Sparse Optimization with Least-Squares Constraints
Abstract
The use of convex optimization for the recovery of sparse signals from incomplete or compressed data is now common practice. Motivated by the success of basis pursuit in recovering sparse vectors, new formulations have been proposed that take advantage of different types of sparsity. In this paper we propose an efficient algorithm for solving a general class of sparsifying formulations. For several common types of sparsity we provide applications, along with details on how to apply the algorithm, and experimental results.
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Copyright © 2011 Society for Industrial and Applied Mathematics.
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Submitted: 3 February 2010
Accepted: 7 February 2011
Published online: 4 October 2011
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