2021•International Journal of Mathematical Modelling and Numerical OptimisationRequires access
A survey on update parameters of nonlinear conjugate gradient methods
Rupaj Kumar Nayak, Nirmaly Kumar Mohanty
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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.