Linear Algebra 3 Edition
Author: G. Strang

Index Code :  Science.Mathematics.LinearAlgebra

Example Code

Chapters / Sections

Examples
1. Introduction to Vectors                        
    1.1 Vectors and Linear Combinations A B                    
    1.2 Lengths and Dot Products 1 2 3 4 5 6 A B        
2. Solving Linear Equations                        
    2.1 Vectors and Linear Equations 1 A B                  
    2.2 The Idea of Elimination 1 2 3 A B              
    2.3 Elimination Using Matrices 1 2 A B C              
    2.4 Rules for Matrix Operations 1 2 3 4 A B C          
    2.5 Inverse Matrices 1 2 3 4 5 A B          
    2.6 Elimination = Factorization : A = LU 1 2 3 A B              
    2.7 Transpose and Permutations 1 2 3 4 A B            
3. Vector Spaces and Subspace  
    3.1 Spaces of Vectors 1 2 3 4 5 A B          
    3.2 The Nullspace of A: Solving Ax = 0 1 2 3 4 A B
    3.3 The Rank and the Row Reduced Form 1 2 A B                
    3.4 The Complete Solution to Ax = b 1 2 A B C              
    3.5 Independence, Basis and Dimension 1 2 3 4 5 6 7 8 9 A B  
    3.6 Dimensions of the Four Subspaces 1 2 A B                
4. Orthogonality  
    4.1 Orthogonality of the Four Subspaces 1 2 3 4 5 A B          
    4.2 Projections 1 2 3 A B              
    4.3 Least Squares Approximations 1 2 3 A B              
    4.4 Orthogonal Bases and Gram-Schmidt 1 2 3 4 5 A            
5. Determinant  
    5.1 The Properties of Determinants A B                    
    5.2 Permutations and Cofactors 1 2 3 4 5 6 7 A B      
    5.3 Cramer's Rule, Inverse, and Volumes 1 2 3 4 5 6 7 8 9 10 A B
6. Eigenvalues and Eigenvectors  
    6.1 Introduction to Eigenvalues 1 2 3 4 A B            
    6.2 Diagonalizing a Matrix 1 2 3 A B              
    6.3 Applications to Differential Equations 1 2 3 4 5 6 A B        
    6.4 Symmetric Matrices 1 2 3 4 A              
    6.5 Positive Definite Matrices 1 2 3 4 5 6 7 A B      
    6.6 Similar Matrices 1 2 3 4 A B            
    6.7 Singular Value Decomposition (SVD) 1 2 A                  
7. Linear Transformation                        
    7.1 The Idea of a Linear Transformation 1 2 3 4 5 6 A B        
    7.2 The Matrix of a Linear Transformation 1 2 3 4 5 6 7 8 9 10 A B
    7.3 Change of Basis 1 2 3 A                
    7.4 Diagonalization and the Pseudoinverse 1 2 3 4 A              
8. Applications                        
    8.1 Matrices in Engineering 1 2                    
    8.2 Graphs and Networks 1                      
    8.3 Markov Matrices and Economic Models 1 2 3 4 5              
    8.4 Linear Programming 1 2                    
    8.5 Fourier Series: Linear Algebra for Functions 1 2 3                  
    8.6 Computer Graphics                        
9. Numerical Linear Algebra                        
    9.1 Gaussian Elimination in Practice                        
    9.2 Norms and Condition Numbers 1 2 3 4                
    9.3 Iterative Methods for Linear Algebra 1                      
10.Complex Vectors and Matrices                        
    10.1 Complex Numbers 1                      
    10.2 Hermitian and Unitary Matrices 1 2 3              
    10.3 The Fast Fourier Transform 1