Development Modified Conjugate Gradiente Algorithm
Article Sidebar
Main Article Content
Abstract
In this paper was to develop method V1-CG methods associated gradient amended to increase the speed of the convergence while retaining the charactenstic mass convergence as the derivation of this method was based on strict convex quadratic function has been develop this way to public function in whichthe supreme derivatives not equal zero.
The Desent property and global convergence for the proposed algorithm are established, oure numerical experiment on some test function and it showed us a clear improvement on the way V1-CG modified.
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Tikrit Journal of Pure Science is licensed under the Creative Commons Attribution 4.0 International License, which allows users to copy, create extracts, abstracts, and new works from the article, alter and revise the article, and make commercial use of the article (including reuse and/or resale of the article by commercial entities), provided the user gives appropriate credit (with a link to the formal publication through the relevant DOI), provides a link to the license, indicates if changes were made, and the licensor is not represented as endorsing the use made of the work. The authors hold the copyright for their published work on the Tikrit J. Pure Sci. website, while Tikrit J. Pure Sci. is responsible for appreciate citation of their work, which is released under CC-BY-4.0, enabling the unrestricted use, distribution, and reproduction of an article in any medium, provided that the original work is properly cited.
References
[1] Andrei, N. (2007c), Conjugate Gradient Algorithms for Molecular Formation under Pair Wise Potential Minimization, Center for Advanced Modeling and Optimization.
[2] Andrei, N. (2008), An Unconstrained Optimization Test Function collection, Advance Model. Optimization, Vol.(10),PP.147-161.
[3] Chong, E. K. P. and Zak, S. H., (2001), An Introduction to Optimization, Jone Wiley & Sons, Inc., Canada.
[4] Fletcher, R., (1987), Practical Methods of Opimization, A Wiley Inter Science Publication, John-Wiley & Sons, Inc., New York.
[5] Hestenes, M. R. and Stiefel, E., (1952), Methods of Conjugate Gradients for Solving Linear Systems, Journal of Research of the National Bureau of Standards, Vol. (5), No.(49), pp. 409-436.
[6] Hiroshi Y. and Naoki S. (2005), Anew nonlinear conjugate gradient method for unconstrained optimization, Journal of the Operations Research, Vol.(48), No.(4),PP. 284-296.
[7] Khalil, A. K., (2010), Developing of Gradient Algorithms for Solving Unconstrained Non-Linear Problem with Artificial Neural Networks, Doctoral thesis Faculty of Computing and Mathematics University of Mosal Sciences.
[8] Li, D. and M. Fukushima, (2001), Amodified BFGC Method and its global Convergence in nonconvex minimization J. comput. Appl. Math,129.
[9] Nocedal, J. and Wright, S. J., (2006), Numerical Optimization Springer Series in Operation Research, 2