A Geometrically Converging Dual Method for Distributed Optimization Over Time-Varying Graphs [Elektronisk resurs]

• In this article, we consider a distributed convex optimization problem over time-varying undirected networks. We propose a dual method, primarily averaged network dual ascent (PANDA), that is proven to converge R-linearly to the optimal point given that the agents' objective functions are strongly convex and have Lipschitz continuous gradients. Like dual decomposition, PANDA requires half the amount of variable exchanges per iterate of methods based on DIGing, and can provide with practical improved performance as empirically demonstrated. 

Ämnesord

Engineering and Technology  (hsv)

Electrical Engineering, Electronic Engineering, Information Engineering  (hsv)

Control Engineering  (hsv)

Teknik och teknologier  (hsv)

Elektroteknik och elektronik  (hsv)

Reglerteknik  (hsv)

Genre

government publication  (marcgt)

Indexterm och SAB-rubrik

Optimization

Convergence

Linear programming

Convex functions

Symmetric matrices

Europe

Information science

Convex optimization

distributed optimization

time-varying networks