Mouiyad Bani Yousef
Convex combinationOptimization problemNonlinear conjugate gradient methodApplied mathematicsNumerical testsUnconstrained optimizationMathematicsConvergence (routing)Conjugate gradient methodLine search
Publications 3
The nonlinear conjugate gradient (CG) method is a widely used approach for solving large-scale optimization problems in many fields, such as physics, engineering, economics, and design. The efficiency of this method is mainly attributable to its global convergence properties and low memory requirement. In this paper, a new conjugate gradient coefficient is proposed based on the Aini-Rivaie-Mustafa (ARM) method. Furthermore, the proposed method is proved globally convergent under exact line searc...
Conjugate gradient (CG) methods are an important class of methods for unconstrained optimization, especially for large-scale problems. Recently, they have been much studied. In this paper, we propose a new hybrid conjugate gradient method for solving unconstrained optimization problems, which is a convex combination of an earlier version of Polak- Ribiere and Polyak (PRP) and a recent modification of Mouiyad Bani Yousef (MMR) method. The proposed method is proved globally convergent under exact ...
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