A New Preconditioned Inexact Line-Search Technique for Unconstrained Optimization

In this paper, we study the global convergence properties of the new class of preconditioned conjugate gradient descent algorithm, when applied to convex objective non-linear unconstrained optimization functions. We assume that a new inexact line search rule which is similar to the Armijo line-searc...

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Veröffentlicht in:	AL-Rafidain journal of computer sciences and mathematics Jg. 9; H. 2; S. 25 - 39
Hauptverfasser:	Abbas Y. Al-Bayati, Ivan S. Latif
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Mosul University 04.12.2012
Schlagworte:	inexact line-search preconditioned cg self-scaling vm-update unconstrained optimization
ISSN:	1815-4816, 2311-7990
Online-Zugang:	Volltext
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Beschreibung
Zusammenfassung:	In this paper, we study the global convergence properties of the new class of preconditioned conjugate gradient descent algorithm, when applied to convex objective non-linear unconstrained optimization functions. We assume that a new inexact line search rule which is similar to the Armijo line-search rule is used. It's an estimation formula to choose a large step-size at each iteration and use the same formula to find the direction search. A new preconditioned conjugate gradient direction search is used to replace the conjugate gradient descent direction of ZIR-algorithm. Numerical results on twenty five well-know test functions with various dimensions show that the new inexact line-search and the new preconditioned conjugate gradient search directions are efficient for solving unconstrained nonlinear optimization problem in many situations.
ISSN:	1815-4816 2311-7990
DOI:	10.33899/csmj.2012.163698