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 …

In recent years, research is very active in nonconvex optimization. There are two principal
reasons for this: The first is the importance of its applications to concrete problems in …

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 …

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 …

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 …

In this paper we introduce disciplined convex-concave programming (DCCP), which
combines the ideas of disciplined convex programming (DCP) with convex-concave …

Recent works have revealed an essential paradigm in designing loss functions that
differentiate individual losses versus aggregate losses. The individual loss measures the …

Solutions for learning from large scale datasets, including kernel learning algorithms that
scale linearly with the volume of the data and experiments carried out on realistically large …