A versatile distributed MCMC algorithm for large scale inverse problems
| Autor: | Pierre-Antoine Thouvenin, Audrey Repetti, Pierre Chainais |
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| Přispěvatelé: | Université de Lille, Centrale Lille, Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Heriot-Watt University [Edinburgh] (HWU) |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | 30th European Signal Processing Conference, EUSIPCO 2022 30th European Signal Processing Conference, EUSIPCO 2022, Aug 2022, Belgrade, Serbia |
| Popis: | International audience; 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. Index Terms-Markov chain Monte-Carlo methods, distributed algorithm, inverse problems, Single Program Multiple Data architecture. |
| Databáze: | OpenAIRE |
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