Sparse Optimization with Least-Squares Constraints
Sparse Optimization with Least-Squares Constraints
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DOI：
10.1137/100785028
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发表时间：
2011-10
期刊：
影响因子：
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通讯作者：
E. Berg;M. Friedlander
中科院分区：
文献类型：
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作者：
E. Berg;M. Friedlander
利用凸优化从不完全或压缩数据中恢复稀疏信号现在是常见的做法。受基追踪在恢复稀疏向量方面取得成功的启发，人们提出了利用不同类型稀疏性的新公式。在本文中，我们提出了一种用于求解一类通用的稀疏化公式的高效算法。对于几种常见的稀疏性类型，我们提供了应用实例，以及如何应用该算法的详细信息和实验结果。
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.