Matrices

Author: Shmuel Friedland
Publisher: World Scientific
ISBN: 9814667986
Size: 24.49 MB
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' This volume deals with advanced topics in matrix theory using the notions and tools from algebra, analysis, geometry and numerical analysis. It consists of seven chapters that are loosely connected and interdependent. The choice of the topics is very personal and reflects the subjects that the author was actively working on in the last 40 years. Many results appear for the first time in the volume. Readers will encounter various properties of matrices with entries in integral domains, canonical forms for similarity, and notions of analytic, pointwise and rational similarity of matrices with entries which are locally analytic functions in one variable. This volume is also devoted to various properties of operators in inner product space, with tensor products and other concepts in multilinear algebra, and the theory of non-negative matrices. It will be of great use to graduate students and researchers working in pure and applied mathematics, bioinformatics, computer science, engineering, operations research, physics and statistics. Contents:Domains, Modules and MatricesCanonical Forms for SimilarityFunctions of Matrices and Analytic SimilarityInner Product SpacesElements of Multilinear AlgebraNon-Negative MatricesVarious Topics Readership: Graduate students, researchers in mathematics, applied mathematics, statistics, computer science, bioinformatics, engineering, and physics. Key Features:Includes a number of selected related topics in matrix theory that the author was actively working on for 40 yearsIncludes many results that are not available in the books that are currently on the marketKeywords:Analytic Similarity of Matrices;Application to Cellular Communication;Companion Matrix;Cones;Convexity;CUR-Approximation;Determinants;Equivalence of Matrices;Functions of Matrices;Graphs;Inequalities;Inner Product Spaces;Inverse Eigenvalue Problems;Low Rank Approximation;Matrix Exponents;Max-Min Characterization of Eigenvalues;Majorization;Markov Chains;Max-Min Characterization of Eigenvalues;Moore–Penrose Inverse;Normal Forms of Matrices;Norms;Pencils of Matrices;Perturbations;Positive Definite Operators and Matrices;Property L;Perron–Frobenius Theorem;Rellich''s Theorem;Singular Value Decomposition;Sparse Bases;Spectral Functions;Strict Similarity of Pencils;Symmetric and Hermitian Forms;Tensor Products "People who do, or who plan to do, research in the topics in linear algebra that are covered here, will undoubtedly find this to be a very valuable book." Mathematical Association of America '

Matrix Analysis And Applications

Author: Xian-Da Zhang
Publisher: Cambridge University Press
ISBN: 1108417418
Size: 68.63 MB
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The theory, methods and applications of matrix analysis are presented here in a novel theoretical framework.

Introduction To Matrix Analysis And Applications

Author: Fumio Hiai
Publisher: Springer Science & Business Media
ISBN: 3319041509
Size: 65.77 MB
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Matrices can be studied in different ways. They are a linear algebraic structure and have a topological/analytical aspect (for example, the normed space of matrices) and they also carry an order structure that is induced by positive semidefinite matrices. The interplay of these closely related structures is an essential feature of matrix analysis. This book explains these aspects of matrix analysis from a functional analysis point of view. After an introduction to matrices and functional analysis, it covers more advanced topics such as matrix monotone functions, matrix means, majorization and entropies. Several applications to quantum information are also included. Introduction to Matrix Analysis and Applications is appropriate for an advanced graduate course on matrix analysis, particularly aimed at studying quantum information. It can also be used as a reference for researchers in quantum information, statistics, engineering and economics.

Fundamentals Of Matrix Analysis With Applications

Author: Edward Barry Saff
Publisher: John Wiley & Sons
ISBN: 111895369X
Size: 53.30 MB
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An accessible and clear introduction to linear algebra with a focus on matrices and engineering applications Providing comprehensive coverage of matrix theory from a geometric and physical perspective, Fundamentals of Matrix Analysis with Applications describes the functionality of matrices and their ability to quantify and analyze many practical applications. Written by a highly qualified author team, the book presents tools for matrix analysis and is illustrated with extensive examples and software implementations. Beginning with a detailed exposition and review of the Gauss elimination method, the authors maintain readers’ interest with refreshing discussions regarding the issues of operation counts, computer speed and precision, complex arithmetic formulations, parameterization of solutions, and the logical traps that dictate strict adherence to Gauss’s instructions. The book heralds matrix formulation both as notational shorthand and as a quantifier of physical operations such as rotations, projections, reflections, and the Gauss reductions. Inverses and eigenvectors are visualized first in an operator context before being addressed computationally. Least squares theory is expounded in all its manifestations including optimization, orthogonality, computational accuracy, and even function theory. Fundamentals of Matrix Analysis with Applications also features: Novel approaches employed to explicate the QR, singular value, Schur, and Jordan decompositions and their applications Coverage of the role of the matrix exponential in the solution of linear systems of differential equations with constant coefficients Chapter-by-chapter summaries, review problems, technical writing exercises, select solutions, and group projects to aid comprehension of the presented concepts Fundamentals of Matrix Analysis with Applications is an excellent textbook for undergraduate courses in linear algebra and matrix theory for students majoring in mathematics, engineering, and science. The book is also an accessible go-to reference for readers seeking clarification of the fine points of kinematics, circuit theory, control theory, computational statistics, and numerical algorithms.

Matrices

Author: Denis Serre
Publisher: Springer Science & Business Media
ISBN: 9781441976833
Size: 59.49 MB
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In this book, Denis Serre begins by providing a clean and concise introduction to the basic theory of matrices. He then goes on to give many interesting applications of matrices to different aspects of mathematics and also other areas of science and engineering. With forty percent new material, this second edition is significantly different from the first edition. Newly added topics include: • Dunford decomposition, • tensor and exterior calculus, polynomial identities, • regularity of eigenvalues for complex matrices, • functional calculus and the Dunford–Taylor formula, • numerical range, • Weyl's and von Neumann’s inequalities, and • Jacobi method with random choice. The book mixes together algebra, analysis, complexity theory and numerical analysis. As such, this book will provide many scientists, not just mathematicians, with a useful and reliable reference. It is intended for advanced undergraduate and graduate students with either applied or theoretical goals. This book is based on a course given by the author at the École Normale Supérieure de Lyon.

Matrix Algebra

Author: James E. Gentle
Publisher: Springer
ISBN: 3319648675
Size: 23.43 MB
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Matrix algebra is one of the most important areas of mathematics for data analysis and for statistical theory. This much-needed work presents the relevant aspects of the theory of matrix algebra for applications in statistics. It moves on to consider the various types of matrices encountered in statistics, such as projection matrices and positive definite matrices, and describes the special properties of those matrices. Finally, it covers numerical linear algebra, beginning with a discussion of the basics of numerical computations, and following up with accurate and efficient algorithms for factoring matrices, solving linear systems of equations, and extracting eigenvalues and eigenvectors.

Applied Linear Algebra And Matrix Analysis

Author: Thomas S. Shores
Publisher: Springer
ISBN: 3319747487
Size: 70.78 MB
Format: PDF, Docs
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This new book offers a fresh approach to matrix and linear algebra by providing a balanced blend of applications, theory, and computation, while highlighting their interdependence. Intended for a one-semester course, Applied Linear Algebra and Matrix Analysis places special emphasis on linear algebra as an experimental science, with numerous examples, computer exercises, and projects. While the flavor is heavily computational and experimental, the text is independent of specific hardware or software platforms. Throughout the book, significant motivating examples are woven into the text, and each section ends with a set of exercises.

Applications Of The Theory Of Matrices

Author: F. R. Gantmacher
Publisher: Courier Corporation
ISBN: 0486445542
Size: 22.94 MB
Format: PDF
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The breadth of matrix theory's applications is reflected by this volume, which features material of interest to applied mathematicians as well as to control engineers studying stability of a servo-mechanism and numerical analysts evaluating the roots of a polynomial. Starting with a survey of complex symmetric, antisymmetric, and orthogonal matrices, the text advances to explorations of singular bundles of matrices and matrices with nonnegative elements. Applied mathematicians will take particular note of the full and readable chapter on applications of matrix theory to the study of systems of linear differential equations, and the text concludes with an exposition on the Routh-Hurwitz problem plus several helpful appendixes. 1959 edition.

Linear Algebra And Matrix Analysis For Statistics

Author: Sudipto Banerjee
Publisher: CRC Press
ISBN: 1420095382
Size: 74.83 MB
Format: PDF
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Linear Algebra and Matrix Analysis for Statistics offers a gradual exposition to linear algebra without sacrificing the rigor of the subject. It presents both the vector space approach and the canonical forms in matrix theory. The book is as self-contained as possible, assuming no prior knowledge of linear algebra. The authors first address the rudimentary mechanics of linear systems using Gaussian elimination and the resulting decompositions. They introduce Euclidean vector spaces using less abstract concepts and make connections to systems of linear equations wherever possible. After illustrating the importance of the rank of a matrix, they discuss complementary subspaces, oblique projectors, orthogonality, orthogonal projections and projectors, and orthogonal reduction. The text then shows how the theoretical concepts developed are handy in analyzing solutions for linear systems. The authors also explain how determinants are useful for characterizing and deriving properties concerning matrices and linear systems. They then cover eigenvalues, eigenvectors, singular value decomposition, Jordan decomposition (including a proof), quadratic forms, and Kronecker and Hadamard products. The book concludes with accessible treatments of advanced topics, such as linear iterative systems, convergence of matrices, more general vector spaces, linear transformations, and Hilbert spaces.

Matrix Algebra And Its Applications To Statistics And Econometrics

Author: Calyampudi Radhakrishna Rao
Publisher: World Scientific
ISBN: 9789810232689
Size: 33.36 MB
Format: PDF, ePub
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"I recommend this book for its extensive coverage of topics not easily found elsewhere and for its focus on applications".Zentralblatt MATH"The book is an excellent source on linear algebra, matrix theory and applications in statistics and econometrics, and is unique in many ways. I recommend it to anyone interested in these disciplines, and especially in how they benefit from one another".Statistical Papers, 2000