Open AccessNew Scaled Proposed formulas For Conjugate Gradient Methods in Unconstrained Optimization
Author(s) -
Abbas Al-Bayati,
Marwan S. Jameel
Publication year - 2014
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.2014.163748
Subject(s) - conjugate gradient method , nonlinear conjugate gradient method , line search , convergence (economics) , algorithm , mathematical optimization , gradient method , nonlinear system , mathematics , derivation of the conjugate gradient method , line (geometry) , computer science , gradient descent , artificial intelligence , physics , geometry , computer security , quantum mechanics , artificial neural network , economics , radius , economic growth
University of Telafer, Iraq University of Mosul/Iraq profabbasalbayati@yahoo.com Received on: 10/6/2013 Accepted on: 16/9/2013 ABSTRACT In this paper, three efficient Scaled Nonlinear Conjugate Gradient (CG) methods for solving unconstrained optimization problems are proposed. These algorithms are implemented with inexact line searches (ILS). Powell restarting criterion is applied to all these algorithms and gives dramatic saving in the computational efficiency. The global convergence results of these algorithms are established under the Strong Wolfe line search condition. Numerical results show that our proposed CG-algorithms are efficient and stationary by comparing with standard Fletcher-Reeves (FR); PolakRibiere (PR) CG-algorithms, using 35-nonlinear test functions.
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