Due to the malicious attacks in wireless networks, physical layer security has attracted
increasing concerns from both academia and industry. The research on physical layer …

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 …

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 …

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

Given a set of entities associated with points in Euclidean space, minimum sum-of-squares
clustering (MSSC) consists in partitioning this set into clusters such that the sum of squared …

Fundamental qualitative properties of the minimum sum-of-squares clustering problem are
established in this paper. We prove that the problem always has a global solution and …

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 …

Feature selection consists of choosing a subset of available features that capture the
relevant properties of the data. In supervised pattern classification, a good choice of features …

This paper introduces an algorithm for solving the minimum sum-of-squares clustering
problems using their difference of convex representations. A non-smooth non-convex …

The purpose of this paper is to develop new efficient approaches based on DC (Difference
of Convex functions) programming and DCA (DC Algorithm) to perform clustering via …