A new self-scaling variable metric (DFP) method for unconstrained optimization problems

Volume 9, Issue 1, Article 6 - 2020

Authors: Salah Gazi Shareef;Alaa Luqman Ibrahim;Zinah Talal Yaseen

Copyright © 2020 . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Abstract

In this study, a new self-scaling variable metric (VM)-updating method for solving nonlinear unconstrained optimization problems is presented. The general strategy of (New VM-updating) is to propose a new quasi-newton condition used for update the usual DFP Hessian to a number of times in a way to be specified in some iteration with PCG method to improve the performance of the Hessian approximation. We show that it produces a positive definite matrix. Experimental results indicate that the new suggested method was more efficient than the standard DFP method, with respect to the number of functions evaluations (NOF) and number of iterations (NOI).

How To Cite This Article

S. G. Shareef, A. L. Ibrahim and Z. T. Yaseen, “A new self-scaling variable metric (DFP) method for unconstrained optimization problems”, General Letters in Mathematics, 9(1) (2020), 46-52. https://doi.org/10.31559/glm2020.9.1.6