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　

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