Refaad for Studies, Research and Developement
A new self-scaling variable metric (DFP) method for unconstrained optimization problems
2519-9269 (Print)
2519-9277 (Online)
Volume 9
Issue 1
2020
Sep
0
A new self-scaling variable metric (DFP) method for unconstrained optimization problems
46
52
English
Salah Gazi ShareefDepartment of Mathematics, Faculty of Science, University of Zakho, Zakho, Kurdistan Region, Iraqsalah.shareef@uoz.edu.krd
Alaa Luqman IbrahimDepartment of Mathematics, Faculty of Science, University of Zakho, Zakho, Kurdistan Region, Iraq.alaa.ibrahim@uoz.edu.krd
Zinah Talal YaseenDepartment of Mathematics, College of Computer Science and Mathematics, University of Mosul, Mosul, Iraq.Zena-talal@uomosul.edu.iq
https://doi.org/10.31559/glm2020.9.1.6
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).
Unconstrained optimization; self-scaling; Variable metric; Hessian approximation; DFP update
http://www.refaad.com/views/GLM/916.html
http://www.refaad.com/Files/GLM/GLM-9-1-6.pdf