Tensor Analysis

The second volume will deal with a presentation of the main matrix and tensor decompositions and their properties of uniqueness, as well as very useful tensor networks for the analysis of massive data. Parametric estimation algorithms will be presented for the identification of the main tensor decompositions. After a brief historical review of the compressed sampling methods, an overview of the main methods of retrieving matrices and tensors with missing data will be performed under the low rank hypothesis. Illustrative examples will be provided.

Authors: de Souza Sánchez Filho, Emil
Presents concepts in a straightforward way, while maintaining a great level of rigor
Provides 56 solved exercises and a select set of unsolved problems with answers
Presents a didactic and concise text suited to undergraduate and graduate students
Enriches understanding of tensor calculus applied to all technical sciences and engineering disciplines, providing the reader with complete illustrations that supplement the presented concepts