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Algorithms for simultaneous sparse approximation. Part II: Convex relaxation
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DOI：10.1016/j.sigpro.2005.05.031.png)
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Abstract
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A simultaneous sparse approximation problem requests a good approximation of several input signals at once using different linear combinations of the same elementary signals. At the same time, the problem balances the error in approximation against the total number of elementary signals that participate. These elementary signals typically model coherent structures in the input signals, and they are chosen from a large, linearly dependent collection. The first part of this paper presents theoretical and numerical results for a greedy pursuit algorithm, called simultaneous orthogonal matching pursuit. The second part of the paper develops another algorithmic approach called convex relaxation. This method replaces the combinatorial simultaneous sparse approximation problem with a closely related convex program that can be solved efficiently with standard mathematical programming software. The paper develops conditions Linder which convex relaxation computes good solutions to simultaneous sparse approximation problems. (C) 2005 Elsevier B.V. All rights reserved.
Keywords:
combinatorial optimization
convex relaxation
multiple measurement vectors
simultaneous sparse approximation
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