Compressed Sensing with Sparse Corruptions: Fault-Tolerant Sparse Collocation Approximations (Q4611522)

scientific article; zbMATH DE number 7003641

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English	Compressed Sensing with Sparse Corruptions: Fault-Tolerant Sparse Collocation Approximations	scientific article; zbMATH DE number 7003641

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instance of

scholarly article

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title

Compressed Sensing with Sparse Corruptions: Fault-Tolerant Sparse Collocation Approximations (English)

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zbMATH Open document ID

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SIAM/ASA Journal on Uncertainty Quantification

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publication date

21 January 2019

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full work available at URL

https://arxiv.org/abs/1703.00135

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Mathematics Subject Classification ID

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compressed sensing

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corrupted measurements

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fault tolerance

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describes a project that uses

SPGL1

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TESTPACK

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MaRDI profile type

MaRDI publication profile

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arXiv ID

1703.00135

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cites work

Infinite-dimensional compressed sensing and function interpolation

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Infinite-dimensional $\ell ^1$ minimization and function approximation from pointwise data

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BREAKING THE COHERENCE BARRIER: A NEW THEORY FOR COMPRESSED SENSING

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The Quest for Optimal Sampling: Computationally Efficient, Structure-Exploiting Measurements for Compressed Sensing

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On the Absence of Uniform Recovery in Many Real-World Applications of Compressed Sensing and the Restricted Isometry Property and Nullspace Property in Levels

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Enhancing sparsity by reweighted $\ell _{1}$ minimization

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

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Compressed Sensing and Parallel Acquisition

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A non-adapted sparse approximation of PDEs with stochastic inputs

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Refined analysis of sparse MIMO radar

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A mathematical introduction to compressive sensing

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Q3997987

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Stochastic Collocation Methods via $\ell_1$ Minimization Using Randomized Quadratures

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Compressive sampling of polynomial chaos expansions: convergence analysis and sampling strategies

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Enhancing $\ell_1$-minimization estimates of polynomial chaos expansions using basis selection

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A Generalized Sampling and Preconditioning Scheme for Sparse Approximation of Polynomial Chaos Expansions

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Compressed sensing with local structure: uniform recovery guarantees for the sparsity in levels class

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Compressed sensing and matrix completion with constant proportion of corruptions

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Stochastic Collocation on Unstructured Multivariate Meshes

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Exact Recoverability From Dense Corrupted Observations via $\ell _{1}$-Minimization

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A weighted $\ell_1$-minimization approach for sparse polynomial chaos expansions

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Sparse Legendre expansions via $\ell_1$-minimization

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Interpolation via weighted $\ell_{1}$ minimization

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Correcting Data Corruption Errors for Multivariate Function Approximation

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Recovery of Sparsely Corrupted Signals

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Probing the Pareto Frontier for Basis Pursuit Solutions

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Dense Error Correction Via $\ell^1$-Minimization

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The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations

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STOCHASTIC COLLOCATION ALGORITHMS USING l1-MINIMIZATION

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Alternating Direction Algorithms for $\ell_1$-Problems in Compressive Sensing

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Reweighted $\ell_1$ minimization method for stochastic elliptic differential equations

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Minimization of $\ell_{1-2}$ for Compressed Sensing

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Sitelinks

Mathematics(1 entry)

mardi Publication:4611522