Librería: Ria Christie Collections, Uxbridge, Reino Unido
EUR 127,96
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Añadir al carritoCondición: New. In.
Librería: Books Puddle, New York, NY, Estados Unidos de America
EUR 161,97
Cantidad disponible: 4 disponibles
Añadir al carritoCondición: New.
Librería: Revaluation Books, Exeter, Reino Unido
EUR 181,10
Cantidad disponible: 2 disponibles
Añadir al carritoHardcover. Condición: Brand New. 262 pages. 9.25x6.10x0.71 inches. In Stock.
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 136,10
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Añadir al carritoBuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - The book provides an introduction of very recent results about the tensors and mainly focuses on the authors' work and perspective. A systematic description about how to extend the numerical linear algebra to the numerical multi-linear algebra is also delivered in this book. The authors design the neural network model for the computation of the rank-one approximation of real tensors, a normalization algorithm to convert some nonnegative tensors to plane stochastic tensors and a probabilistic algorithm for locating a positive diagonal in a nonnegative tensors,adaptive randomized algorithms for computing the approximate tensor decompositions, andthe QR type method for computing U-eigenpairs of complex tensors.This book could be used for the Graduate course, such as Introduction to Tensor. Researchers may also find it helpful as a reference in tensor research.
Librería: Brook Bookstore On Demand, Napoli, NA, Italia
EUR 102,25
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Añadir al carritoCondición: new. Questo è un articolo print on demand.
Idioma: Inglés
Publicado por Springer Nature Singapore Apr 2020, 2020
ISBN 10: 9811520585 ISBN 13: 9789811520587
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
EUR 128,39
Cantidad disponible: 2 disponibles
Añadir al carritoBuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The book provides an introduction of very recent results about the tensors and mainly focuses on the authors' work and perspective. A systematic description about how to extend the numerical linear algebra to the numerical multi-linear algebra is also delivered in this book. The authors design the neural network model for the computation of the rank-one approximation of real tensors, a normalization algorithm to convert some nonnegative tensors to plane stochastic tensors and a probabilistic algorithm for locating a positive diagonal in a nonnegative tensors,adaptive randomized algorithms for computing the approximate tensor decompositions, andthe QR type method for computing U-eigenpairs of complex tensors.This book could be used for the Graduate course, such as Introduction to Tensor. Researchers may also find it helpful as a reference in tensor research. 264 pp. Englisch.
Librería: moluna, Greven, Alemania
EUR 109,83
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Añadir al carritoGebunden. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Introduces the neural network models and Takagi factorization for the computation of tensor rank-one approximations and US- (U-) eigenvaluesEnriches the properties of nonnegative tensors, defines the sign nonsingular tensors and derives .
Librería: Majestic Books, Hounslow, Reino Unido
EUR 167,96
Cantidad disponible: 4 disponibles
Añadir al carritoCondición: New. Print on Demand.
Librería: Biblios, Frankfurt am main, HESSE, Alemania
EUR 168,17
Cantidad disponible: 4 disponibles
Añadir al carritoCondición: New. PRINT ON DEMAND.
Idioma: Inglés
Publicado por Springer, Springer Apr 2020, 2020
ISBN 10: 9811520585 ISBN 13: 9789811520587
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
EUR 128,39
Cantidad disponible: 1 disponibles
Añadir al carritoBuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The book provides an introduction of very recent results about the tensors and mainly focuses on the authors' work and perspective. A systematic description about how to extend the numerical linear algebra to the numerical multi-linear algebra is also delivered in this book. The authors design the neural network model for the computation of the rank-one approximation of real tensors, a normalization algorithm to convert some nonnegative tensors to plane stochastic tensors and a probabilistic algorithm for locating a positive diagonal in a nonnegative tensors, adaptive randomized algorithms for computing the approximate tensor decompositions, and the QR type method for computing U-eigenpairs of complex tensors.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 264 pp. Englisch.