
Advanced Numerical Methods with Matlab 1
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Content
- Intro
- Table of Contents
- Title
- Copyright
- Preface
- PART 1: Introduction
- 1 Review of Linear Algebra
- 1.1. Vector spaces
- 1.2. Linear mappings
- 1.3. Matrices
- 1.4. Determinants
- 1.5. Scalar product
- 1.6. Vector norm
- 1.7. Matrix eigenvectors and eigenvalues
- 1.8. Using Matlab
- 2 Numerical Precision
- 2.1. Introduction
- 2.2. Machine representations of numbers
- 2.3. Integers
- 2.4. Real numbers
- 2.5. Representation errors
- 2.6. Determining the best algorithm
- 2.7. Using Matlab
- PART 2: Approximating Functions
- 3 Polynomial Interpolation
- 3.1. Introduction
- 3.2. Interpolation problems
- 3.3. Polynomial interpolation techniques
- 3.4. Interpolation with the Lagrange basis
- 3.5. Interpolation with the Newton basis
- 3.6. Interpolation using spline functions
- 3.7. Using Matlab
- 4 Numerical Differentiation
- 4.1. First-order numerical derivatives and the truncation error
- 4.2. Higher-order numerical derivatives
- 4.3. Numerical derivatives and interpolation
- 4.4. Studying the differentiation error
- 4.5. Richardson extrapolation
- 4.6. Application to the heat equation
- 4.7. Using Matlab
- 5 Numerical Integration
- 5.1. Introduction
- 5.2. Rectangle method
- 5.3. Trapezoidal rule
- 5.4. Simpson's rule
- 5.5. Hermite's rule
- 5.6. Newton-Côtes rules
- 5.7. Gauss-Legendre method
- 5.8. Using Matlab
- PART 3: Solving Linear Systems
- 6 Matrix Norm and Conditioning
- 6.1. Introduction
- 6.2. Matrix norm
- 6.3. Condition number of a matrix
- 6.4. Preconditioning
- 6.5. Using Matlab
- 7 Direct Methods
- 7.1. Introduction
- 7.2. Method of determinants or Cramer's method
- 7.3. Systems with upper triangular matrices
- 7.4. Gaussian method
- 7.5. Gauss-Jordan method
- 7.6. LU decomposition
- 7.7. Thomas algorithm
- 7.8. Cholesky decomposition
- 7.9. Using Matlab
- 8 Iterative Methods
- 8.1. Introduction
- 8.2. Classical iterative techniques
- 8.3. Convergence of iterative methods
- 8.4. Conjugate gradient method
- 8.5. Using Matlab
- 9 Numerical Methods for Computing Eigenvalues and Eigenvectors
- 9.1. Introduction
- 9.2. Computing det (A - ?I) directly
- 9.3. Krylov methods
- 9.4. LeVerrier method
- 9.5. Jacobi method
- 9.6. Power iteration method
- 9.7. Inverse power method
- 9.8. Givens-Householder method
- 9.9. Using Matlab
- 10 Least-squares Approximation
- 10.1. Introduction
- 10.2. Analytic formulation
- 10.3. Algebraic formulation
- 10.4. Numerically solving linear equations by QR factorization
- 10.5. Applications
- 10.6. Using Matlab
- PART 4: Appendices
- Appendix 1: Introduction to Matlab
- A1.1. Introduction
- A1.2. Starting up Matlab
- A1.3. Mathematical functions
- A1.4. Operators and programming with Matlab
- A1.5. Writing a Matlab script
- A1.6. Generating figures with Matlab
- Appendix 2: Introduction to Optimization
- A2.1. Introduction
- A2.2. Standard results on functions from Rn to R
- A2.3. Optimization without constraints
- Bibliography
- Index
- End User License Agreement
1
Review of Linear Algebra
We will denote the fields of real and complex numbers by R and C respectively. If there is no need to distinguish between them, we will instead simply refer to the field
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