Open AccessA Globally Convergence Spectral Conjugate Gradient Method for Solving Unconstrained Optimization Problems
Author(s) -
Basim A. Hassan
Publication year - 2013
Publication title -
maǧallaẗ al-rāfidayn li-ʿulūm al-ḥāsibāt wa-al-riyāḍiyyāẗ/al-rafidain journal for computer sciences and mathematics
Language(s) - English
Resource type - Journals
eISSN - 2311-7990
pISSN - 1815-4816
DOI - 10.33899/csmj.2013.163543
Subject(s) - conjugate gradient method , nonlinear conjugate gradient method , convergence (economics) , derivation of the conjugate gradient method , line search , conjugate residual method , gradient method , gradient descent , mathematical optimization , descent (aeronautics) , mathematics , computer science , optimization problem , algorithm , artificial intelligence , artificial neural network , radius , aerospace engineering , engineering , economics , computer security , economic growth
In this paper, a modified spectral conjugate gradient method for solving unconstrained optimization problems is studied, which has sufficient descent direction and global convergence with an inexact line searches. The Fletcher-Reeves restarting criterion was employed to the standard and new versions and gave dramatic savings in the computational time. The Numerical results show that the proposed method is effective by comparing it with the FR-method.
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