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.
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Buch. Etat : 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. N° de réf. du vendeur 9789811520587
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Gebunden. Etat : 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 . N° de réf. du vendeur 335896052
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Buch. Etat : 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. N° de réf. du vendeur 9789811520587
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