With the popularity of distributed power generation units, traditional consumers become
prosumers. Meanwhile, the emerging information and communication technology brings …

False data injection attacks (FDIA) against power system state estimation have been well
studied due to its potential threat to real-time energy management. However, the existing …

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 …

For a linear equality constrained convex optimization problem, we initially propose a mixed
primal-dual dynamical system with Hessian driven damping. This dynamical system …

This paper focuses on a time-varying constrained nonconvex optimization problem, and
considers the synthesis and analysis of online regularized primal-dual gradient methods to …

Second-order dynamical systems are important tools for solving optimization problems, and
most of the existing works in this field have focused on unconstrained optimization problems …

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

In this paper, we investigate the continuous time partial primal–dual gradient dynamics (P-
PDGD) for solving convex optimization problems with the form min x∈ X, y∈ Ω f (x)+ h (y), st …

Primal-dual dynamics (PDD) and its variants are prominent first-order continuous-time
algorithms to determine the primal and dual solutions of a constrained optimization problem …