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Mathematics --- lineaire algebra --- 512.64 --- 519.61 --- 519.61 Numerical methods of algebra --- Numerical methods of algebra --- 512.64 Linear and multilinear algebra. Matrix theory --- Linear and multilinear algebra. Matrix theory --- Algebras, Linear --- Computer science --- Algèbre linéaire --- Informatique --- Mathématiques

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519.65 --- 519.61 --- 681.3*G14 --- 681.3*G14 Quadrature and numerical differentiation: adaptive quadrature; equal intervalintegration; error analysis; finite difference methods; gaussian quadrature; iterated methods; multiple quadrature --- Quadrature and numerical differentiation: adaptive quadrature; equal intervalintegration; error analysis; finite difference methods; gaussian quadrature; iterated methods; multiple quadrature --- 519.61 Numerical methods of algebra --- Numerical methods of algebra --- 519.65 Approximation. Interpolation --- Approximation. Interpolation --- Ordered algebraic structures

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Numerical linear algebra is one of the most important subjects in the field of statistical computing. Statistical methods in many areas of application require computations with vectors and matrices. This book describes accurate and efficient computer algorithms for factoring matrices, solving linear systems of equations, and extracting eigenvalues and eigenvectors. Although the book is not tied to any particular software system, it describes and gives examples of the use of modern computer software for numerical linear algebra. An understanding of numerical linear algebra requires basic knowledge both of linear algebra and of how numerical data are stored and manipulated in the computer. The book begins with a discussion of the basics of numerical computations, and then describes the relevant properties of matrix inverses, matrix factorizations, matrix and vector norms, and other topics in linear algebra; hence, the book is essentially self- contained. The topics addressed in this book constitute the most important material for an introductory course in statistical computing, and should be covered in every such course. The book includes exercises and can be used as a text for a first course in statistical computing or as supplementary text for various courses that emphasize computations. James Gentle is University Professor of Computational Statistics at George Mason University. During a thirteen-year hiatus from academic work before joining George Mason, he was director of research and design at the world's largest independent producer of Fortran and C general-purpose scientific software libraries. These libraries implement many algorithms for numerical linear algebra. He is a Fellow of the American Statistical Association and member of the International Statistical Institute. He has held several national.

Algebra --- Numerical analysis --- Algebras, Linear. --- Linear models (Statistics) --- Algèbre linéaire --- Modèles linéaires (Statistique) --- 519.61 --- Numerical methods of algebra --- 519.61 Numerical methods of algebra --- Algèbre linéaire --- Modèles linéaires (Statistique) --- Algebras, Linear --- Models, Linear (Statistics) --- Mathematical models --- Mathematical statistics --- Statistics --- Linear algebra --- Algebra, Universal --- Generalized spaces --- Mathematical analysis --- Calculus of operations --- Line geometry --- Topology --- lineaire algebra --- Algebra. --- Statistics . --- Matrix theory. --- Statistics and Computing/Statistics Programs. --- Linear and Multilinear Algebras, Matrix Theory. --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics

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Numerical analysis --- Data processing --- -519.6 --- 681.3*G1 --- Mathematical analysis --- Computational mathematics. Numerical analysis. Computer programming --- 681.3*G1 Numerical analysis --- 519.6 Computational mathematics. Numerical analysis. Computer programming --- Numerical linear algebra: conditioning determinants Eigenvalues error analysis linear systems matrix inversion pseudoinverses sparse and very largesystems --- 681.3*G13 Numerical linear algebra: conditioning determinants Eigenvalues error analysis linear systems matrix inversion pseudoinverses sparse and very largesystems --- Algebras, Linear --- Algebras, Linear. --- Numerical analysis. --- Data processing. --- 519.61 --- 519.61 Numerical methods of algebra --- Numerical methods of algebra --- Linear algebra --- Algebra, Universal --- Generalized spaces --- Calculus of operations --- Line geometry --- Topology --- 519.6 --- 681.3*G13 --- 681.3*G13 Numerical linear algebra: conditioning; determinants; Eigenvalues; error analysis; linear systems; matrix inversion; pseudoinverses; sparse and very largesystems --- Numerical linear algebra: conditioning; determinants; Eigenvalues; error analysis; linear systems; matrix inversion; pseudoinverses; sparse and very largesystems --- Analyse numérique. --- Algèbre linéaire. --- Itération (mathématiques) --- Iterative methods (Mathematics) --- Numerical analysis - Data processing --- -Data processing