A geometrical stability condition for compressed sensing

DOI10.1016/j.laa.2016.04.017zbMath1350.94018arXiv1510.08241OpenAlexW1846305482MaRDI QIDQ286166

Axel Flinth

Publication date: 20 May 2016

Published in: Linear Algebra and its Applications (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1510.08241

zbMATH Keywords

sparse recoverycompressed sensingconvex geometryGrassmannian condition number

Mathematics Subject Classification ID

Convex programming (90C25) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Convex sets in (n) dimensions (including convex hypersurfaces) (52A20)

Related Items (2)

Compressed sensing construction for underdetermined source separation ⋮ A Data-Independent Distance to Infeasibility for Linear Conic Systems

Uses Software

• CoSaMP

Cites Work

• Unnamed Item
• A mathematical introduction to compressive sensing
• A geometric analysis of Renegar's condition number, and its interplay with conic curvature
• Some perturbation theory for linear programming
• The convex geometry of linear inverse problems
• On the Reconstruction of Block-Sparse Signals With an Optimal Number of Measurements
• A Coordinate-Free Condition Number for Convex Programming
• Living on the edge: phase transitions in convex programs with random data
• Recovering Compressively Sampled Signals Using Partial Support Information
• Precise Stability Phase Transitions for $\ell_1$ Minimization: A Unified Geometric Framework
• Signal Space CoSaMP for Sparse Recovery With Redundant Dictionaries
• Stable signal recovery from incomplete and inaccurate measurements

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