Theory and Computation of Complex Tensors and its Applications
Autor Maolin Che, Yimin Weien Limba Engleză Paperback – 2 apr 2021
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.
| Toate formatele și edițiile | Preț | Express |
|---|---|---|
| Paperback (1) | 712.89 lei 6-8 săpt. | |
| Springer Nature Singapore – 2 apr 2021 | 712.89 lei 6-8 săpt. | |
| Hardback (1) | 719.50 lei 6-8 săpt. | |
| Springer Nature Singapore – 2 apr 2020 | 719.50 lei 6-8 săpt. |
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Specificații
ISBN-13: 9789811520617
ISBN-10: 9811520615
Ilustrații: XII, 250 p. 48 illus., 22 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.37 kg
Ediția:1st ed. 2020
Editura: Springer Nature Singapore
Colecția Springer
Locul publicării:Singapore, Singapore
ISBN-10: 9811520615
Ilustrații: XII, 250 p. 48 illus., 22 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.37 kg
Ediția:1st ed. 2020
Editura: Springer Nature Singapore
Colecția Springer
Locul publicării:Singapore, Singapore
Cuprins
Preface.- Introduction.- The pseudo-spectrum theory.- Perturbation theory.- Tensor complementarity problems.- Plane stochastic tensors.- Neural Networks.- US- and U-eigenpairs of complex tensors.- Randomized algorithms.- Bibliography.- Index.
Textul de pe ultima copertă
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.
Caracteristici
Introduces the neural network models and Takagi factorization for the computation of tensor rank-one approximations and US- (U-) eigenvalues Enriches the properties of nonnegative tensors, defines the sign nonsingular tensors and derives a probabilistic algorithm for locating a positive diagonal in a nonnegative tensors Gives adaptive randomized algorithms for the computation of the low multilinear rank approximations and the tensor train approximations of the tensors