PROMP: a sparse recovery approach to lattice-valued signals

DOI10.1016/j.acha.2016.12.004zbMath1408.94862OpenAlexW2593005003MaRDI QIDQ1669069

Gitta Kutyniok, Axel Flinth

Publication date: 30 August 2018

Published in: Applied and Computational Harmonic Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.acha.2016.12.004

zbMATH Keywords

high-dimensional geometrycompressed sensingorthogonal matching pursuitbasis pursuitlattice search

Mathematics Subject Classification ID

Geometric probability and stochastic geometry (60D05) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Related Items (8)

Optimizing Sparsity over Lattices and Semigroups ⋮ Compressed sensing for finite-valued signals ⋮ Sparse recovery with integrality constraints ⋮ Lattices from equiangular tight frames ⋮ An algebraic perspective on integer sparse recovery ⋮ Lattices from tight frames and vertex transitive graphs ⋮ Unbiasing in iterative reconstruction algorithms for discrete compressed sensing ⋮ Sparse representation of vectors in lattices and semigroups

Uses Software

• CVX
• PDCO
• CoSaMP

Cites Work

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• A mathematical introduction to compressive sensing
• CoSaMP: Iterative signal recovery from incomplete and inaccurate samples
• The dimension of almost spherical sections of convex bodies
• Probability of unique integer solution to a system of linear equations
• Counting the faces of randomly-projected hypercubes and orthants, with applications
• Smallest singular value of random matrices and geometry of random polytopes
• Optimal Choice of Weights for Sparse Recovery With Prior Information
• Explicit Matrices with the Restricted Isometry Property: Breaking the Square-Root Bottleneck
• Greed is Good: Algorithmic Results for Sparse Approximation
• Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
• Iteratively reweighted least squares minimization for sparse recovery
• Lower bounds on the maximum cross correlation of signals (Corresp.)
• Atomic Decomposition by Basis Pursuit
• On the Reconstruction of Block-Sparse Signals With an Optimal Number of Measurements
• Spatial Compressive Sensing for MIMO Radar
• Closest point search in lattices
• Recovering Compressively Sampled Signals Using Partial Support Information
• Model-Based Compressive Sensing
• Sparse nonnegative solution of underdetermined linear equations by linear programming
• Stable signal recovery from incomplete and inaccurate measurements

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