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A decentralized prediction-correction method for networked time-varying convex optimization

  • Delft University of Technology
  • University of Pennsylvania

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We study networked unconstrained convex optimization problems where the objective function changes continuously in time. We propose a decentralized algorithm (DePCoT) with a discrete time-sampling scheme to find and track the solution trajectory based on prediction and gradient-based correction steps, while sampling the problem data at a constant sampling period h. Under suitable conditions and for limited sampling periods, we establish that the asymptotic error bound behaves as O(h2), which outperforms the state of the art existing error bound of O(h) for correction-only methods. The key contributions are the prediction step and a decentralized method to approximate the inverse of the Hessian of the cost function in a decentralized way, which yields quantifiable trade-offs between communication and accuracy.

Original languageEnglish
Title of host publication2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages509-512
Number of pages4
ISBN (Electronic)9781479919635
DOIs
Publication statusPublished - 1 Jan 2015
Externally publishedYes
Event6th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015 - Cancun, Mexico
Duration: 13 Dec 201516 Dec 2015

Publication series

Name2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015

Conference

Conference6th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015
Country/TerritoryMexico
CityCancun
Period13/12/1516/12/15

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