In recent years, mobile devices are equipped with increasingly advanced sensing and
computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up …

Difference of Convex functions (DC) Programming and DC Algorithm (DCA) constitute the
backbone of Nonconvex Programming and Global Optimization. The paper is devoted to the …

This paper gives an overview of the majorization-minimization (MM) algorithmic framework,
which can provide guidance in deriving problem-driven algorithms with low computational …

Non-local low-rank tensor approximation has been developed as a state-of-the-art method
for hyperspectral image (HSI) restoration, which includes the tasks of denoising …

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 …

Migrating computational intensive tasks from mobile devices to more resourceful cloud
servers is a promising technique to increase the computational capacity of mobile devices …

In this paper we study an algorithm for solving a minimization problem composed of a
differentiable (possibly nonconvex) and a convex (possibly nondifferentiable) function. The …

A fundamental challenge in develo** high-impact machine learning technologies is
balancing the need to model rich, structured domains with the ability to scale to big data …

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

In this paper, we explore optimization-based and data-driven solutions in a reconfigurable
intelligent surface (RIS)-aided multi-user mobile edge computing (MEC) system, where the …