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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.
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.
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