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Globally convergent algorithms for solving unconstrained optimization problems

journal contribution
posted on 2015-01-01, 00:00 authored by S Taheri, Musa MammadovMusa Mammadov, S Seifollahi
New algorithms for solving unconstrained optimization problems are presented based on the idea of combining two types of descent directions: the direction of anti-gradient and either the Newton or quasi-Newton directions. The use of latter directions allows one to improve the convergence rate. Global and superlinear convergence properties of these algorithms are established. Numerical experiments using some unconstrained test problems are reported. Also, the proposed algorithms are compared with some existing similar methods using results of experiments. This comparison demonstrates the efficiency of the proposed combined methods.

History

Journal

Optimization

Volume

64

Issue

2

Pagination

249 - 263

Publisher

Taylor & Francis

Location

Abingdon, Eng.

ISSN

0233-1934

eISSN

1029-4945

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal

Copyright notice

2012, Taylor & Francis