Convergence and stability of iteratively reweighted least squares for low-rank matrix recovery

DOI10.3934/ipi.2017030zbMath1368.65060OpenAlexW2634020449MaRDI QIDQ2360780

Song Li, Yun Cai

Publication date: 12 July 2017

Published in: Inverse Problems and Imaging (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3934/ipi.2017030

zbMATH Keywords

algorithmstabilityconvergencenumerical experimentsnoisy measurementsiteratively reweighted least squaresnuclear norm minimizationsparse signal recoverylow rank matrix recoverymatrix restricted isometry property

Mathematics Subject Classification ID

Numerical solutions to overdetermined systems, pseudoinverses (65F20) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Iterative numerical methods for linear systems (65F10) Matrix completion problems (15A83)

Related Items (3)

Convergence and stability analysis of iteratively reweighted least squares for noisy block sparse recovery ⋮ Minimization of the difference of Nuclear and Frobenius norms for noisy low rank matrix recovery ⋮ Weighted lp − l1 minimization methods for block sparse recovery and rank minimization

Uses Software

• CVX

Cites Work

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