Purpose Big data is increasingly becoming a major organizational enterprise force to reckon
with in this global era for all sizes of industries. It is a trending new enterprise system or …

Accompanied with the rising popularity of compressed sensing, the Alternating Direction
Method of Multipliers (ADMM) has become the most widely used solver for linearly …

Background subtraction has been a fundamental and widely studied task in video analysis,
with a wide range of applications in video surveillance, teleconferencing, and 3D modeling …

The augmented Lagrangian method (ALM) is a classical optimization tool that solves a given
“difficult”(constrained) problem via finding solutions of a sequence of “easier”(often …

In this paper, we consider a block-structured convex optimization model, where in the
objective the block variables are nonseparable and they are further linearly coupled in the …

Fog computing is a promising architectural pattern to reduce the amount of data that is
transferred to the cloud for processing and analysis. In this paper, we study fog-assisted data …

We consider minimization of the sum of a large number of convex functions, and we propose
an incremental aggregated version of the proximal algorithm, which bears similarity to the …

Abstract The classic Alternating Direction Method of Multipliers (ADMM) is a popular
framework to solve linear-equality constrained problems. In this paper, we extend the ADMM …

Probabilistic ideas and tools have recently begun to permeate into several fields where they
had traditionally not played a major role, including fields such as numerical linear algebra …

The presence of TV regularizer always induces an unsatisfactory staircase effect. To
overcome the staircase while better sustaining edge information, this work proposes a novel …