Byp-power (or partial p-power) transformation, the

Lagrangian function in nonconvex optimization problem becomes

locally convex. In this paper, we present a neural network based on

an NCP function for solving nonconvex...

In this paper we present a novel recurrent neural network for solving convex nonlinear

programming problems. The proposed neural network is derived based on an NCP function, and one

of the prominent features of this neural network...

his article presents a novel neural network (NN) based on NCP function for solving nonconvex nonlinear optimization (NCNO) problem subject to nonlinear inequality constraints. We first apply the p-power convexification of

the Lagrangian...

This paper presents a neural network based on NCP function to solve a class of nonconvex nonlinear optimization (NCNO) problems. The proposed neural network is a gradient model, which is constructed with an NCP

function and an unconstrained...

In this paper we propose a novel neural network model to solving linear

and (convex) quadratic programming problems. The neural network model is

derived based of an NCP function. In theoretical aspect, global convergence of...

This paper presents a gradient neural network model for solving convex nonlinear

programming (CNP) problems. The main idea is to convert the CNP problem into an equivalent

unconstrained minimization problem ...

In this paper, we present a recurrent neural network for solving mixed linear complementarity

problems (MLCPs) with positive semi-definite matrices. The proposed neural

network is derived based on an NCP function and has a low...

The present scientific attempt is devoted to investigating the fuzzy nonconvex optimization problems (NCOPs) utilizing the concepts of recurrent neural networks (RNNs). To the best of our knowledge, this paper is the first ...