• A NEW COMBINED CONJUGATE GRADIENT-MEMORYLESS BFGS ALGORITHM FOR SOLVING UNCONSTRIANED OPTIMIZATION PROBLEMS
Abstract
Conjugate Gradient (CG) methods, which we have investigated in this study, were widely used in optimization, especially for large scale optimization problems, because it does not need the storage of any matrix. In this paper, we have constructed a new combined CG-memoryless BFGS algorithm. Our new proposed algorithm which is suitable for solving large scale optimization problem has been constructed by interleaving the modified CG-method due to Liu and Li (2012) with the standard memoryless BFGS update. Numerical results, showed that the new algorithm has been proved to be an effective algorithm in solving large scale optimization problems and gave us a very good numerical results and this algorithm always produce descent search directions and were shown to be globally convergent under some assumptions.
Keywords
Unconstrained Optimization, Conjugate Gradients, Memoryless BFGS Updates, Hybrid Search Directions, Descent Directions, Globally Convergent Methods.
Full Text:
PDF
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
| © 2010-2022 International Journal of Mathematical Archive (IJMA) Copyright Agreement & Authorship Responsibility |