General Letters in Mathematics

Volume 9 - Issue 1 (6) | PP: 46 - 52 Language : English
DOI : https://doi.org/10.31559/glm2020.9.1.6
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A new self-scaling variable metric (DFP) method for unconstrained optimization problems

Salah Gazi Shareef ,
Alaa Luqman Ibrahim ,
Zinah Talal Yaseen
Received Date Revised Date Accepted Date Publication Date
21/12/2019 22/4/2020 18/6/2020 13/10/2020
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
Shareef , S. G.Ibrahim , A. L. & Yaseen , Z. T. (2020). A new self-scaling variable metric (DFP) method for unconstrained optimization problems . General Letters in Mathematics, 9 (1), 46-52, 10.31559/glm2020.9.1.6

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