Hugendubel.info - Die B2B Online-Buchhandlung 

Merkliste
Die Merkliste ist leer.
Bitte warten - die Druckansicht der Seite wird vorbereitet.
Der Druckdialog öffnet sich, sobald die Seite vollständig geladen wurde.
Sollte die Druckvorschau unvollständig sein, bitte schliessen und "Erneut drucken" wählen.

Tensor-Based Dynamical Systems

Theory and Applications
BuchGebunden
106 Seiten
Englisch
Springererschienen am05.03.20242024
This book provides a comprehensive review on tensor algebra, including tensor products, tensor unfolding, tensor eigenvalues, and tensor decompositions.mehr
Verfügbare Formate
BuchGebunden
EUR85,59
E-BookPDF1 - PDF WatermarkE-Book
EUR85,59

Produkt

KlappentextThis book provides a comprehensive review on tensor algebra, including tensor products, tensor unfolding, tensor eigenvalues, and tensor decompositions.
Details
ISBN/GTIN978-3-031-54504-7
ProduktartBuch
EinbandartGebunden
Verlag
Erscheinungsjahr2024
Erscheinungsdatum05.03.2024
Auflage2024
ReiheSynthesis Lectures on Mathematics & Statistics
Seiten106 Seiten
SpracheEnglisch
Gewicht335 g
IllustrationenXV, 106 p. 19 illus., 17 illus. in color.
Artikel-Nr.55856609

Inhalt/Kritik

Inhaltsverzeichnis
Tensors and Tensor Algebra.- Tucker Product Representation.- Einstein Product Representation.- CP/tensor Train Decomposition Representation.- Tensor Vector Product Representation.- Contract Product Representation.- T-product Representation.mehr

Schlagworte

Autor

Can Chen, Ph.D. is an Assistant Professor in the School of Data Science and Society with a second appointment in the Department of Mathematics at the University of North Carolina at Chapel Hill. He received the B.S. degree in Mathematics from the University of California, Irvine in 2016, and the M.S. degree in Electrical and Computer Engineering and the Ph.D. degree in Applied and Interdisciplinary Mathematics from the University of Michigan in 2020 and 2021, respectively. He was a Postdoctoral Research Fellow in the Channing Division of Network Medicine at Brigham and Women's Hospital and Harvard Medical School from 2021 to 2023. His research interests span a diverse range of fields, including control theory, network science, tensor algebra, numerical analysis, data science, machine learning, deep learning, hypergraph learning, data analysis, and computational biology.