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A new self-scaling VM-algorithm for non-convex optimization, part 1 | ||
| Journal of Education and Science | ||
| Article 13, Volume 25, Issue 1, 2012, Pages 116-125 PDF (0 K) | ||
| DOI: 10.33899/edusj.2012.59003 | ||
| Authors | ||
| Abbas Y. AL-Bayati; Maha S.Y. AL-Salih | ||
| Abstract | ||
| 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. | ||
| Keywords | ||
| self-scaling VM-algorithm; non-convex optimization; non-monotone line search | ||
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