We introduce the CRONOS algorithm for convex optimization of two-layer neural networks.
CRONOS is the first algorithm capable of scaling to high-dimensional datasets such as …

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

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

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 …

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 …

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 …

The alternating direction method of multipliers (ADMM) proposed by Glowinski and
Marrocco is a benchmark algorithm for two-block separable convex optimization problems …

Primal-dual hybrid gradient method (PDHG, aka Chambolle and Pock method) is a well-
studied algorithm for minimax optimization problems with a bilinear interaction term …

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