Difference of Convex functions (DC) Programming and DC Algorithm (DCA) constitute the
backbone of Nonconvex Programming and Global Optimization. The paper is devoted to the …

The year 2015 marks the 30th birthday of DC (Difference of Convex functions) programming
and DCA (DC Algorithms) which constitute the backbone of nonconvex programming and …

DC (difference of convex functions) programming and DC algorithm (DCA) are powerful
tools for nonsmooth nonconvex optimization. This field was created in 1985 by Pham Dinh …

Mathematical optimization has always been at the heart of engineering, statistics, and
economics. In these applied domains, optimization concepts and methods have often been …

Sparse optimization refers to an optimization problem involving the zero-norm in objective or
constraints. In this paper, nonconvex approximation approaches for sparse optimization …

Natural image statistics indicate that we should use nonconvex norms for most
regularization tasks in image processing and computer vision. Still, they are rarely used in …

The boosted difference of convex functions algorithm (BDCA) was recently proposed for
minimizing smooth difference of convex (DC) functions. BDCA accelerates the convergence …

Many modern statistical estimation problems are defined by three major components: a
statistical model that postulates the dependence of an output variable on the input features; …

We present a natural extension of DC programming and DCA for modeling and solving
general DC programs with DC constraints. Two resulting approaches consist in …

We introduce two new algorithms to minimise smooth difference of convex (DC) functions
that accelerate the convergence of the classical DC algorithm (DCA). We prove that the point …