A new self-scaling VM-algorithm for non-convex optimization, part 1
| Autor: | Abbas Y. AL-Bayati, Maha S.Y. AL-Salih |
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| Jazyk: | Arabic<br />English |
| Rok vydání: | 2012 |
| Předmět: | |
| Zdroj: | مجلة التربية والعلم, Vol 25, Iss 1, Pp 116-125 (2012) |
| Druh dokumentu: | article |
| ISSN: | 1812-125X 2664-2530 |
| DOI: | 10.33899/edusj.2012.59003 |
| Popis: | Abstract The self-scaling VM-algorithms solves an unconstrained non-linear optimization problems by scaling the Hessian approximation matrix before it is updated at each iteration to avoid the possible large eigen-values in the Hessian approximation matrices of the objective function f(x).It has been proved that these algorithms have a global and super-linear convergences when f(x)is non- convex. In this paper we are going to propose a new self-scaling VM-algorithm with a new non-monotone line search procedure with a detailed study of the global and super-linear convergence property for the new proposed algorithm in non-convex optimization. Keywords: VM-methods, non-monotone line searches, self-scaling AL-Bayati VM- method, global converge, super-linear convergence. |
| Databáze: | Directory of Open Access Journals |
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