- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Erschienen in
-
In: Numerical Algorithms, 93, 2, S. 669-693
- Ereignis
-
Veröffentlichung
- (wo)
-
Augsburg
- (wer)
-
Universität Augsburg
- (wann)
-
2023
- Ereignis
-
Veröffentlichung
- (wo)
-
Berlin
- (wer)
-
Springer Science and Business Media LLC
- (wann)
-
2023
- Urheber
- DOI
-
10.1007/s11075-022-01433-5
- URN
-
urn:nbn:de:bvb:384-opus4-1022036
- Rechteinformation
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
14.08.2025, 10:53 MESZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Eisenmann, Henrik
- Krahmer, Felix
- Pfeffer, Max
- Uschmajew, André
- Universität Augsburg
- Springer Science and Business Media LLC
Entstanden
- 2023
Ähnliche Objekte (12)
Riemannian thresholding methods for row-sparse and low-rank matrix recovery
Newton-Type Optimal Thresholding Algorithms for Sparse Optimization Problems
A Riemannian rank-adaptive method for low-rank matrix completion
A Riemannian rank-adaptive method for low-rank matrix completion
Partial gradient optimal thresholding algorithms for a class of sparse optimization problems
Improved iterative shrinkage-thresholding for sparse signal recovery via Laplace mixtures models
Implicit Low-Rank Riemannian Schemes for the Time Integration of Stiff Partial Differential Equations
Topics in learning sparse and low-rank models of non-negative data
On convergence of iterative thresholding algorithms to approximate sparse solution for composite nonconvex optimization
Hyperspectral anomaly detection via low-rank and sparse decomposition with cluster subspace accumulation
Spectral weighted sparse unmixing based on adaptive total variation and low-rank constraints
Compressive Higher-order Sparse and Low-Rank Acquisition with a Hyperspectral Light Stage
Riemannian thresholding methods for row-sparse and low-rank matrix recovery
Newton-Type Optimal Thresholding Algorithms for Sparse Optimization Problems
A Riemannian rank-adaptive method for low-rank matrix completion
A Riemannian rank-adaptive method for low-rank matrix completion
Partial gradient optimal thresholding algorithms for a class of sparse optimization problems
Improved iterative shrinkage-thresholding for sparse signal recovery via Laplace mixtures models
Implicit Low-Rank Riemannian Schemes for the Time Integration of Stiff Partial Differential Equations
Topics in learning sparse and low-rank models of non-negative data
On convergence of iterative thresholding algorithms to approximate sparse solution for composite nonconvex optimization
Hyperspectral anomaly detection via low-rank and sparse decomposition with cluster subspace accumulation
Spectral weighted sparse unmixing based on adaptive total variation and low-rank constraints
Compressive Higher-order Sparse and Low-Rank Acquisition with a Hyperspectral Light Stage
Riemannian thresholding methods for row-sparse and low-rank matrix recovery
Newton-Type Optimal Thresholding Algorithms for Sparse Optimization Problems
A Riemannian rank-adaptive method for low-rank matrix completion
A Riemannian rank-adaptive method for low-rank matrix completion
Partial gradient optimal thresholding algorithms for a class of sparse optimization problems
Improved iterative shrinkage-thresholding for sparse signal recovery via Laplace mixtures models
Implicit Low-Rank Riemannian Schemes for the Time Integration of Stiff Partial Differential Equations
Topics in learning sparse and low-rank models of non-negative data
On convergence of iterative thresholding algorithms to approximate sparse solution for composite nonconvex optimization
Hyperspectral anomaly detection via low-rank and sparse decomposition with cluster subspace accumulation
Spectral weighted sparse unmixing based on adaptive total variation and low-rank constraints