A Simple Proof for Recoverability of L1-Minimization (II): the Nonnegativity Case

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dc.contributor.authorZhang, Yin
dc.date.accessioned2018-06-18T17:56:58Z
dc.date.available2018-06-18T17:56:58Z
dc.date.issued2005-09
dc.date.noteSeptember 2005
dc.description.abstractWhen using L1 minimization to recover a sparse, nonnegative solution to a under-determined linear system of equations, what is the highest sparsity level at which recovery can still be guaranteed? Recently, Donoho and Tanner discovered, by invoking classic results from the theory of convex polytopes that the highest sparsity level equals half of the number of equations. In this report, we provide a completely self-contained, yet short and elementary, proof for this remarkable result. We also connect dots for different recoverability conditions obtained from different spaces.
dc.format.extent10 pp
dc.identifier.citationZhang, Yin. "A Simple Proof for Recoverability of L1-Minimization (II): the Nonnegativity Case." (2005) <a href="https://hdl.handle.net/1911/102041">https://hdl.handle.net/1911/102041</a>.
dc.identifier.digitalTR05-10
dc.identifier.urihttps://hdl.handle.net/1911/102041
dc.language.isoeng
dc.titleA Simple Proof for Recoverability of L1-Minimization (II): the Nonnegativity Case
dc.typeTechnical report
dc.type.dcmiText
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