A New Preconditioned Inexact Line-Search Technique for Unconstrained Optimization | ||
| AL-Rafidain Journal of Computer Sciences and Mathematics | ||
| Article 2, Volume 9, Issue 2, December 2012, Pages 25-39 PDF (690.37 K) | ||
| Document Type: Research Paper | ||
| DOI: 10.33899/csmj.2012.163698 | ||
| Authors | ||
| Abbas Y. Al-Bayati1; Ivan S. Latif2 | ||
| 1College of Computer Sciences and Mathematics University of Mosul, Mosul, Iraq | ||
| 2College of Scientific Education University of Salahaddin | ||
| Abstract | ||
| 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. | ||
| Keywords | ||
| Preconditioned CG; unconstrained Optimization; Self-Scaling VM-update; inexact Line-Search | ||
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