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A New Globally Convergent Self-Scaling Vm Algorithm for Convex and Nonconvex Optimization | ||
| Kirkuk Journal of Science | ||
| Article 1, Volume 6, Issue 1, 2011, Pages 114-130 PDF (0 K) | ||
| DOI: 10.32894/kujss.2011.42544 | ||
| Authors | ||
| Abbas Y. AL-Bayati; Basim A. Hassan | ||
| Abstract | ||
| In unconstrained optimization, the original quasi-Newton condition where is the difference of the gradients at two successive iterations. Li and Fukushima proposed a modified BFGS methods based on a new Quasi –Newton equation where , where is a small positive constant .In this paper, we first propose the modified version of self-scaling VM-algorithm which was based on Li and Fukushima Quasi–Newton equation, i.e where . The corresponding AL-Bayati type algorithm is proved to possess the global convergence property in both convex and non-convex optimization problems. Experimental results indicate that the new proposed algorithm was more efficient than the standard BFGS- algorithm. | ||
| Keywords | ||
| A New Globally; Convergent; Self; Scaling; Vm Algorithm; Convex; Nonconvex Optimization | ||
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