This paper focuses on a time-varying constrained nonconvex optimization problem, and considers the synthesis analysis of online regularized primal-dual gradient methods to track Karush--Kuhn--Tucker (KKT) trajectory. The proposed method is implemented in running fashion, sense that underlying problem changes during execution algorithms. In order study its performance, we first derive continuous...

Dual decomposition has been successfully employed in a variety of distributed convex optimization problems solved by a network of computing and communicating nodes. Often, when the cost function is separable but the constraints are coupled, the dual decomposition scheme involves local parallel subgradient calculations and a global subgradient update performed by a master node. In this paper, we...

In this paper we propose an efficient distributed algorithm for solving loosely coupled convex optimization problems. The algorithm is based on a primal-dual interior-point method in which we use the alternating direction method of multipliers (ADMM) to compute the primal-dual directions at each iteration of the method. This enables us to join the exceptional convergence properties of primal-du...