
Theory and Computation of Complex Tensors and its Applications
Springer (Publisher)
Published on 2. April 2020
Book
Hardback
XII, 250 pages
978-981-15-2058-7 (ISBN)
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.
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.
More details
Edition
2020 ed.
Language
English
Place of publication
Singapore
Singapore
Target group
Professional and scholarly
Illustrations
26 s/w Abbildungen, 22 farbige Abbildungen
XII, 250 p. 48 illus., 22 illus. in color.
Dimensions
Height: 241 mm
Width: 160 mm
Thickness: 20 mm
Weight
565 gr
ISBN-13
978-981-15-2058-7 (9789811520587)
DOI
10.1007/978-981-15-2059-4
Schweitzer Classification
Other editions
Additional editions

Maolin Che | Yimin Wei
Theory and Computation of Complex Tensors and its Applications
Book
04/2021
Springer
€128.39
Shipment within 3-4 weeks

Maolin Che | Yimin Wei
Theory and Computation of Complex Tensors and its Applications
E-Book
04/2020
1st Edition
Springer
€128.39
Available for download
Content
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.