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Authors: KHELLADI SAMIA, BENTERKI DJAMEL.
DOI: 10.24193/subbmath.2022.4.14
Published Online: 2022-12-02
Published Print: 2022-12-30
pp. 855-869
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In this paper, we present some hybrid methods for solving unconstrained optimization problems. These methods are defined using proper combinations of the search directions and included parameters in conjugate gradient and quasi-Newton method of Broyden–Fletcher–Goldfarb–Shanno (CG-BFGS). Their global convergence under the Wolfe line search is analyzed for general objective functions. Numerical experiments show the superiority of the modified hybrid (CG-BFGS) method with respect to some existing methods.
Mathematics Subject Classification (2010): 65K05, 90C26, 90C30.
Keywords: Unconstrained optimization, global convergence, conjugate gradient methods, quasi-Newton methods, Wolfe line search.
