Global convergence of a descent PRP type conjugate gradient method for nonconvex optimization

Autor:	Hongrun Zhang, Qingjie Hu, Yu Chen
Rok vydání:	2022
Předmět:	Numerical Analysis Line search Applied Mathematics Computation MathematicsofComputing_NUMERICALANALYSIS Type (model theory) Convexity Nonlinear conjugate gradient method Computational Mathematics Conjugate gradient method Convergence (routing) Applied mathematics Mathematics Descent (mathematics)
Zdroj:	Applied Numerical Mathematics. 173:38-50
ISSN:	0168-9274
DOI:	10.1016/j.apnum.2021.11.001
Popis:	Nonlinear conjugate gradient methods are often used to solve large-scale unconstrained optimization problems owing to their lower memory requirement and less computation cost. In this paper, we propose a new Polak-Ribiere-Polyak type conjugate gradient method, which satisfies the sufficient descent condition independent of any line search. A remarkable property about this method is its strong global convergence with the standard Wolfe line search conditions as well as the standard Armijo line search strategy without convexity assumption of the objective function. The numerical results demonstrating the efficiency of the proposed method are reported.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::1dc9d33aec600da5796de7a80e120bca https://doi.org/10.1016/j.apnum.2021.11.001 Zobrazit plný text záznamu Full Text from ScienceDirect