Flavors of Compressive Sensing (Q4609797)

scientific article; zbMATH DE number 6852805

Language	Label	Description	Also known as
English	Flavors of Compressive Sensing	scientific article; zbMATH DE number 6852805

Statements

instance of

scholarly article

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title

Flavors of Compressive Sensing (English)

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author

Simon Foucart

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published in

Springer Proceedings in Mathematics & Statistics

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publication date

26 March 2018

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zbMATH Keywords

Gelfand width

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Random matrices

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restricted isometry property

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iterative hard thresholding

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orthogonal matching pursuit

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basis pursuit

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describes a project that uses

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MaRDI publication profile

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full work available at URL

https://doi.org/10.1007/978-3-319-59912-0_4

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cites work

Restricted isometry property of matrices with independent columns and neighborly polytopes by random sampling

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Living on the edge: phase transitions in convex programs with random data

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Certifying the Restricted Isometry Property is Hard

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One-bit compressive sensing of dictionary-sparse signals

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Hard thresholding pursuit algorithms: number of iterations

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Bounded orthogonal systems and the $\Lambda$ (p)-set problem

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Explicit constructions of RIP matrices and related problems

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Are bitvectors optimal?

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Sparse Representation of a Polytope and Recovery of Sparse Signals and Low-Rank Matrices

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Solving quadratic equations via phaselift when there are about as many equations as unknowns

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Exact matrix completion via convex optimization

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Decoding by Linear Programming

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Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information

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Compressed sensing with coherent and redundant dictionaries

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PhaseLift: Exact and Stable Signal Recovery from Magnitude Measurements via Convex Programming

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Polynomial approximation via compressed sensing of high-dimensional functions on lower sets

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Compressed sensing and best 𝑘-term approximation

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Orthogonal matching pursuit under the restricted isometry property

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Signal Space CoSaMP for Sparse Recovery With Redundant Dictionaries

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Restricted Isometry Constants Where $\ell ^{p}$ Sparse Recovery Can Fail for $0≪ p \leq 1$

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For most large underdetermined systems of linear equations the minimal 𝓁<sub>1</sub>‐norm solution is also the sparsest solution

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Counting faces of randomly projected polytopes when the projection radically lowers dimension

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Observed universality of phase transitions in high-dimensional geometry, with implications for modern data analysis and signal processing

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Stability and Robustness of Weak Orthogonal Matching Pursuits

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Stability and robustness of $\ell_1$-minimizations with Weibull matrices and redundant dictionaries

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Dictionary-sparse recovery via thresholding-based algorithms

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Real versus complex null space properties for sparse vector recovery

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Sparse recovery with pre-Gaussian random matrices

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Sparse disjointed recovery from noninflating measurements

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The Gelfand widths of $\ell_p$-balls for $0 < p \leq 1$

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A mathematical introduction to compressive sensing

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Q3715582

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Lower bounds for constant weight codes

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Recovering Low-Rank Matrices From Few Coefficients in Any Basis

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Sobolev duals for random frames and $\varSigma \varDelta $ quantization of compressed sensing measurements

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Unbalanced expanders and randomness extractors from Parvaresh--Vardy codes

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Robust sparse phase retrieval made easy

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DIAMETERS OF SOME FINITE-DIMENSIONAL SETS AND CLASSES OF SMOOTH FUNCTIONS

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One-Bit Compressive Sensing With Norm Estimation

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Q4868577

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Sparse recovery under weak moment assumptions

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Sparse Signal Recovery from Quadratic Measurements via Convex Programming

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How neighborly can a centrally symmetric polytope be?

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Q4320142

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The geometry of random $\{-1,1\}$-polytopes

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Hardness vs randomness

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Simultaneously Structured Models With Application to Sparse and Low-Rank Matrices

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Q3344608

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One-Bit Compressed Sensing by Linear Programming

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Robust 1-bit Compressed Sensing and Sparse Logistic Regression: A Convex Programming Approach

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A Simpler Approach to Matrix Completion

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Two observations regarding embedding subsets of Euclidean spaces in normed spaces

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Selecting a proportion of characters

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Greedy Approximation

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The Computational Complexity of the Restricted Isometry Property, the Nullspace Property, and Related Concepts in Compressed Sensing

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Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit

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Widths of embeddings in function spaces

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Sparse Recovery With Orthogonal Matching Pursuit Under RIP

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Identifiers

zbMATH Open document ID

1391.94211

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DOI

10.1007/978-3-319-59912-0_4

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Mathematics Subject Classification ID

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Sitelinks

Mathematics(1 entry)

mardi Publication:4609797