Year: 2006
Journal of Computational Mathematics, Vol. 24 (2006), Iss. 6 : pp. 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.
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/2006-JCM-8789
Journal of Computational Mathematics, Vol. 24 (2006), Iss. 6 : pp. 761–770
Published online: 2006-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 10
Keywords: Unconstrained optimization Preconditioned gradient path Trust region method Curvilinear search.