Intelligent reflecting surfaces (IRSs) tune wireless environments to increase spectrum and
energy efficiencies. In view of much recent attention to the IRS concept as a promising …

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 …

This paper aims to develop new and fast algorithms for recovering a sparse vector from a
small number of measurements, which is a fundamental problem in the field of compressive …

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 …

Abstract Deep Neural Networks (DNNs) have achieved extraordinary success in numerous
areas. However, DNNs often carry a large number of weight parameters, leading to the …

In this paper, we study the ratio of the L_1 and L_2 norms, denoted as L_1/L_2, to promote
sparsity. Due to the nonconvexity and nonlinearity, there has been little attention to this scale …

This paper studies a fundamental bicriteria optimization problem for variable selection in
statistical learning; the two criteria are a loss/residual function and a model control (also …

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 …