Low-rank matrix recovery for parallel architectures

TitleLow-rank matrix recovery for parallel architectures
Publication TypeSINBAD Presentation
AuthorsOscar Lopez, Rajiv Kumar, Felix J. Herrmann
PublisherSINBAD
Year of Publication2016
Abstract

Low-rank matrix recovery (LRMR) techniques offer potential tools for frugal seismic data acquisition, where dense acquisition is replaced by large-scale optimization. This shift of focus means that efficient numerical methods are critical for successful LRMR implementation in seismic applications. In this talk, we extend the rank-penalization methodology to a parallelizable framework. We adopt a factorization-based alternating minimization scheme and decouple it into an independent system of simpler sub-problems that can be handled in parallel. The methodology is flexible, where the approach can be adapted to the number of workers available. Numerical experiments are conducted to demonstrate that the quality of reconstruction is comparable to existing methods at a fraction of the computational time.

KeywordsPresentation, private, SINBAD, SINBADFALL2016, SLIM
URLhttps://www.slim.eos.ubc.ca/Publications/Private/Conferences/SINBAD/2016/Fall/lopez2016SINBADFlrm/lopez2016SINBADFlrm.pdf
URL1

https://www.slim.eos.ubc.ca/Publications/Private/Conferences/SINBAD/2016...

Citation Keylopez2016SINBADFlrm