New Scaled Sufficient Descent Conjugate Gradient Algorithm for Solving Unconstraint Optimization Problems | Science Publications

Journal of Computer Science

Rafiq S. Muhammad¹  Abbas v Y.A. Bayati¹ 

关键词: Unconstrained optimization;    hybrid conjugate gradient;    scaled conjugate gradient;    sufficient descent condition;    conjugacy condition;   

DOI  :  10.3844/jcssp.2010.511.518

学科分类：计算机科学（综合）

来源: Science Publications

【 摘 要 】

Problem statement: The scaled hybrid Conjugate Gradient (CG) algorithm which usually used for solving non-linear functions was presented and was compared with two standard well-Known NAG routines, yielding a new fast comparable algorithm. Approach: We proposed, a new hybrid technique based on the combination of two well-known scaled (CG) formulas for the quadratic model in unconstrained optimization using exact line searches. A global convergence result for the new technique was proved, when the Wolfe line search conditions were used. Results: Computational results, for a set consisting of 1915 combinations of (unconstrained optimization test problems/dimensions) were implemented in this research making a comparison between the new proposed algorithm and the other two similar algorithms in this field. Conclusion: Our numerical results showed that this new scaled hybrid CG-algorithm substantially outperforms Andrei-sufficient descent condition (CGSD) algorithm and the well-known Andrei standard sufficient descent condition from (ACGA) algorithm.

【 授权许可】

Unknown