Smoothing methods, extensively used for solving important mathematical programming
problems and applications, are applied here to generate and solve an unconstrained …

We consider a class of smoothing methods for minimization problems where the feasible set
is convex but the objective function is not convex, not differentiable and perhaps not even …

In this paper, we investigate the solution of bang‐bang optimal control problems by shooting
methods. We will show how modifying the performance index by a term depending on a …

Recently, variable selection and sparse reconstruction are solved by finding an optimal
solution of a minimization model, where the objective function is the sum of a data-fitting …

In this paper we take a new look at smoothing Newton methods for solving the nonlinear
complementarity problem (NCP) and the box constrained variational inequalities (BVI) …

We consider superlinearly convergent analogues of Newton methods for nondifferentiable
operator equations in function spaces. The superlinear convergence analysis of semismooth …

We introduce a new NCP-function in order to reformulate the nonlinear complementarity
problem as a nonsmooth system of equations. This new NCP-function turns out to have …

This paper presents a new formulation for the stochastic linear complementarity problem
(SLCP), which aims at minimizing an expected residual defined by an NCP function. We …

We propose a novel stochastic method, namely the stochastic accelerated mirror-prox
(SAMP) method, for solving a class of monotone stochastic variational inequalities (SVI). The …

A new smoothing strategy for solving\epsilon {\hbox {-}}{\rm {support}} vector regression
(\epsilon {\hbox {-}}{\rm {SVR}}), tolerating a small error in fitting a given data set linearly or …