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Matrix Methods Theory Algorithms And Applications
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Download or read book Matrix Methods written by Vadim Olshevsky and published by World Scientific. This book was released on 2010 with total page 604 pages. Available in PDF, EPUB and Kindle. Book excerpt: Matrix methods provide the key to many problems in pure and applied mathematics. However, linear algebra theory, numerical algorithms and matrices in FEM/BEM applications usually live as if in three separate worlds. In this volume, maybe for the first time ever, they are compiled together as one entity as it was at the Moscow meeting, where the algebraic part was impersonated by Hans Schneider, algorithms by Gene Golub, and applications by Guri Marchuk. All topics intervened in plenary sessions are specially categorized into three sections of this volume. --
Download or read book Matrix Methods written by Vadim Olshevsky and published by World Scientific. This book was released on 2010 with total page 604 pages. Available in PDF, EPUB and Kindle. Book excerpt: Operators preserving primitivity for matrix pairs / L.B. Beasley, A.E. Guterman -- Decompositions of quaternions and their matrix equivalents / D. Janovská, G. Opfer -- Sensitivity analysis of Hamiltonian and reversible systems prone to dissipation-induced instabilities / O.N. Kirillov -- Block triangular miniversal deformations of matrices and matrix pencils / L. Klimenko, V.V. Sergeichuk -- Determining the Schein rank of boolean matrices / E.E. Marenich -- Lattices of matrix rows and matrix columns. Lattices of invariant column eigenvectors / V. Marenich -- Matrix algebras and their length / O.V. Markova -- On a new class of singular nonsymmetric matrices with nonnegative integer spectra / T. Nahtman, D. von Rosen -- Reduction of a set of matrices over a principal ideal domain to the Smith normal forms by means of the same one-sided transformation / V.M. Prokip -- Nonsymmetric algebraic Riccati equations associated with an M-matrix : recent advances and algorithms / D.A. Bini, B. Iannazzo, B. Meini, F. Poloni -- A generalized conjugate direction method for nonsymmetric large ill-conditioned linear systems / E.R. Boudinov, A.I. Manevich -- There exist normal Hankel ([symbol], [symbol])-circulants of any order [symbol] / V.N. Chugunov, Kh. D. Ikramov -- On the treatment of boundary artifacts in image restoration by reflection and/or anti-reflection / M. Donatelli, S. Serra-Capizzano -- Zeros of determinants of [symbol]-matrices / W. Gander -- How to find a good submatrix / S.A. Goreinov [und weiteren] -- Conjugate and semi-conjugate direction methods with preconditioning projectors / V.P. Il'in -- Some relationships between optimal preconditioner and superoptimal preconditioner / J.-B. Chen [und weiteren] -- Scaling, preconditioning, and superlinear convergence in GMRES-type iterations / I. Kaporin -- Toeplitz and Toeplitz-block-Toeplitz matrices and their correlation with syzygies of polynomials / H. Khalil, B. Mourrain, M. Schatzman -- Concepts of data-sparse tensor-product approximation in many-particle modelling / H.-J. Flad [und weiteren] -- Separation of variables in nonlinear fermi equation / Yu. I. Kuznetsov -- Faster multipoint polynomial evaluation via structured matrices / B. Murphy, R.E. Rosholt -- Testing pivoting policies in Gaussian elimination / B. Murphy [und weiteren] -- Newton's iteration for matrix inversion, advances and extensions / V.Y. Pan -- Truncated decompositions and filtering methods with reflective/antireflective boundary conditions : a comparison / C. Tablino Possio -- Discrete-time stability of a class of hermitian polynomial matrices with positive semidefinite coefficients / H.K. Wimmer -- Splitting algorithm for solving mixed variational inequalities with inversely strongly monotone operators / I. Badriev, O. Zadvornov -- Multilevel algorithm for graph partitioning / N.S. Bochkarev, O.V. Diyankov, V.Y. Pravilnikov -- 2D-extension of singular spectrum analysis : algorithm and elements of theory / N.E. Golyandina, K.D. Usevich -- Application of radon transform for fast solution of boundary value problems for elliptic PDE in domains with complicated geometry / A.I. Grebennikov -- Application of a multigrid method to solving diffusion-type equations / M.E. Ladonkina, O. Yu. Milukova, V.F. Tishkin -- Monotone matrices and finite volume schemes for diffusion problems preserving non-negativity of solution / I.V. Kapyrin -- Sparse approximation of FEM matrix for sheet current integro-differential equation / M. Khapaev, M. Yu. Kupriyanov -- The method of magnetic field computation in presence of an ideal conductive multiconnected surface by using the integro-differential equation of the first kind / T. Kochubey, V.I. Astakhov -- Spectral model order reduction preserving passivity for large multiport RCLM networks / Yu. M. Nechepurenko, A.S. Potyagalova, I.A. Karaseva -- New smoothers in multigrid methods for strongly nonsymmetric linear systems / G.V. Muratova, E.M. Andreeva -- Operator equations for eddy currents on singular carriers / J. Naumenko -- Matrix approach to modelling of polarized radiation transfer in heterogeneous systems / T.A. Sushkevich, S.A. Strelkov, S.V. Maksakova -- The Method of Regularization of Tikhonov Based on Augmented Systems / A.I. Zhdanov, T.G. Parchaikina
Book Synopsis Matrix Methods in Data Mining and Pattern Recognition by : Lars Elden
Download or read book Matrix Methods in Data Mining and Pattern Recognition written by Lars Elden and published by SIAM. This book was released on 2007-07-12 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: Several very powerful numerical linear algebra techniques are available for solving problems in data mining and pattern recognition. This application-oriented book describes how modern matrix methods can be used to solve these problems, gives an introduction to matrix theory and decompositions, and provides students with a set of tools that can be modified for a particular application.Matrix Methods in Data Mining and Pattern Recognition is divided into three parts. Part I gives a short introduction to a few application areas before presenting linear algebra concepts and matrix decompositions that students can use in problem-solving environments such as MATLAB®. Some mathematical proofs that emphasize the existence and properties of the matrix decompositions are included. In Part II, linear algebra techniques are applied to data mining problems. Part III is a brief introduction to eigenvalue and singular value algorithms. The applications discussed by the author are: classification of handwritten digits, text mining, text summarization, pagerank computations related to the GoogleÔ search engine, and face recognition. Exercises and computer assignments are available on a Web page that supplements the book.Audience The book is intended for undergraduate students who have previously taken an introductory scientific computing/numerical analysis course. Graduate students in various data mining and pattern recognition areas who need an introduction to linear algebra techniques will also find the book useful.Contents Preface; Part I: Linear Algebra Concepts and Matrix Decompositions. Chapter 1: Vectors and Matrices in Data Mining and Pattern Recognition; Chapter 2: Vectors and Matrices; Chapter 3: Linear Systems and Least Squares; Chapter 4: Orthogonality; Chapter 5: QR Decomposition; Chapter 6: Singular Value Decomposition; Chapter 7: Reduced-Rank Least Squares Models; Chapter 8: Tensor Decomposition; Chapter 9: Clustering and Nonnegative Matrix Factorization; Part II: Data Mining Applications. Chapter 10: Classification of Handwritten Digits; Chapter 11: Text Mining; Chapter 12: Page Ranking for a Web Search Engine; Chapter 13: Automatic Key Word and Key Sentence Extraction; Chapter 14: Face Recognition Using Tensor SVD. Part III: Computing the Matrix Decompositions. Chapter 15: Computing Eigenvalues and Singular Values; Bibliography; Index.
Book Synopsis Matrix Methods: Theory, Algorithms and Applications by :
Download or read book Matrix Methods: Theory, Algorithms and Applications written by and published by . This book was released on with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Design Structure Matrix Methods and Applications by : Steven D. Eppinger
Download or read book Design Structure Matrix Methods and Applications written by Steven D. Eppinger and published by MIT Press. This book was released on 2012-05-25 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to a powerful and flexible network modeling tool for developing and understanding complex systems, with many examples from a range of industries. Design structure matrix (DSM) is a straightforward and flexible modeling technique that can be used for designing, developing, and managing complex systems. DSM offers network modeling tools that represent the elements of a system and their interactions, thereby highlighting the system's architecture (or designed structure). Its advantages include compact format, visual nature, intuitive representation, powerful analytical capacity, and flexibility. Used primarily so far in the area of engineering management, DSM is increasingly being applied to complex issues in health care management, financial systems, public policy, natural sciences, and social systems. This book offers a clear and concise explanation of DSM methods for practitioners and researchers.
Book Synopsis Functions of Matrices by : Nicholas J. Higham
Download or read book Functions of Matrices written by Nicholas J. Higham and published by SIAM. This book was released on 2008-01-01 with total page 445 pages. Available in PDF, EPUB and Kindle. Book excerpt: A thorough and elegant treatment of the theory of matrix functions and numerical methods for computing them, including an overview of applications, new and unpublished research results, and improved algorithms. Key features include a detailed treatment of the matrix sign function and matrix roots; a development of the theory of conditioning and properties of the Fre;chet derivative; Schur decomposition; block Parlett recurrence; a thorough analysis of the accuracy, stability, and computational cost of numerical methods; general results on convergence and stability of matrix iterations; and a chapter devoted to the f(A)b problem. Ideal for advanced courses and for self-study, its broad content, references and appendix also make this book a convenient general reference. Contains an extensive collection of problems with solutions and MATLAB implementations of key algorithms.
Download or read book Matrix Algebra written by James E. Gentle and published by Springer Science & Business Media. This book was released on 2007-07-27 with total page 536 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Matrix Methods in Data Mining and Pattern Recognition, Second Edition by : Lars Elden
Download or read book Matrix Methods in Data Mining and Pattern Recognition, Second Edition written by Lars Elden and published by SIAM. This book was released on 2019-08-30 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thoroughly revised second edition provides an updated treatment of numerical linear algebra techniques for solving problems in data mining and pattern recognition. Adopting an application-oriented approach, the author introduces matrix theory and decompositions, describes how modern matrix methods can be applied in real life scenarios, and provides a set of tools that students can modify for a particular application. Building on material from the first edition, the author discusses basic graph concepts and their matrix counterparts. He introduces the graph Laplacian and properties of its eigenvectors needed in spectral partitioning and describes spectral graph partitioning applied to social networks and text classification. Examples are included to help readers visualize the results. This new edition also presents matrix-based methods that underlie many of the algorithms used for big data. The book provides a solid foundation to further explore related topics and presents applications such as classification of handwritten digits, text mining, text summarization, PageRank computations related to the Google search engine, and facial recognition. Exercises and computer assignments are available on a Web page that supplements the book. This book is primarily for undergraduate students who have previously taken an introductory scientific computing/numerical analysis course and graduate students in data mining and pattern recognition areas who need an introduction to linear algebra techniques.
Book Synopsis Spectral Algorithms by : Ravindran Kannan
Download or read book Spectral Algorithms written by Ravindran Kannan and published by Now Publishers Inc. This book was released on 2009 with total page 153 pages. Available in PDF, EPUB and Kindle. Book excerpt: Spectral methods refer to the use of eigenvalues, eigenvectors, singular values and singular vectors. They are widely used in Engineering, Applied Mathematics and Statistics. More recently, spectral methods have found numerous applications in Computer Science to "discrete" as well as "continuous" problems. Spectral Algorithms describes modern applications of spectral methods, and novel algorithms for estimating spectral parameters. The first part of the book presents applications of spectral methods to problems from a variety of topics including combinatorial optimization, learning and clustering. The second part of the book is motivated by efficiency considerations. A feature of many modern applications is the massive amount of input data. While sophisticated algorithms for matrix computations have been developed over a century, a more recent development is algorithms based on "sampling on the fly" from massive matrices. Good estimates of singular values and low rank approximations of the whole matrix can be provably derived from a sample. The main emphasis in the second part of the book is to present these sampling methods with rigorous error bounds. It also presents recent extensions of spectral methods from matrices to tensors and their applications to some combinatorial optimization problems.
Book Synopsis Hierarchical Matrices: Algorithms and Analysis by : Wolfgang Hackbusch
Download or read book Hierarchical Matrices: Algorithms and Analysis written by Wolfgang Hackbusch and published by Springer. This book was released on 2015-12-21 with total page 511 pages. Available in PDF, EPUB and Kindle. Book excerpt: This self-contained monograph presents matrix algorithms and their analysis. The new technique enables not only the solution of linear systems but also the approximation of matrix functions, e.g., the matrix exponential. Other applications include the solution of matrix equations, e.g., the Lyapunov or Riccati equation. The required mathematical background can be found in the appendix. The numerical treatment of fully populated large-scale matrices is usually rather costly. However, the technique of hierarchical matrices makes it possible to store matrices and to perform matrix operations approximately with almost linear cost and a controllable degree of approximation error. For important classes of matrices, the computational cost increases only logarithmically with the approximation error. The operations provided include the matrix inversion and LU decomposition. Since large-scale linear algebra problems are standard in scientific computing, the subject of hierarchical matrices is of interest to scientists in computational mathematics, physics, chemistry and engineering.
Book Synopsis Exploiting Hidden Structure in Matrix Computations: Algorithms and Applications by : Michele Benzi
Download or read book Exploiting Hidden Structure in Matrix Computations: Algorithms and Applications written by Michele Benzi and published by Springer. This book was released on 2017-01-24 with total page 406 pages. Available in PDF, EPUB and Kindle. Book excerpt: Focusing on special matrices and matrices which are in some sense `near’ to structured matrices, this volume covers a broad range of topics of current interest in numerical linear algebra. Exploitation of these less obvious structural properties can be of great importance in the design of efficient numerical methods, for example algorithms for matrices with low-rank block structure, matrices with decay, and structured tensor computations. Applications range from quantum chemistry to queuing theory. Structured matrices arise frequently in applications. Examples include banded and sparse matrices, Toeplitz-type matrices, and matrices with semi-separable or quasi-separable structure, as well as Hamiltonian and symplectic matrices. The associated literature is enormous, and many efficient algorithms have been developed for solving problems involving such matrices. The text arose from a C.I.M.E. course held in Cetraro (Italy) in June 2015 which aimed to present this fast growing field to young researchers, exploiting the expertise of five leading lecturers with different theoretical and application perspectives.
Book Synopsis Iterative Methods for Sparse Linear Systems by : Yousef Saad
Download or read book Iterative Methods for Sparse Linear Systems written by Yousef Saad and published by SIAM. This book was released on 2003-04-01 with total page 537 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mathematics of Computing -- General.
Download or read book Matrix Algebra written by James E. Gentle and published by Springer Nature. This book was released on with total page 714 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Introduction to Matrix Analytic Methods in Stochastic Modeling by : G. Latouche
Download or read book Introduction to Matrix Analytic Methods in Stochastic Modeling written by G. Latouche and published by SIAM. This book was released on 1999-01-01 with total page 331 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presents the basic mathematical ideas and algorithms of the matrix analytic theory in a readable, up-to-date, and comprehensive manner.
Download or read book Matrices written by Stephen Barnett and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Techniques of matrix theory find wide application throughout engineering and the physical, life, and social sciences. Consequently, matrix methods comprise an important component in any `tool kit' of applied mathematics. This wide-ranging textbook provides a clearly written and up-to-date account of these methods, suitable for both undergraduates and more advanced students. The aim is to provide a down-to-earth approach with results illustrated by many examples drawn from the areas of application. The range of topics covered is large: from basic matrix algebra to advanced concepts such as generalized inverses and Hadamard matrices, and applications to error-correcting codes, control theory, and linear programming. In addition, the book contains numerous exercises, together with answers, making it ideal for students in any field where matrices are used.
Book Synopsis A Matrix Algebra Approach to Artificial Intelligence by : Xian-Da Zhang
Download or read book A Matrix Algebra Approach to Artificial Intelligence written by Xian-Da Zhang and published by Springer Nature. This book was released on 2020-05-23 with total page 844 pages. Available in PDF, EPUB and Kindle. Book excerpt: Matrix algebra plays an important role in many core artificial intelligence (AI) areas, including machine learning, neural networks, support vector machines (SVMs) and evolutionary computation. This book offers a comprehensive and in-depth discussion of matrix algebra theory and methods for these four core areas of AI, while also approaching AI from a theoretical matrix algebra perspective. The book consists of two parts: the first discusses the fundamentals of matrix algebra in detail, while the second focuses on the applications of matrix algebra approaches in AI. Highlighting matrix algebra in graph-based learning and embedding, network embedding, convolutional neural networks and Pareto optimization theory, and discussing recent topics and advances, the book offers a valuable resource for scientists, engineers, and graduate students in various disciplines, including, but not limited to, computer science, mathematics and engineering.
Book Synopsis Direct Methods for Sparse Linear Systems by : Timothy A. Davis
Download or read book Direct Methods for Sparse Linear Systems written by Timothy A. Davis and published by SIAM. This book was released on 2006-09-01 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: The sparse backslash book. Everything you wanted to know but never dared to ask about modern direct linear solvers. Chen Greif, Assistant Professor, Department of Computer Science, University of British Columbia.Overall, the book is magnificent. It fills a long-felt need for an accessible textbook on modern sparse direct methods. Its choice of scope is excellent John Gilbert, Professor, Department of Computer Science, University of California, Santa Barbara.Computational scientists often encounter problems requiring the solution of sparse systems of linear equations. Attacking these problems efficiently requires an in-depth knowledge of the underlying theory, algorithms, and data structures found in sparse matrix software libraries. Here, Davis presents the fundamentals of sparse matrix algorithms to provide the requisite background. The book includes CSparse, a concise downloadable sparse matrix package that illustrates the algorithms and theorems presented in the book and equips readers with the tools necessary to understand larger and more complex software packages.With a strong emphasis on MATLAB and the C programming language, Direct Methods for Sparse Linear Systems equips readers with the working knowledge required to use sparse solver packages and write code to interface applications to those packages. The book also explains how MATLAB performs its sparse matrix computations.Audience This invaluable book is essential to computational scientists and software developers who want to understand the theory and algorithms behind modern techniques used to solve large sparse linear systems. The book also serves as an excellent practical resource for students with an interest in combinatorial scientific computing.Preface; Chapter 1: Introduction; Chapter 2: Basic algorithms; Chapter 3: Solving triangular systems; Chapter 4: Cholesky factorization; Chapter 5: Orthogonal methods; Chapter 6: LU factorization; Chapter 7: Fill-reducing orderings; Chapter 8: Solving sparse linear systems; Chapter 9: CSparse; Chapter 10: Sparse matrices in MATLAB; Appendix: Basics of the C programming language; Bibliography; Index.
