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| ['Van de Geijn R. Advanced Linear Algebra. Foundations to Frontiers 2023.pdf'] | 0 bytes |
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SOURCE: Van de Geijn R. Advanced Linear Algebra. Foundations to Frontiers 2023
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MEDIAINFO
Textbook in PDF format Acknowledgements Preface Getting Started Opening Remarks Welcome Outline Week 0 What you will learn Setting Up For ALAFF Accessing these notes Cloning the ALAFF repository MatLAB Setting up to implement in C (optional) Enrichments Ten surprises from numerical linear algebra Best algorithms of the 20th century Wrap Up Additional Homework Summary Orthogonality Norms Opening Remarks Why norms? Overview What you will learn Vector Norms Absolute value What is a vector norm? The vector 2-norm (Euclidean length) The vector p-norms Unit ball Equivalence of vector norms Matrix Norms Of linear transformations and matrices What is a matrix norm? The Frobenius norm Induced matrix norms The matrix 2-norm Computing the matrix 1-norm and -norm Equivalence of matrix norms Submultiplicative norms Condition Number of a Matrix Conditioning of a linear system Loss of digits of accuracy The conditioning of an upper triangular matrix Enrichments Condition number estimation Practical computation of the vector 2-norm Wrap Up Additional homework Summary The Singular Value Decomposition Opening Remarks Low rank approximation Overview What you will learn Orthogonal Vectors and Matrices Orthogonal vectors Component in the direction of a vector Orthonormal vectors and matrices Unitary matrices Examples of unitary matrices Change of orthonormal basis Why we love unitary matrices The Singular Value Decomposition The Singular Value Decomposition Theorem Geometric interpretation An "algorithm" for computing the SVD The Reduced Singular Value Decomposition SVD of nonsingular matrices Best rank-k approximation Enrichments Principle Component Analysis (PCA) Wrap Up Additional homework Summary The QR Decomposition Opening Remarks Choosing the right basis Overview Week 3 What you will learn Gram-Schmidt Orthogonalization Classical Gram-Schmidt (CGS) Gram-Schmidt and the QR factorization Classical Gram-Schmidt algorithm Modified Gram-Schmidt (MGS) In practice, MGS is more accurate Cost of Gram-Schmidt algorithms Householder QR Factorization Using unitary matrices Householder transformation Practical computation of the Householder vector Householder QR factorization algorithm Forming Q Applying QH Orthogonality of resulting Q Enrichments Blocked Householder QR factorization Systematic derivation of algorithms Available software Wrap Up Additional homework Summary Linear Least Squares Opening Remarks Fitting the best line Overview What you will learn Solution via the Method of Normal Equations The four fundamental spaces of a matrix The Method of Normal Equations Solving the normal equations Conditioning of the linear least squares problem Why using the Method of Normal Equations could be bad Solution via the SVD The SVD and the four fundamental spaces Case 1: A has linearly independent columns Case 2: General case Solution via the QR factorization A has linearly independent columns Via Gram-Schmidt QR factorization Via the Householder QR factorization A has linearly dependent columns Enrichments Rank-Revealing QR (RRQR) via MGS Rank Revealing Householder QR factorization Wrap Up Additional homework Summary Solving Linear Systems The LU and Cholesky Factorizations Opening Remarks Of Gaussian elimination and LU factorization Overview What you will learn From Gaussian elimination to LU factorization Gaussian elimination LU factorization: The right-looking algorithm Existence of the LU factorization Gaussian elimination via Gauss transforms LU factorization with (row) pivoting Gaussian elimination with row exchanges Permutation matrices LU factorization with partial pivoting Solving A x = y via LU factorization with pivoting Solving with a triangular matrix LU factorization with complete pivoting Improving accuracy via iterative refinement Cholesky factorization Hermitian Positive Definite matrices The Cholesky Factorization Theorem Cholesky factorization algorithm (right-looking variant) Proof of the Cholesky Factorizaton Theorem Cholesky factorization and solving LLS Implementation with the classical BLAS Enrichments Other LU factorization algorithms Blocked LU factorization Formal derivation of factorization algorithms Wrap Up Additional homework Summary Numerical Stability Opening Remarks Whose problem is it anyway? Overview What you will learn Floating Point Arithmetic Storing real numbers as floating point numbers Error in storing a real number as a floating point number Models of floating point computation Stability of a numerical algorithm Conditioning versus stability Absolute value of vectors and matrices Error Analysis for Basic Linear Algebra Algorithms Initial insights Backward error analysis of dot product: general case Dot product: error results Matrix-vector multiplication Matrix-matrix multiplication Error Analysis for Solving Linear Systems Numerical stability of triangular solve Numerical stability of LU factorization Numerical stability of linear solve via LU factorization Numerical stability of linear solve via LU factorization with partial pivoting Is LU with Partial Pivoting Stable? Enrichments Systematic derivation of backward error analyses LU factorization with pivoting can fail in practice The IEEE floating point standard Wrap Up Additional homework Summary Solving Sparse Linear Systems Opening Remarks Where do sparse linear systems come from? Overview What you will learn Direct Solution Banded matrices Nested dissection Observations Iterative Solution Jacobi iteration Gauss-Seidel iteration Convergence of splitting methods Successive Over-Relaxation (SOR) Enrichments Details! Parallelism in splitting methods Dr. SOR Wrap Up Additional homework Summary Descent Methods Opening Remarks Solving linear systems by solving a minimization problem Overview What you will learn Search directions Basics of descent methods Toward practical descent methods Relation to Splitting Methods Method of Steepest Descent Preconditioning The Conjugate Gradient Method A-conjugate directions Existence of A-conjugate search directions Conjugate Gradient Method Basics Technical details Practical Conjugate Gradient Method algorithm Final touches for the Conjugate Gradient Method Enrichments Conjugate Gradient Method: Variations on a theme Wrap Up Additional homework Summary The Algebraic Eigenvalue Problem Eigenvalues and Eigenvectors Opening Remarks Relating diagonalization to eigenvalues and eigenvectors Overview What you will learn Basics Singular matrices and the eigenvalue problem The characteristic polynomial More properties of eigenvalues and vectors The Schur and Spectral Decompositions Diagonalizing a matrix Jordan Canonical Form The Power Method and related approaches The Power Method The Power Method: Convergence The Inverse Power Method The Rayleigh Quotient Iteration Discussion Enrichments How to compute the eigenvalues of a 2 2 matrix Wrap Up Additional homework Summary Practical Solution of the Hermitian Eigenvalue Problem Opening Remarks Subspace iteration with a Hermitian matrix Overview What you will learn From Power Method to a simple QR algorithm A simple QR algorithm A simple shifted QR algorithm Deflating the problem Cost of a simple QR algorithm A Practical Hermitian QR Algorithm Reduction to tridiagonal form Givens' rotations Simple tridiagonal QR algorithm The implicit Q theorem The Francis implicit QR Step A complete algorithm Enrichments QR algorithm among the most important algorithms of the 20th century Who was John Francis Casting the reduction to tridiagonal form in terms of matrix-matrix multiplication Optimizing the tridiagonal QR algorithm The Method of Multiple Relatively Robust Representations (MRRR) Wrap Up Additional homework Summary Computing the SVD Opening Remarks Linking the Singular Value Decomposition to the Spectral Decomposition Overview What you will learn Practical Computation of the Singular Value Decomposition Computing the SVD from the Spectral Decomposition A strategy for computing the SVD Reduction to bidiagonal form Implicitly shifted bidiagonal QR algorithm Jacobi's Method Jacobi rotation Jacobi's method for computing the Spectral Decomposition Jacobi's method for computing the Singular Value Decomposition Enrichments Principal Component Analysis Casting the reduction to bidiagonal form in terms of matrix-matrix multiplication Optimizing the bidiagonal QR algorithm Wrap Up Additional homework Summary Attaining High Performance Opening Remarks Simple Implementation of matrix-matrix multiplication Overview What you will learn Linear Algebra Building Blocks A simple model of the computer Opportunities for optimization Basics of optimizing matrix-matrix multiplication Optimizing matrix-matrix multiplication, the real story BLAS and BLIS Casting Computation in Terms of Matrix-Matrix Multiplication Blocked Cholesky factorization Blocked LU factorization Other high-performance dense linear algebra algorithms Libraries for higher level dense linear algebra functionality Sparse algorithms Enrichments BLIS and beyond Optimizing matrix-matrix multiplication - We've got a MOOC for that! Deriving blocked algorithms - We've got a MOOC for that too! Parallel high-performance algorithms Wrap Up Additional homework Summary Are you ready? Notation Householder notation Knowledge from Numerical Analysis Cost of basic linear algebra operations Computation with scalars Vector-vector operations Matrix-vector operations Matrix-matrix operations Summary Catastrophic cancellation GNU Free Documentation License References Answers and Solutions to Homeworks Index
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