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    Algebraic and Computational Aspects of Real Tensor Ranks

    Posted By: Underaglassmoon
    Algebraic and Computational Aspects of Real Tensor Ranks

    Algebraic and Computational Aspects of Real Tensor Ranks
    Springer | Statistics | April 19, 2016 | ISBN-10: 4431554580 | 108 pages | pdf | 1.66 mb

    Authors: Sakata, Toshio, Sumi, Toshio, Miyazaki, Mitsuhiro
    Presents the first comprehensive treatment of maximal ranks and typical ranks over the real number file
    Provides interesting ideas of determinant polynomials, determinantal ideals, absolutely nonsingular tensors and absolutely full column rank tensors
    Includes numerical methods of determining ranks by simultaneous singular value decomposition through a theory of matrix star algebra


    This book provides comprehensive summaries of theoretical (algebraic) and computational aspects of tensor ranks, maximal ranks, and typical ranks, over the real number field. Although tensor ranks have been often argued in the complex number field, it should be emphasized that this book treats real tensor ranks, which have direct applications in statistics. The book provides several interesting ideas, including determinant polynomials, determinantal ideals, absolutely nonsingular tensors, absolutely full column rank tensors, and their connection to bilinear maps and Hurwitz-Radon numbers. In addition to reviews of methods to determine real tensor ranks in details, global theories such as the Jacobian method are also reviewed in details. The book includes as well an accessible and comprehensive introduction of mathematical backgrounds, with basics of positive polynomials and calculations by using the Groebner basis. Furthermore, this book provides insights into numerical methods of finding tensor ranks through simultaneous singular value decompositions.

    Number of Illustrations and Tables
    5 b/w illustrations
    Topics
    Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
    Statistics and Computing / Statistics Programs
    Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law

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