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 …

It is well known that the conjugate gradient algorithm is one of the most classic and useful
methods for solving large-scale optimization problems, where the Polak-Ribière-Polyak …

Deep neural networks of deep learning algorithms can be applied into regressions and
classifications. While the regression performances and classification performances of the …

Conjugate gradient methods have played a vital role in finding the minimizers of large-scale
unconstrained optimization problems due to the simplicity of their iteration, convergence …

The new coronavirus, SARS-CoV-2, caused the COVID-19 pandemic, characterized by its
high rate of contamination, propagation capacity, and lethality rate. In this work, we …

In this paper, a nonlinear conjugate gradient algorithm is presented. This given algorithm
possesses the following properties:(i) the sufficient descent property is satisfied;(ii) the trust …

In the process of ship collision avoidance decision making, steering collision avoidance is
the most frequently adopted collision avoidance method. In order to obtain an effective and …

Abstract The Broyden–Fletcher–Goldfarb–Shanno (BFGS) method plays an important role
among the quasi-Newton algorithms for nonconvex and unconstrained optimization …

A Polak–Ribière–Polyak (PRP) algorithm is one of the oldest and popular conjugate
gradient algorithms for solving nonlinear unconstrained optimization problems. In this paper …

It is efficient to use the Hestenes–Stiefe (HS) conjugate gradient algorithm in solving large-
scale complex smooth optimization problems because of its simplicity and low calculation …