This monograph is about a class of optimization algorithms called proximal algorithms. Much
like Newton's method is a standard tool for solving unconstrained smooth optimization …

In this paper we propose novel methods for completion (from limited samples) and de-
noising of multilinear (tensor) data and as an application consider 3-D and 4-D (color) video …

Sparsity has been widely exploited for exact reconstruction of a signal from a small number
of random measurements. Recent advances have suggested that structured or group …

This book, as the title suggests, is about first-order methods, namely, methods that exploit
information on values and gradients/subgradients (but not Hessians) of the functions …

We present a method for learning sparse representations shared across multiple tasks. This
method is a generalization of the well-known single-task 1-norm regularization. It is based …

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

The convex feasibility problem, that is, nding a point in the intersection of nitely many closed
convex sets in Euclidean space, arises in various areas of mathematics and physical …

We present a new method for regularized convex optimization and analyze it under both
online and stochastic optimization settings. In addition to unifying previously known firstorder …

We present a series of new theoretical, algorithmic, and empirical results for domain
adaptation and sample bias correction in regression. We prove that the discrepancy is a …

Speckle reduction is a longstanding topic in synthetic aperture radar (SAR) imaging. Since
most current and planned SAR imaging satellites operate in polarimetric, interferometric, or …