In the past decade, sparse and low-rank recovery has drawn much attention in many areas
such as signal/image processing, statistics, bioinformatics, and machine learning. To …

In this paper we consider nonconvex optimization and learning over a network of distributed
nodes. We develop a Proximal Primal-Dual Algorithm (Prox-PDA), which enables the …

Energy disaggregation or non-intrusive load monitoring addresses the issue of extracting
device-level energy consumption information by monitoring the aggregated signal at one …

In this paper, we consider distributed optimization problems over a multiagent network,
where each agent can only partially evaluate the objective function, and it is allowed to …

In this paper, we propose a perturbed proximal primal–dual algorithm (PProx-PDA) for an
important class of linearly constrained optimization problems, whose objective is the sum of …

This paper explores and analyzes two randomized designs for robust principal component
analysis employing low-dimensional data sketching. In one design, a data sketch is …

In this paper, we consider distributed optimization problems over a multi-agent network,
where each agent can only partially evaluate the objective function, and it is allowed to …

We study a stochastic and distributed algorithm for nonconvex problems whose objective
consists a sum $ N $ nonconvex $ L_i/N $-smooth functions, plus a nonsmooth regularizer …

In this paper, we study a class of nonconvex nonsmooth optimization problems with bilinear
constraints, which have wide applications in machine learning and signal processing. We …

Alternating Direction Method of Multipliers (ADMM) has recently been proposed as a
potential alternative optimizer to the Stochastic Gradient Descent (SGD) for deep learning …