Keywords : Nonlinear Optimization


Parallel Direct Search Methods

Bashir M. Khalaf; Mohammed W. Al-Neama

AL-Rafidain Journal of Computer Sciences and Mathematics, 2010, Volume 7, Issue 3, Pages 51-60
DOI: 10.33899/csmj.2010.163909

Mostly minimization or maximization of a function is very expensive. Since function evaluation of the objective function requires a considerable time. Hence, our objective in this work is the development of parallel algorithms for minimization of objective functions evaluation takes long computing time. The base of the developed parallel algorithms is the evaluation of the objective function at various points in same time (i.e. simultaneously).
We consider in this work the parallelization of the direct search methods, as these methods are non-sensitive for noise and globally convergent. We have developed two algorithms mainly they are dependent on the Hock & Jeff method in unconstrtrained optimization.
The developed parallel algorithm are suitable for running on MIMD machine which are consisting of several processors operating independently, each processor has it's own memory and communicating with each other through a suitable network.
 
 

Modified the CG-Algorithm for Unconstrained Non-Linear Optimization by Using Oren’s Update

Abbas Y. Al-Bayati; Abdulghafor M. Al-Rozbayani

AL-Rafidain Journal of Computer Sciences and Mathematics, 2005, Volume 2, Issue 2, Pages 11-19
DOI: 10.33899/csmj.2005.164078

In this paper we have modified a new extended generalized conjugate gradient steps with self-scaling variable metric updates for unconstrained optimization. The new proposed algorithm is based on the inexact line searches and it is examined by using different non-linear test functions with various dimensions.