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

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 …

The primal-dual hybrid gradient (PDHG) algorithm is popular in solving min-max problems
which are being widely used in a variety of areas. To improve the applicability and efficiency …

Primal-dual hybrid gradient (PDHG) method is a canonical and popular prototype for solving
saddle point problem (SPP). However, the nonlinear coupling term in SPP excludes the …

In this paper, based a novel primal-dual dynamical model with adaptive scaling parameters
and Bregman divergences, we propose new accelerated primal-dual proximal gradient …

In this paper, we study a first-order inexact primal-dual algorithm (I-PDA) for solving a class
of convex-concave saddle point problems. The I-PDA, which involves a relative error …

The class of convex–concave bilinear saddle point problems encompasses many important
convex optimization models arising in a wide array of applications. The most of existing …

In this paper, we design an inertial accelerated primal–dual algorithm to address the convex–
concave saddle point problem, which is formulated as min x max yf (x)+< K x, y>− g (y) …

In this paper, we design first-order primal-dual algorithms to address the convex-concave
saddle point problem, which is formulated as $\min_ {x}\max_ {y} f (x)+\langle Kx, y\rangle-g …

This work presents a universal accelerated primal–dual method for affinely constrained
convex optimization problems. It can handle both Lipschitz and Hölder gradients but does …