Elman neural network (ENN) is one of the local recursive networks with a feedback
mechanism. The parameter conjugate gradient method is a promising alternative to the …

Conjugate gradient methods are the most famous methods for solving unconstrained, large-
scale optimization. In this article, we propose a new coefficient of the conjugate gradient …

The three-term conjugate gradient methods solving large-scale optimization problems are
favored by many researchers because of their nice descent and convergent properties. In …

Nonlinear systems of equations are widely used in science and engineering and, therefore,
exploring efficient ways to solve them is paramount. In this paper, a new derivative-free …

This paper presents a numerical investigation of the spectral conjugate directions
formulation for optimizing unconstrained problems. A novel modified algorithm is proposed …

Conjugate Gradient (CG) method is the most prominent iterative mathematical technique
that can be useful for the optimization of both linear and non-linear systems due to its …

Elman recurrent network is a representative model with feedback mechanism. Although
gradient descent method has been widely used to train Elman network, it frequently leads to …

Hybridization is one of the popular approaches in modifying the conjugate gradient method.
In this paper, a new hybrid conjugate gradient is suggested and analyzed in which the …

Conjugate gradient (CG) methods were instrumental in solving unconstrained, wide-ranging
optimization. In this paper, we propose a new CG coefficient family, which holds conditions …

This paper applies an idea of adaptive momentum for the nonlinear conjugate gradient to
accelerate optimization problems in sparse recovery. Specifically, we consider two types of …