This paper presents two new approximate versions of the alternating direction method of
multipliers (ADMM) derived by modifying of the original “Lagrangian splitting” convergence …

Distributed optimization using multiple computing agents in a localized and coordinated
manner is a promising approach for solving large-scale optimization problems, eg, those …

We propose two approximate versions of the first-order primal-dual algorithm (PDA) to solve
a class of convex-concave saddle point problems. The introduced approximate criteria are …

Compared with the alternating direction method of multipliers (ADMM), the symmetric
ADMM, which updates the Lagrange multiplier twice in each iteration, is a more efficient …

In this paper, we develop two inexact alternating direction methods of multipliers (ADMMs)
with relative error criteria for which only a few parameters are needed to control the error …

In this paper, an inexact Alternating Direction Method of Multipliers (ADMM) has been
proposed for solving the two-block separable convex optimization problem subject to linear …

This paper proposes and analyzes an inexact variant of the proximal generalized alternating
direction method of multipliers (ADMM) for solving separable linearly constrained convex …

We propose a new relative-error inexact version of the alternating direction method of
multipliers (ADMM) for convex optimization. We prove the asymptotic convergence of our …

This paper proposes a partially inexact proximal alternating direction method of multipliers
for computing approximate solutions of a linearly constrained convex optimization problem …

One of the most popular algorithms for saddle point problems is the so-named primal-dual
hybrid gradient method, which have been received much considerable attention in the …