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International Journal of Mathematics and Mathematical SciencesVolume 2009 2009, Article ID 435851, 17 pages

Research Article

Department of Mathematics and Computer Science, Faculty of Science, Kuwait University, P.O. 5969, Safat 13060, Kuwait City, Kuwait

Department of Mathematics, Faculty of Science, Alexandria University, Alexandria, Egypt

Received 15 December 2008; Accepted 2 February 2009

Academic Editor: Irena Lasiecka

Copyright © 2009 Mohammedi R. Abdel-Aziz and Mahmoud M. El-Alem. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The minimization of a quadratic function within an ellipsoidal trust region is an importantsubproblem for many nonlinear programming algorithms. When the number of variables is large,one of the most widely used strategies is to project the original problem into a small dimensionalsubspace. In this paper, we introduce an algorithm for solving nonlinear least squares problems. This algorithm is based on constructing a basis for the Krylov subspace in conjunction with amodel trust region technique to choose the step. The computational step on the small dimensionalsubspace lies inside the trust region. The Krylov subspace is terminated such that the terminationcondition allows the gradient to be decreased on it. A convergence theory of this algorithm ispresented. It is shown that this algorithm is globally convergent.

Autor: Mohammedi R. Abdel-Aziz and Mahmoud M. El-Alem



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