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Exact Algorithms for $ L ^{ 1 } $ -TV Regularization of Real-Valued or Circle-Valued Signals

Michaël Unser, Martin Kurt Storath

We consider

L ^{ 1 }

-TV regularization of univariate signals with values on the real line or on the unit circle. While the real data space leads to a convex optimization problem, the problem is nonconvex for circle-valued data. In this paper, we deriv ...

SIAM2016

Espace vectoriel

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Nonequilibrium localization and the interplay between disorder and interactions

Spectral methods for tempered fractional differential equations

Reduced Basis Approximation And A Posteriori Error Estimates For Parametrized Elliptic Eigenvalue Problems

Reduced basis method and domain decomposition for elliptic problems in networks and complex parametrized geometries

Logarithmic estimates for continuity equations

MATHICSE Technical Report : Multi space reduced basis preconditioners for large-scale parametrized PDEs

Space lower bounds for low-stretch greedy embeddings

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

Exact Algorithms for $ L ^{ 1 } $ -TV Regularization of Real-Valued or Circle-Valued Signals

Cross-lingual Linking of Multi-word Entities and their corresponding Acronyms

Reduced Basis Approximation And A Posteriori Error Estimates For Parametrized Elliptic Eigenvalue Problems

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

Space lower bounds for low-stretch greedy embeddings

Exact Algorithms for $ L ^{ 1 } $ -TV Regularization of Real-Valued or Circle-Valued Signals

Logarithmic estimates for continuity equations

Cross-lingual Linking of Multi-word Entities and their corresponding Acronyms

Spectral methods for tempered fractional differential equations

Reduced basis method and domain decomposition for elliptic problems in networks and complex parametrized geometries

Nonequilibrium localization and the interplay between disorder and interactions

MATHICSE Technical Report : Multi space reduced basis preconditioners for large-scale parametrized PDEs