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 …

Proximal splitting algorithms are well suited to solving large-scale nonsmooth optimization
problems, in particular those arising in machine learning. We propose a new primal-dual …

Phase retrieval (PR) aims to recover a signal from the magnitudes of a set of inner products.
This problem arises in many audio signal processing applications which operate on a short …

Perspective functions arise explicitly or implicitly in various forms in applied mathematics
and in statistical data analysis. To date, no systematic strategy is available to solve the …

Safe screening rules are powerful tools to accelerate iterative solvers in sparse regression
problems. They allow early identification of inactive coordinates (ie, those not belonging to …

This paper proposes a randomized optimization framework for constrained signal
reconstruction, where the word" constrained" implies that data-fidelity is imposed as a hard …

Point-SAGA is a randomized algorithm for minimizing a sum of convex functions using their
proximity operators (proxs), proposed by Defazio (2016). At every iteration, the prox of only …

Convex optimization problems, whose solutions live in very high dimensional spaces, have
become ubiquitous. To solve them, proximal splitting algorithms are particularly adequate …

Consider $ n $ agents connected over a network collaborate to minimize the average of their
local cost functions combined with a common nonsmooth function. This paper introduces a …

A perspective function is a construction which combines a base function defined on a given
space with a nonlinear scaling function defined on another space and which yields a lower …