In this paper, we consider a proximal linearized alternating direction method of multipliers, or
PL-ADMM, for solving linearly constrained nonconvex and possibly nonsmooth optimization …

This work proposes an accelerated primal-dual dynamical system for affine constrained
convex optimization and presents a class of primal-dual methods with nonergodic …

This paper proposes a novel CTA (Combine-Then-Adapt)-based decentralized algorithm for
solving convex composite optimization problems over undirected and connected networks …

In this article, we propose a novel dual inexact splitting algorithm (DISA) for distributed
convex composite optimization problems, where the local loss function consists of a smooth …

We introduce a novel primal-dual flow for affine constrained convex optimization problems.
As a modification of the standard saddle-point system, our flow model is proved to possess …

Many iterative methods in applied mathematics can be thought of as fixed-point iterations,
and such algorithms are usually analyzed analytically, with inequalities. In this paper, we …

We study linear convergence of some first-order methods such as the proximal gradient
method (PGM), the proximal alternating linearized minimization (PALM) algorithm and the …

Inference by means of mathematical modeling from a collection of observations remains a
crucial tool for scientific discovery and is ubiquitous in application areas such as signal …

This paper provides a self-contained ordinary differential equation solver approach for
separable convex optimization problems. A novel primal-dual dynamical system with built-in …

In this paper, we develop a symmetric accelerated stochastic Alternating Direction Method of
Multipliers (SAS-ADMM) for solving separable convex optimization problems with linear …