The bounds of restricted isometry constants for low rank matrices recovery

DOI10.1007/s11425-013-4624-yzbMath1273.90156OpenAlexW1910249095MaRDI QIDQ365859

Song Li, Hui-min Wang

Publication date: 9 September 2013

Published in: Science China. Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11425-013-4624-y

zbMATH Keywords

convex optimizationnuclear normcompressed sensinglow-rank matrix recoveryrestricted isometry constantsSchatten-\(p\) norm

Mathematics Subject Classification ID

Related Items (12)

Sharp Restricted Isometry Bounds for the Inexistence of Spurious Local Minima in Nonconvex Matrix Recovery ⋮ Stable recovery of low-rank matrix via nonconvex Schatten \(p\)-minimization ⋮ On the Schatten \(p\)-quasi-norm minimization for low-rank matrix recovery ⋮ A necessary and sufficient condition for sparse vector recovery via \(\ell_1-\ell_2\) minimization ⋮ Sharp RIP bound for sparse signal and low-rank matrix recovery ⋮ Convergence of projected Landweber iteration for matrix rank minimization ⋮ Low-rank matrix recovery via regularized nuclear norm minimization ⋮ Convergence analysis of projected gradient descent for Schatten-\(p\) nonconvex matrix recovery ⋮ Characterization of ℓ1 minimizer in one-bit compressed sensing ⋮ Optimal RIP bounds for sparse signals recovery via \(\ell_p\) minimization ⋮ ROP: matrix recovery via rank-one projections ⋮ Perturbation analysis of low-rank matrix stable recovery

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