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 considers nonconvex distributed constrained optimization over networks,
modeled as directed (possibly time-varying) graphs. We introduce the first algorithmic …

The past decade has seen the rapid growth of model based image reconstruction (MBIR)
algorithms, which are often applications or adaptations of convex optimization algorithms …

Mathematical optimization has always been at the heart of engineering, statistics, and
economics. In these applied domains, optimization concepts and methods have often been …

We consider a class of difference-of-convex (DC) optimization problems whose objective is
level-bounded and is the sum of a smooth convex function with Lipschitz gradient, a proper …

In this paper, we focus on the constrained sparse regression problem, where the loss
function is convex but nonsmooth and the penalty term is defined by the cardinality function …

We study the minimization problem of a non-convex sparsity promoting penalty function, the
transformed l_1 l 1 (TL1), and its application in compressed sensing (CS). The TL1 penalty …

Recent years have witnessed a surge of interest in parallel and distributed optimization
methods for large-scale systems. In particular, nonconvex large-scale optimization problems …

Many contemporary applications in signal processing and machine learning give rise to
structured nonconvex nonsmooth optimization problems that can often be tackled by simple …

Many modern statistical estimation problems are defined by three major components: a
statistical model that postulates the dependence of an output variable on the input features; …