@article {10.3844/jcssp.2010.511.518, article_type = {journal}, title = {New Scaled Sufficient Descent Conjugate Gradient Algorithm for Solving Unconstraint Optimization Problems}, author = {AL-Bayati, Abbas Y. and Muhammad, Rafiq S.}, volume = {6}, number = {5}, year = {2010}, month = {May}, pages = {511-518}, doi = {10.3844/jcssp.2010.511.518}, url = {https://thescipub.com/abstract/jcssp.2010.511.518}, abstract = {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.}, journal = {Journal of Computer Science}, publisher = {Science Publications} }