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.
Y. AL-Bayati, A., & A. Hassan, B. (2011). A New Globally Convergent Self-Scaling Vm Algorithm for Convex and Nonconvex Optimization. Kirkuk Journal of Science, 6(1), 114-130. doi: 10.32894/kujss.2011.42544
MLA
Abbas Y. AL-Bayati; Basim A. Hassan. "A New Globally Convergent Self-Scaling Vm Algorithm for Convex and Nonconvex Optimization". Kirkuk Journal of Science, 6, 1, 2011, 114-130. doi: 10.32894/kujss.2011.42544
HARVARD
Y. AL-Bayati, A., A. Hassan, B. (2011). 'A New Globally Convergent Self-Scaling Vm Algorithm for Convex and Nonconvex Optimization', Kirkuk Journal of Science, 6(1), pp. 114-130. doi: 10.32894/kujss.2011.42544
VANCOUVER
Y. AL-Bayati, A., A. Hassan, B. A New Globally Convergent Self-Scaling Vm Algorithm for Convex and Nonconvex Optimization. Kirkuk Journal of Science, 2011; 6(1): 114-130. doi: 10.32894/kujss.2011.42544
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