Conjugate gradient (CG) algorithms are widely applied to machine learning problems owing
to their low calculation cost compared with second-order methods and better convergence …

This book is on conjugate gradient methods for unconstrained optimization. The concept of
conjugacy was introduced by Magnus Hestenes and Garrett Birkhoff in 1936 in the context of …

Abstract State-of-charge (SOC) is the key parameter for battery management, and the
accurate estimation of SOC is pretty important for the safe and stable operation of lithium …

Nonlinear systems present a quite complicate problem. As the number of dimensions
increases, it becomes more difficult to find the solution of the problem. In this paper, a …

Conjugate gradient methods are a class of important methods for solving linear equations
and for solving nonlinear optimization. In this article, a review on conjugate gradient …

In this paper, we are concerned with the conjugate gradient methods for solving
unconstrained optimization problems. It is well-known that the direction generated by a …

In 2012, Rivaie et al. introduced RMIL conjugate gradient (CG) method which is globally
convergent under the exact line search. Later, Dai (2016) pointed out abnormality in the …

Nonlinear conjugate gradient (CG) methods have played an important role in solving large-
scale unconstrained optimization. Their wide application in many fields is due to their low …

Elman network is a classical recurrent neural network with an internal delay feedback. In this
paper, we propose a recalling-enhanced recurrent neural network (RERNN) which has a …

In this work we present and analyze a new scaled conjugate gradient algorithm and its
implementation, based on an interpretation of the secant equation and on the inexact Wolfe …