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 …

We investigate the convex–concave procedure, a local heuristic that utilizes the tools of
convex optimization to find local optima of difference of convex (DC) programming problems …

This paper is devoted to a thorough study on convex analysis approach to dc (difference of
convex functions) programming and gives the State of the Art. Main results about dc duality …

The DC programming and its DC algorithm (DCA) address the problem of minimizing a
function f= g− h (with g, h being lower semicontinuous proper convex functions on R n) on …

This paper is devoted to difference of convex functions (dc) optimization: dc duality, local
and global optimality conditions in dc programming, the dc algorithm (DCA), and its …

Linearly constrained indefinite quadratic problems play an important role in global
optimization. In this paper we study dc theory and its local approachto such problems. The …

Feature selection is an important combinatorial optimisation problem in the context of
supervised pattern classification. This paper presents four novel continuous feature …

Optimization problems involving dc functions (differences of convex functions) and dc sets
(differences of convex sets) occur quite frequently in operations research, economics …

High-dimensional regression/classification continues to be an important and challenging
problem, especially when features are highly correlated. Feature selection, combined with …

We present a novel approach to the tomographic reconstruction of binary objects from few
projection directions within a limited range of angles. A quadratic objective functional over …