A versatile distributed MCMC algorithm for large scale inverse problems

Pierre-Antoine Thouvenin, Audrey Repetti, Pierre Chainais

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

1 Citation (Scopus)
24 Downloads (Pure)

Abstract

For large scale inverse problems, inference can be tackled with distributed algorithms, dividing the task over multiple computing nodes or cores referred to as workers. Since random sampling methods yield not only estimates but also credibility intervals, we leverage data augmentations and MCMC algorithms to design a distributed sampler. In contrast with usual approaches relying on a client-server architecture, we propose a flexible distributed sampler relying on a Single Program Multiple Data implementation, in which all workers have a similar task. This distributed strategy allows the computing time and volume of communications to be reduced by separately handling blocks of data and parameters on different workers. Experiments on a large synthetic image inpainting problem illustrate the performance of the proposed approach to produce high quality estimates in a small amount of time.
Original languageEnglish
Title of host publication30th European Signal Processing Conference 2022
PublisherIEEE
Pages2016-2020
Number of pages5
ISBN (Electronic)9789082797091
DOIs
Publication statusPublished - 18 Oct 2022
Event30th European Signal Processing Conference 2022 - Belgrade, Serbia
Duration: 29 Aug 20222 Sept 2022
Conference number: 30
https://2022.eusipco.org/

Conference

Conference30th European Signal Processing Conference 2022
Abbreviated titleEUSIPCO 2022
Country/TerritorySerbia
CityBelgrade
Period29/08/222/09/22
Internet address

Keywords

  • Markov chain Monte-Carlo methods
  • Single Program Multiple Data architecture
  • distributed algorithm
  • inverse problems

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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