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Matrix Theory - Classics and Advances examines matrix theory and its application in solving a series of problems related to natural phenomena and applied science. It consists of eleven chapters divided into two sections. Section 1, "Theory and Progress", discusses the classical problems of matrix theory and its contribution to different fields of pure mathematics. Section 2, "Applications", contains the research related to the application of matrix theory in applied science.

Matrices.

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This book addresses both the basic and applied aspects of the finite iterative algorithm, CGLS iterative algorithm, and explicit algorithm to some linear matrix equations. The author presents the latest results in three parts: (1) We consider the finite iterative algorithm to the coupled transpose matrix equations and the coupled operator matrix equations with sub-matrix constrained. These two finite iterative algorithms are closely related and progressive. (2) We present MCGLS iterative algorithm for studying least squares problems to the generalized Sylvester-conjugate matrix equation, the generalized Sylvester-conjugate transpose matrix equation, and the coupled linear operator systems, respectively. (3) Compared with the previous two parts, we consider here the explicit solution to some linear matrix equations, which are the nonhomogeneous Yakubovich matrix equation, the nonhomogeneous Yakubovich transpose matrix equation, and the generalized Sylvester matrix equation, respectively. This book is intended for students, researchers, and professionals in the field of numerical algebra, linear matrix equations, nonlinear matrix equations, and control theory.

MATHEMATICS / Matrices.

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Random matrix theory has a long history, beginning in the first instance in multivariate statistics. It was used by Wigner to supply explanations for the important regularity features of the apparently random dispositions of the energy levels of heavy nuclei. The subject was further deeply developed under the important leadership of Dyson, Gaudin and Mehta, and other mathematical physicists. In the early 1990's, random matrix theory witnessed applications in string theory and deep connections with operator theory, and the integrable systems were established by Tracy and Widom. More recently,

Random matrices.

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The present monograph on matrix partial orders, appearing for the first time, is a unique presentation of many partial orders on matrices that have fascinated mathematicians for their beauty and applied scientists for their wide-ranging application potential. Except for the Lowner order, the partial orders considered are relatively new and came into being in the late 1970's. After a detailed introduction to generalized inverses and decompositions, the three basic partial orders - namely, the minus, the sharp and the star - and the corresponding one-sided orders are presented using various...

Matrices. --- Linear operators.

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This book focuses on the behaviour of large random matrices. Standard results are covered, and the presentation emphasizes elementary operator theory and differential equations, so as to be accessible to graduate students and other non-experts. The introductory chapters review material on Lie groups and probability measures in a style suitable for applications in random matrix theory. Later chapters use modern convexity theory to establish subtle results about the convergence of eigenvalue distributions as the size of the matrices increases. Random matrices are viewed as geometrical objects with large dimension. The book analyzes the concentration of measure phenomenon, which describes how measures behave on geometrical objects with large dimension. To prove such results for random matrices, the book develops the modern theory of optimal transportation and proves the associated functional inequalities involving entropy and information. These include the logarithmic Sobolev inequality, which measures how fast some physical systems converge to equilibrium.

Random matrices --- 512.643 --- 512.643 Matrices and linear mappings. Matrix theory. Determinants. Eigenvalues --- Matrices and linear mappings. Matrix theory. Determinants. Eigenvalues --- Matrices, Random --- Matrices --- Random matrices.