Year: 2003
Journal of Computational Mathematics, Vol. 21 (2003), Iss. 2 : pp. 221–228
Abstract
Trust region (TR) algorithms are a class of recently developed algorithms for nonlinear optimization. A new family of TR algorithms for unconstrained optimization, which is the extension of the usual TR method, is presented in this paper. When the objective function is bounded below and continuously differentiable, and the norm of the Hesse approximations increases at most linearly with the iteration number, we prove the global convergence of the algorithms. Limited numerical results are reported, which indicate that our new TR algorithm is competitive.
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/2003-JCM-10276
Journal of Computational Mathematics, Vol. 21 (2003), Iss. 2 : pp. 221–228
Published online: 2003-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 8
Keywords: trust region method global convergence quasi-Newton method unconstrained optimization nonlinear programming.