In this paper, we analyze the convergence of the alternating direction method of multipliers
(ADMM) for minimizing a nonconvex and possibly nonsmooth objective function, ϕ (x_0 …

A large number of imaging problems reduce to the optimization of a cost function, with
typical structural properties. The aim of this paper is to describe the state of the art in …

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 …

We propose an asynchronous, decentralized algorithm for consensus optimization. The
algorithm runs over a network in which the agents communicate with their neighbors and …

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 …

In this work we propose a new splitting technique, namely Asymmetric Forward–Backward–
Adjoint splitting, for solving monotone inclusions involving three terms, a maximally …

In this paper, we propose a robust linear discriminant analysis (RLDA) through
Bhattacharyya error bound optimization. RLDA considers a nonconvex problem with the L 1 …

Tseng's algorithm finds a zero of the sum of a maximally monotone operator and a
monotone continuous operator by evaluating the latter twice per iteration. In this paper, we …

We propose a new and low per-iteration complexity first-order primal-dual optimization
framework for a convex optimization template with broad applications. Our analysis relies on …