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

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dc.contributor.authorZhang, Yinen_US
dc.date.accessioned2018-06-18T17:56:58Zen_US
dc.date.available2018-06-18T17:56:58Zen_US
dc.date.issued2005-09en_US
dc.date.noteSeptember 2005en_US
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.en_US
dc.format.extent10 ppen_US
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>.en_US
dc.identifier.digitalTR05-10en_US
dc.identifier.urihttps://hdl.handle.net/1911/102041en_US
dc.language.isoengen_US
dc.titleA Simple Proof for Recoverability of L1-Minimization (II): the Nonnegativity Caseen_US
dc.typeTechnical reporten_US
dc.type.dcmiTexten_US
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