Keywords : constrained

Self-Scaling Variable Metric in Constrained Optimization

Eman Hamed; Marwa W. Hamad

AL-Rafidain Journal of Computer Sciences and Mathematics, 2020, Volume 14, Issue 1, Pages 21-30
DOI: 10.33899/csmj.2020.164673

In this paper, we investigated of a new self-scaling by use quasi-Newton method and conjugate gradient method. The new algorithm satisfies a quasi-newton condition and mutually conjugate, and practically proved its efficiency when compared with the well-known algorithms in this domain, by depending on efficiency measure, number of function, number of iteration, and number of constrained, NOF, NOI and NOC.

New low storage VM-algorithm for constrained optimization

Abbas Y. Al-Bayati; Hamsa Th. Chilmerane

AL-Rafidain Journal of Computer Sciences and Mathematics, 2009, Volume 6, Issue 3, Pages 11-19
DOI: 10.33899/csmj.2009.163817

In this paper a new low-storage VM-algorithm for constrained optimization is investigated both theoretically and experimentally. The new algorithm is based on both the well-known Fletcher's low storage algorithm which generates columns Z spanned on the gradient vectors g1, g2, ... gn  and the idea of both Buckley and LeNir of combined variable storage-conjugate gradient method. The well-known SUMT algorithm is adapted to implement the new idea. The new algorithm is very robust compared with the standard low-storage Fletcher algorithm and the standard SUMT algorithm which was designed for solving constrained problems, of the    numerical results of application very promising.