In this article, we establish the local and global exponential convergence of a primal–dual
dynamics (PDD) for solving equality-constrained optimization problems without strong …

This article studies the finite-time (FT) convergence of a fast primal–dual gradient dynamics
(PDGD), called FT-PDGD, for solving constrained optimization with general constraints and …

In this note, we provide an overarching analysis of primal-dual dynamics associated to linear
equality-constrained optimization problems using contraction analysis. For the well-known …

This paper establishes exponential convergence rates for a class of primal–dual gradient
algorithms in distributed optimization without strong convexity. The convergence analysis is …

We study a class of distributed optimization problems with a globally coupled equality
constraint. A novel nested primal-dual gradient algorithm (NPGA) is proposed from the dual …

In this article, we provide an overarching analysis of primal-dual dynamics associated with
linear equality-constrained optimization problems using contraction analysis. For the well …

This paper studies the distributed optimization problem when the objective functions might
be nondifferentiable and subject to heterogeneous set constraints. Unlike existing …

In this paper, we analyze the performance of the primal-dual gradient dynamics algorithm in
the presence of stochastic communication channel uncertainty. In contrast to the existing …

We study the problem of minimizing a sum of local objective convex functions over a network
of processors/agents. This problem naturally calls for distributed optimization algorithms, in …

This article considers distributed optimization by a group of agents over an undirected
network. The objective is to minimize the sum of a twice differentiable convex function and …