203,79 €
Theory and Computation of Complex Tensors and its Applications
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Theory and Computation of Complex Tensors and its Applications
El. knyga:
203,79 €
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 al…
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- Author: Maolin Che, Yimin Wei
- Publisher: Springer-Verlag GmbH
- Year: 2020
- ISBN: 9789811520594
- ISBN-10: 9811520593
- ISBN-13: 9789811520594
- Format: PDF
- Language: English
Theory and Computation of Complex Tensors and its Applications (e-book) (used book) | bookbook.eu
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Description
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.
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.
- Author: Maolin Che, Yimin Wei
- Publisher: Springer-Verlag GmbH
- Year: 2020
- ISBN: 9789811520594
- ISBN-10: 9811520593
- ISBN-13: 9789811520594
- Format: PDF
- Language: English English
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.
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.
Reviews