A Fixed-Point Continuation Method for L_1-Regularization with Application to Compressed Sensing

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dc.contributor.authorHale, Elaine T.en_US
dc.contributor.authorYin, Wotaoen_US
dc.contributor.authorZhang, Yinen_US
dc.date.accessioned2018-06-18T17:58:14Zen_US
dc.date.available2018-06-18T17:58:14Zen_US
dc.date.issued2007-05en_US
dc.date.noteMay 2007en_US
dc.description.abstractWe consider solving minimization problems with L_1-regularization: min ||x||_1 + mu f(x) particularly for f(x) = (1/2)||Ax-b||M2, where A is m by n and m < n. Our goal is to construct efficient and robust algorithms for solving large-scale problems with dense data, and our approach is based on two powerful algorithmic ideas: operator-splitting and continuation. This paper establishes q-linear convergence rates for our algorithm applied to problems with f(x) convex, but not necessarily strictly convex. We present numerical results for several types of compressed sensing problems, and show that our algorithm compares favorably with three state-of-the-art algorithms when applied to large-scale problems with noisy data.en_US
dc.format.extent45 ppen_US
dc.identifier.citationHale, Elaine T., Yin, Wotao and Zhang, Yin. "A Fixed-Point Continuation Method for L_1-Regularization with Application to Compressed Sensing." (2007) <a href="https://hdl.handle.net/1911/102072">https://hdl.handle.net/1911/102072</a>.en_US
dc.identifier.digitalTR07-07en_US
dc.identifier.urihttps://hdl.handle.net/1911/102072en_US
dc.language.isoengen_US
dc.titleA Fixed-Point Continuation Method for L_1-Regularization with Application to Compressed Sensingen_US
dc.typeTechnical reporten_US
dc.type.dcmiTexten_US
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