Optimization methods are at the core of many problems in signal/image processing,
computer vision, and machine learning. For a long time, it has been recognized that looking …

We introduce ProxSkip—a surprisingly simple and provably efficient method for minimizing
the sum of a smooth ($ f $) and an expensive nonsmooth proximable ($\psi $) function. The …

We revisit the proofs of convergence for a first order primal–dual algorithm for convex
optimization which we have studied a few years ago. In particular, we prove rates of …

Operator-splitting methods convert optimization and inclusion problems into fixed-point
equations; when applied to convex optimization and monotone inclusion problems, the …

Operator-splitting schemes are iterative algorithms for solving many types of numerical
problems. A lot is known about these methods: they converge, and in many cases we know …

Convex nonsmooth optimization problems, whose solutions live in very high dimensional
spaces, have become ubiquitous. To solve them, the class of first-order algorithms known as …

We propose a new plug-and-play image restoration method based on primal-dual splitting.
Existing plug-and-play image restoration methods interpret any off-the-shelf Gaussian …

We propose a new definition for the gradient field of a discrete image defined on a twice
finer grid. The differentiation process from an image to its gradient field is viewed as the …

The goal of this article is to promote the use of fixed point strategies in data science by
showing that they provide a simplifying and unifying framework to model, analyze, and solve …

In this paper, we propose a new primal–dual algorithm for minimizing f (x)+ g (x)+ h (A x) f
(x)+ g (x)+ h (A x), where f, g, and h are proper lower semi-continuous convex functions, f is …