Distributed optimization of strongly convex functions on directed time-varying graphs

Angelia Nedic, Alex Olshevsky

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

We investigate the convergence rate of the recently proposed subgadient-push method for distributed separable optimization over time-varying directed graphs. The algorithm requires no knowledge of either the number of agents or the graph sequence to implement, nor the use of doubly stochastic weights. We show that the algorithm converges at a rate of O(ln t/t) for strongly convex functions. The proportionality constant in the rate estimate depends on some problem parameters, the initial values at the nodes, the speed of the network information diffusion, and the imbalances of influence among the nodes.

Original languageEnglish (US)
Title of host publication2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings
Pages329-332
Number of pages4
DOIs
StatePublished - Dec 1 2013
Externally publishedYes
Event2013 1st IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Austin, TX, United States
Duration: Dec 3 2013Dec 5 2013

Publication series

Name2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings

Other

Other2013 1st IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013
CountryUnited States
CityAustin, TX
Period12/3/1312/5/13

ASJC Scopus subject areas

  • Information Systems
  • Signal Processing

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  • Cite this

    Nedic, A., & Olshevsky, A. (2013). Distributed optimization of strongly convex functions on directed time-varying graphs. In 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings (pp. 329-332). [6736882] (2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings). https://doi.org/10.1109/GlobalSIP.2013.6736882