Many functions encountered in applied mathematics and in statistical data analysis can be
expressed in terms of perspective functions. One of the earliest examples is the Fisher …

In last decades, there has been much effort on the solution and the analysis of the nonlinear
complementarity problem (NCP) by reformulating NCP as an unconstrained minimization …

There recently has been much interest in studying optimization problems over symmetric
cones. In this paper, we first investigate a smoothing function in the context of symmetric …

In this paper, we consider using the neural networks to efficiently solve the second-order
cone constrained variational inequality (SOCCVI) problem. More specifically, two kinds of …

In this paper, we study several NCP-functions for the nonlinear complementarity problem
(NCP) which are indeed based on the generalized Fischer–Burmeister function, ϕp (a …

In this paper, we consider a neural network model for solving the nonlinear complementarity
problem (NCP). The neural network is derived from an equivalent unconstrained …

In this paper, we consider the smoothing Newton method for solving a type of absolute value
equations associated with second order cone (SOCAVE for short), which is a generalization …

In this paper, we apply a gradient neural network model to efficiently solve the convex
second-order cone constrained variational inequality problem. According to a smoothing …

In this paper, we consider a new neural network model to simply solve the convex second-
order cone constrained variational inequality problem. Based on a smoothing method, the …

In this paper, we propose a new family of NCP-functions and the corresponding merit
functions, which are the generalization of some popular NCP-functions and the related merit …