به قلبم قسم: رمان

A comprehensive treatment of numerical linear algebra from the standpoint of both theory and practice. The fourth edition of Gene H. Golub and Charles F. Van Loan's classic is an essential reference for computational scientists and engineers in addition to researchers in the numerical linear algebra community. Anyone whose work requires the solution to a matrix problem and an appreciation of its mathematical properties will find this book to be an indispensible tool. This revision is a cover-to-cover expansion and renovation of the third edition. It now includes an introduction to tensor computations and brand new sections on • fast transforms • parallel LU • discrete Poisson solvers • pseudospectra • structured linear equation problems • structured eigenvalue problems • large-scale SVD methods • polynomial eigenvalue problems Matrix Computations is packed with challenging problems, insightful derivations, and pointers to the literature―everything needed to become a matrix-savvy developer of numerical methods and software. The second most cited math book of 2012 according to MathSciNet, the book has placed in the top 10 for since 2005. Cover Contents Preface Global References Other Books Useful URLs Common Notation 1 Matrix Multiplication 1.1 Basic Algorithms and Notation 1.2 Structure and Efficiency 1.3 Block Matrices and Algorithms 1.4 Fast Matrix-Vector Products 1.5 Vectorization and Locality 1.6 Parallel Matrix Multiplication 2 Matrix Analysis 2.1 Basic Ideas from Linear Algebra 2.2 Vector Norms 2.3 Matrix Norms 2.4 The Singular Value Decomposition 2.5 Subspace Metrics 2.6 The Sensitivity of Square Systems 2.7 Finite Precision Matrix Computations 3 General Linear Systems 3.1 Triangular Systems 3.2 The LU Factorization 3.3 Roundoff Error in Gaussian Elimination 3.4 Pivoting 3.5 Improving and Estimating Accuracy 3.6 Parallel LU 4 Special Linear Systems 4.1 Diagonal Dominance and Symmetry 4.2 Positive Definite Systems 4.3 Banded Systems 4.4 Symmetric Indefinite Systems 4.5 Block Tridiagonal Systems 4.6 Vandermonde Systems 4.7 Classical Methods for Toeplitz Systems 4.8 Circulant and Discrete Poisson Systems 5 Orthogonalization and Least Squares 5.1 Householder and Givens Transformations 5.2 The QR Factorization 5.3 The Full-Rank Least Squares Problem 5.4 Other Orthogonal Factorizations 5.5 The Rank-Deficient Least Squares Problem 5.6 Square and Underdetermined Systems 6 Modified Least Squares Problems and Methods 6.1 Weighting and Regularization 6.2 Constrained Least Squares 6.3 Total Least Squares 6.4 Subspace Computations with the SVD 6.5 Updating Matrix Factorizations 7 Unsymmetric Eigenvalue Problems 7.1 Properties and Decompositions 7.2 Perturbation Theory 7.3 Power Iterations 7.4 The Hessenberg and Real Schur Forms 7.5 The Practical QR Algorithm 7.6 Invariant Subspace Computations 7.7 The Generalized Eigenvalue Problem 7.8 Hamiltonian and Product Eigenvalue Problems 7.9 Pseudospectra 8 Symmetric Eigenvalue Problems 8.1 Properties and Decompositions 8.2 Power Iterations 8.3 The Symmetric QR Algorithm 8.4 More Methods for Tridiagonal Problems 8.5 Jacobi Methods 8.6 Computing the SVD 8.7 Generalized Eigenvalue Problems with Symmetry 9 Functions of Matrices 9.1 Eigenvalue Methods 9.2 Approximation Methods 9.3 The Matrix Exponential 9.4 The Sign, Square Root, and Log of a Matrix 10 Large Sparse Eigenvalue Problems 10.1 The Symmetric Lanczos Process 10.2 Lanczos, Quadrature, and Approximation 10.3 Practical Lanczos Procedures 10.4 Large Sparse SVD Frameworks 10.5 Krylov Methods for Unsymmetric Problems 10.6 Jacobi-Davidson and Related Methods 11 Large Sparse Linear System Problems 11.1 Direct Methods 11.2 The Classical Iterations 11.3 The Conjugate Gradient Method 11.4 Other Krylov Methods 11.5 Preconditioning 11.6 The Multigrid Framework 12 Special Topics 12.1 Linear Systems with Displacement Structure 12.2 Structured-Rank Problems 12.3 Kronecker Product Computations 12.4 Tensor Unfoldings and Contractions 12.5 Tensor Decompositions and Iterations Index A B C D E F G H I J K L M N O P Q R S T U V W Y

The fourth edition of Gene H. Golub and Charles F. Van Loan's classic is an essential reference for computational scientists and engineers in addition to researchers in the numerical linear algebra community. Anyone whose work requires the solution to a matrix problem and an appreciation of its mathematical properties will find this book to be an indispensible tool.

This revision is a cover-to-cover expansion and renovation of the third edition. It now includes an introduction to tensor computations and brand new sections on
• fast transforms
• parallel LU
• discrete Poisson solvers
• pseudospectra
• structured linear equation problems
• structured eigenvalue problems
• large-scale SVD methods
• polynomial eigenvalue problems

Matrix Computations is packed with challenging problems, insightful derivations, and pointers to the literature—everything needed to become a matrix-savvy developer of numerical methods and software. The second most cited math book of 2012 according to MathSciNet, the book has placed in the top 10 for since 2005.

The fourth edition of Gene H. Golub and Charles F. Van Loan's classic is an essential reference for computational scientists and engineers in addition to researchers in the numerical linear algebra community. Anyone whose work requires the solution to a matrix problem and an appreciation of its mathematical properties will find this text useful and engaging. This revision is a cover-to-cover expansion and renovation of the third edition. It now includes an introduction to tensor computations and brand new sections on fast transforms parallel LU discrete Poisson solvers pseudospectra structured linear equation problems structured eigenvalue problems large-scale SVD methods polynomial eigenvalue problems Matrix Computations is packed with challenging problems, insightful derivations, and pointers to the literatureeverything needed to become a matrix-savvy developer of numerical methods and software.

Revised and updated, the third edition of Golub and Van Loan's classic text in computer science provides essential information about the mathematical background and algorithmic skills required for the production of numerical software. This new edition includes thoroughly revised chapters on matrix multiplication problems and parallel matrix computations, expanded treatment of CS decomposition, an updated overview of floating point arithmetic, a more accurate rendition of the modified Gram-Schmidt process, and new material devoted to GMRES, QMR, and other methods designed to handle the sparse unsymmetric linear system problem.

The Johns Hopkins University Press

Provides the vital mathematical background and algorithmic skills requred for the production numerical software.

"Thoroughly revised, updated, and expanded by more than one third, this new edition of Golub and Van Loan's landmark book in scientific computing provides the vital mathematical background and algorithmic skills required for the production of numerical software. New chapters on high performance computing use matrix multiplication to show how to organize a calculation for vector processors as well as for computers with shared or distributed memories. A.so new are discussions of parallel vector methods for linear equations, least squares, and eigenvalue problems."--Back cover. Suitable for computational scientists and engineers in addition to researchers in numerical linear algebra community, this title includes an introduction to tensor computations and fresh sections on: discrete Poisson solvers; pseudospectra; structured linear equation problems; structured eigenvalue problems; and, polynomial eigenvalue problems. This revised edition provides the mathematical background and algorithmic skills required for the production of numerical software. It includes rewritten and clarified proofs and derivations, as well as new topics such as Arnoldi iteration, and domain decomposition methods