Authors: Nirmaly Kumar Mohanty; Rupaj Kumar Nayak
Addresses: Department of Mathematics, International Institute of Information Technology, Bhubaneswar, Odisha, 751029, India ' Department of Mathematics, International Institute of Information Technology, Bhubaneswar, Odisha, 751029, India
Abstract: Nonlinear conjugate gradient methods are a class of techniques that are used for solving nonlinear optimisation problems frequently arising in many engineering applications such as machine learning, computer vision, least-square optimisations, to name a few. With so many surveys on the nonlinear conjugate gradient method (NLCG) available around, this paper addresses the current updates and sheds a new light on the evolution of hybrid conjugate gradient parameters with their global convergence properties.
Keywords: unconstrained optimisation; conjugate gradient method; nonlinear conjugate gradient method; hybrid conjugate parameter.
International Journal of Mathematical Modelling and Numerical Optimisation, 2021 Vol.11 No.3, pp.292 - 325
Received: 21 Apr 2020
Accepted: 26 Sep 2020
Published online: 29 Jul 2021 *