Global convergence of a modified RMIL+ nonlinear conjugate gradient method with strong wolfe

Abdelrhaman Abashar, Osman Omer Osman Yousif, Awad Abdelrahman Abdalla Mohammed, Mohammed A. Saleh


Nonlinear conjugate gradient (CG) methods are extensively used as an important technique for addressing large-scale unconstrained optimization problems which are arise in many aspects of science, engineering, and economics. That is due to their simplicity, convergence properties, and low memory requirements. To generate a new approximation solution in each iteration, the CG methods usually implement under the strong Wolfe line search. For good performance, many studies have been carried out to modify well-known CG methods. In this paper, we did some modifications on one of CG method called RMIL+ in order to obtain a new CG method possesses the sufficient descent property and the global convergence under strong Wolfe line search. The numerical results demonstrate that the suggested method outperforms other CG methods.


Conjugate gradient method; Global convergence; Strong wolfe line search; Sufficient descent property; Unconstrained optimization

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