Cai, T. TonyZhang, Arnu2023-05-232023-05-232014-01-012016-07-31https://repository.upenn.edu/handle/20.500.14332/47762This paper considers compressed sensing and affine rank minimization in both noiseless and noisy cases and establishes sharp restricted isometry conditions for sparse signal and low-rank matrixrecovery. The analysis relies on a key technical tool, which represents points in a polytope by convex combinations of sparse vectors. The technique is elementary while yielding sharp results. It is shown that for any given constant t ≥ 4/3, in compressed sensing, δtkA <; √((t-1)/t) guarantees the exactrecovery of all k sparse signals in the noiseless case through the constrained l1 minimization, and similarly, in affine rank minimization, δtrM <; √((t-1)/t) ensures the exact reconstruction of all matriceswith rank at most r in the noiseless case via the constrained nuclear norm minimization. In addition, for any ε > 0, δtkA <; √(t-1/t) + ε is not sufficient to guarantee the exact recovery of all k-sparse signals for large k. Similar results also hold for matrix recovery. In addition, the conditions δtkA <; √((t-)1/t) and δtrM<; √((t-1)/t) are also shown to be sufficient, respectively, for stable recovery of approximately sparsesignals and low-rank matrices in the noisy case.compressed sensingmatrix algebraminimisationsignal representationaffine rank minimizationcompressed sensingconstrained l1 minimizationconstrained nuclear norm minimizationk-sparse signal recoverylow-rank matrix recoverysharp restricted isometry conditionssparse polytope representationsparse vectorsminimization methodsnoisenoise measurementsparse matricesvectorsaffine rank minimizationconstrained nuclear norm minimizationlow-rank matrix recoveryrestricted isometrysparse signal recoveryComputer SciencesStatistics and ProbabilityArticle