A Modified Globally Convergent Self-Scaling BFGS Algorithm for Unconstrained Optimization | Zendy

Abbas Al-Bayati | Zendy; Basim A. Hassan | Zendy; Sawsan S. Ismael | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

A Modified Globally Convergent Self-Scaling BFGS Algorithm for Unconstrained Optimization

Author(s) -

Abbas Al-Bayati,

Basim A. Hassan,

Sawsan S. Ismael

Publication year - 2012

Publication title -

mağallaẗ al-tarbiyaẗ wa-al-ʻilm

Language(s) - English

Resource type - Journals

eISSN - 2664-2530

pISSN - 1812-125X

DOI - 10.33899/edusj.2012.59195

Subject(s) - broyden–fletcher–goldfarb–shanno algorithm , hessian matrix , quasi newton method , mathematical optimization , scaling , algorithm , line search , inverse , matrix (chemical analysis) , computer science , mathematics , newton's method , computer network , physics , geometry , asynchronous communication , computer security , materials science , nonlinear system , quantum mechanics , radius , composite material

In this paper, a modified globally convergent self-scaling BFGS algorithm for solving convex unconstrained optimization problems was investigated in which it employs exact line search strategy and the inverse Hessian matrix approximations were positive definite. Experimental results indicate that the new proposed algorithm was more efficient than the standard BFGSalgorithm.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research