A Bayesian Approach for Asynchronous Parallel Sparse Recovery

Asynchronous parallel algorithms are often studied for separable optimization problems where the component objective functions are sparse, or act on only a few components of the variable x ∈ R N . One challenge to developing asynchronous approaches for sparse recovery is that the optimization formul...

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Bibliographic Details
Published in2018 52nd Asilomar Conference on Signals, Systems, and Computers pp. 1980 - 1984
Main Authors Zaeemzadeh, Alireza, Haddock, Jamie, Rahnavard, Nazanin, Needell, Deanna
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2018
Subjects
Bayes methods
Convergence
Inference algorithms
Linear programming
Optimization
Program processors
Reliability
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Summary:Asynchronous parallel algorithms are often studied for separable optimization problems where the component objective functions are sparse, or act on only a few components of the variable x ∈ R N . One challenge to developing asynchronous approaches for sparse recovery is that the optimization formulation of this problem has dense component objective functions. However, the assumed sparsity of the signal may be exploited in an asynchronous parallel approach. Here we propose such an approach where multiple processors asynchronously infer hidden variables that estimate the support of x in a Bayesian manner. We include numerical simulations that demonstrate the potential benefits of this method.
ISSN:2576-2303
DOI:10.1109/ACSSC.2018.8645176