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

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dc.contributor.authorHale, Elaine T.
dc.contributor.authorYin, Wotao
dc.contributor.authorZhang, Yin
dc.date.accessioned2018-06-18T17:58:14Z
dc.date.available2018-06-18T17:58:14Z
dc.date.issued2007-05
dc.date.noteMay 2007
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.
dc.format.extent45 pp
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>.
dc.identifier.digitalTR07-07
dc.identifier.urihttps://hdl.handle.net/1911/102072
dc.language.isoeng
dc.titleA Fixed-Point Continuation Method for L_1-Regularization with Application to Compressed Sensing
dc.typeTechnical report
dc.type.dcmiText
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