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 …

Optimization techniques are among the most promising methods to deal with real-world
problems, consisting of several objective functions and constraints. Over the decades, many …

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 …

We propose a DC (Difference of two Convex functions) formulation approach for sparse
optimization problems having a cardinality or rank constraint. With the largest-k norm, an …

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

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

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

Abstract Support Vector Machine (SVM) has become one of the traditional machine learning
algorithms the most used in prediction and classification tasks. However, its behavior …

We develop an exact penalty approach for feature selection in machine learning via the zero-
norm ℓ _ 0 ℓ 0-regularization problem. Using a new result on exact penalty techniques we …

We consider the problem of designing policies for Markov decision processes (MDPs) with
dynamic coherent risk objectives and constraints. We begin by formulating the problem in a …