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 …

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 …

We discuss two key problems related to learning and optimization of neural networks: the
computation of the adversarial attack for adversarial robustness and approximate …

Graph learning has emerged as a promising technique for multi-view clustering due to its
ability to learn a unified and robust graph from multiple views. However, existing graph …

In the present paper, we are concerned with conditions ensuring the exact penalty for
nonconvex programming. Firstly, we consider problems with concave objective and …

Difference-of-Convex programming and related algorithms, which constitute the backbone of
nonconvex programming and global optimization, were introduced in 1985 by Pham Dinh …

We present effective linear programming based computational techniques for solving
nonconvex quadratic programs with box constraints (BoxQP). We first observe that known …

In this paper we investigate two approaches to minimizing a quadratic form subject to the
intersection of finitely many ellipsoids. The first approach is the dc (difference of convex …

We consider a recent branch-and-bound algorithm of the authors for nonconvex quadratic
programming. The algorithm is characterized by its use of semidefinite relaxations within a …