu Sun. (1970). An Adaptive Nonmonotonic Trust Region Method with Curvilinear Searches. Journal of Computational Mathematics. 24 (6). 761-770. doi:
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