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 …

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 …

We present a number of novel contributions to the multiple-source adaptation problem. We
derive new normalized solutions with strong theoretical guarantees for the cross-entropy …

We study minimization of the difference of \ell_1 and \ell_2 norms as a nonconvex and
Lipschitz continuous metric for solving constrained and unconstrained compressed sensing …

Optimization has been expanding in all directions at an astonishing rate during the last few
decades. New algorithmic and theoretical techniques have been developed, the diffusion …

Nonmonotone projected gradient techniques are considered for the minimization of
differentiable functions on closed convex sets. The classical projected gradient schemes are …

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 …

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 …

In this paper, we propose the MIML (Multi-Instance Multi-Label learning) framework where
an example is described by multiple instances and associated with multiple class labels …