The proximal gradient and its variants is one of the most attractive first-order algorithm for
minimizing the sum of two convex functions, with one being nonsmooth. However, it requires …

This monograph covers some recent advances in a range of acceleration techniques
frequently used in convex optimization. We first use quadratic optimization problems to …

This work deals with the topic of information processing over graphs. The presentation is
largely self-contained and covers results that relate to the analysis and design of multi-agent …

In this paper we introduce a simple heuristic adaptive restart technique that can dramatically
improve the convergence rate of accelerated gradient schemes. The analysis of the …

This paper considers a class of constrained stochastic composite optimization problems
whose objective function is given by the summation of a differentiable (possibly nonconvex) …

This paper considers the question of recovering the phase of an object from intensity-only
measurements, a problem which naturally appears in X-ray crystallography and related …

We focus on nonconvex and nonsmooth minimization problems with a composite objective,
where the differentiable part of the objective is freed from the usual and restrictive global …

We consider regularized stochastic learning and online optimization problems, where the
objective function is the sum of two convex terms: one is the loss function of the learning …

We consider global efficiency of algorithms for minimizing a sum of a convex function and a
composition of a Lipschitz convex function with a smooth map. The basic algorithm we rely …

Imaging is an interdisciplinary research area with profound applications in many areas of
science, engineering, technology, and medicine. The most primitive form of imaging is visual …