Paper Info
Title
Saddle-Point Dynamics For Distributed Convex Optimization On General Directed Graphs
Abstract
We show that the continuous-time saddle-point distributed convex optimization algorithm can be formulated as the trajectories of a distributed control systems, where the control input to the dynamics of each agent relies on an observer that estimates the average state. Using this observation and by incorporating a continuous-time version of the so-called push-sum algorithm, this paper relaxes the graph theoretic conditions under which the first component of the trajectories of this modified class of saddle-point dynamical systems for distributed optimization are asymptotically convergent to the set of optimizers. In particular, we prove that strong connectivity is sufficient under this modified dynamics, relaxing the known weight-balanced assumption. As a by product, we also show that the saddle-point distributed optimization dynamics can be extended to time-varying weight-balanced graphs which satisfy a persistency condition on the min-cut of the sequence of Laplacian matrices.
Year
Venue
Field
2016
2016 IEEE 55TH CONFERENCE ON DECISION AND CONTROL (CDC)
Saddle,Mathematical optimization,Saddle point,Control theory,Computer science,Directed graph,Symmetric matrix,Convex function,Dynamical systems theory,Observer (quantum physics),Convex optimization
DocType
ISSN
Citations 
Conference
0743-1546
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Behrouz Touri117621.12
Bahman Gharesifard234026.54