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Matrix Computations and Semiseparable Matrices - Eigenvalve and Singular Value Methods V2

The general properties and mathematical structures of semiseparable matrices were presented in volume 1 of Matrix Computations and;

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Fundamentals of Matrix Computations

Fundamentals of Matrix Computations deals with the concept of matrix computations, a technique of singular value homogenization and its;

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Matrix Computations and Semiseparable Matrices V 1

In recent years several new classes of matrices have been discovered and their structure exploited to design fast and accurate algorithms;

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Quaternion Matrix Computations

, especially the LU, the Cholesky, the QR and the singular value decomposition of quaternion matrices, direct and iterative methods for solving;

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Linear Algebra, Matrix Theory and Applications

vector spaces, least square regression, determinants, eigen values, eigen vectors, positive definite matrices, singular value decomposition and;

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Matrix Algebra for Linear Models

also features: * Methods of deriving results from the properties of eigenvalues and the singular value decomposition * Solutions to matrix;

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Matrix Algorithms

are devoted to the LU and QR decompositions-their computation and applications. The singular value decomposition is also treated, although;

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Fundamentals of Matrix Computations

A significantly revised and improved introduction to a critical aspect of scientific computationMatrix computations lie at the heart;

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Linear Systems Properties

of Fourier series, Fourier transforms, Hilbert transforms, Laplace transforms, and z-transforms. Singular value decomposition techniques for matrix;

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Matrix Algebra

Matrix algebra is one of the most important areas of mathematics for data analysis and for statistical theory. This much-needed work;

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Matrix Algebra

Matrix algebra is one of the most important areas of mathematics for data analysis and for statistical theory. This much-needed work;

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Exploiting Hidden Structure in Matrix Computations: Algorithms and Applications

Focusing on special matrices and matrices which are in some sense `near' to structured matrices, this volume covers a broad range of topics;

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Linear Algebra and Matrix Computations with MATLAB (R)

representations, fundamental analysis, transformations of matrices, matrix equation solutions as well as matrix functions. Attempts on matrix and linear;

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Handbook of Matrices

Matrices are used in many fields such as statistics, econometrics, mathematics, natural sciences and engineering. They provide a concise;

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Linear Algebra

classes with a mix of mathematics, physics, engineering, and computer science students. Major topics include singular value decomposition, the;

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Fast Solvers for Mesh-Based Computations

computations. It also describes how to design and implement those algorithms. The book's structure follows those of the matrices, starting from tri;

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Concise Introduction to Linear Algebra

, eigenvalues and eigenvectors, diagonalizability, spectral decomposition, positive definite matrix, quadratic forms, singular value decompositions and;

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Concise Introduction to Linear Algebra

, eigenvalues and eigenvectors, diagonalizability, spectral decomposition, positive definite matrix, quadratic forms, singular value decompositions and;

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Toeplitz Matrices, Convolution Operators, and Integral Equations

This volume, dedicated to Bernd Silbermann on his sixtieth birthday, collects research articles on Toeplitz matrices and singular integral;

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Matrix Analysis

algebra and has more than 1,100 problems and exercises, new sections on the singular value and CS decompositions and the Weyr canonical form;

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Matrix Analysis

algebra and has more than 1,100 problems and exercises, new sections on the singular value and CS decompositions and the Weyr canonical form;

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High Performance Algorithms for Structured Matrix Problems

well as the area of structured eigenvalue and singular-value problems. The volume highlights research directions perceived to be the most;

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Matrices, Moments and Quadrature with Applications

This computationally oriented book describes and explains the mathematical relationships among matrices, moments, orthogonal polynomials;

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Generalized Inverses

inverse, generalized singular value decomposition, imbedding method, finite method, generalized inverses of polynomial matrices, and generalized;

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Linear Algebra and Matrices

properties of linear transformations in inner product spaces, singular value decomposition, and min-max characterizations of Hermitian matrices and;

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Numerical Methods in Matrix Computations

Matrix algorithms are at the core of scientific computing and are indispensable tools in most applications in engineering. This book offers;

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Polynomial and Matrix Computations

Our Subjects and Objectives. This book is about algebraic and symbolic computation and numerical computing (with matrices and polynomials;

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