- Title
- Globally convergent algorithms for solving unconstrained optimization problems
- Creator
- Taheri, Sona; Mammadov, Musa; Seifollahi, Sattar
- Date
- 2013
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/60698
- Identifier
- vital:5115
- Identifier
-
https://doi.org/10.1080/02331934.2012.745529
- Identifier
- ISSN:0233-1934
- Abstract
- 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.
- Relation
- Optimization Vol. , no. (2013), p. 1-15
- Rights
- Copyright 2012 Taylor & Francis
- Rights
- Open Access
- Rights
- This metadata is freely available under a CCO license
- Subject
- 0102 Applied Mathematics; 0103 Numerical and Computational Mathematics; Unconstrained optimization; Gradient method; Newton's method; Quasi-Newton method; Global convergence; Superlinear convergence
- Full Text
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