MAT4001 at CUHKSZ
Numerical analysis is the study of algorithms that use numerical approximation (as opposed to general symbolic manipulations) for the problems of mathematical analysis (as distinguished from discrete mathematics). Numerical analysis naturally finds application in all fields of engineering and the physical sciences, but in the 21st century also the life sciences, social sciences, medicine, business and even the arts have adopted elements of scientific computations. As an aspect of mathematics and computer science that generates, analyzes, and implements algorithms, the growth in power and the revolution in computing has raised the use of realistic mathematical models in science and engineering, and complex numerical analysis is required to provide solutions to these more involved models of the world.
Before the advent of modern computers, numerical methods often depended on hand interpolation in large printed tables. Since the mid 20th century, computers calculate the required functions instead. These same interpolation formulas nevertheless continue to be used as part of the software algorithms for solving differential equations.
Numerical analysis continues this long tradition of practical mathematical calculations. Much like its tradtion, modern numerical analysis does not seek exact answers, because exact answers are often impossible to obtain in practice. Instead, much of numerical analysis is concerned with obtaining approximate solutions while maintaining reasonable bounds on errors.
For more details, see the Wikipedia page on numerical analysis.
Chapter | Notes | Slides | Assignments |
---|---|---|---|
1 Introduction | 1 | Introduction to Computer Arithmetic and Numerical Analysis | |
2 A Single Nonlinear Equation | 2 | Bisection method; Fixed-point iteration I and II; Newton's method; Secant method | Assignment 1 |
3 Interpolation | 3 | Lagrange interpolation I and II; Divided difference; Hermite interpolation I and II; Cubic splines I, II and III | Assignment 2 |
Mid-term Test | |||
4 Numerical Integration and Differentiation | 4 | Numerical differentiation I and II; Trapezoidal rule and Simpson's rule; Composite rules; Romberg integration; Gaussian quadrature | Assignment 3 |
5 Linear System | 5 | Direct Methods: Gaussian elimination; Pivoting; LU decomposition; Iterative Methods: Norms; Eigenvalues and eigenvectors; Jacobi iterative method; Gauss-Seidel iterative method; SOR method | Assignment 4 |
6 Linear Least Square Data Fitting | 6 | Assignment 5 | |
Final Exam |
# | Resourse | Author | Institue |
---|---|---|---|
Course Homepage | Elementary Numerical Analysis | Kendall E. Atkinson | Iwoa |
Course Homepage | TF502 Numerical Analysis | Boris Houska | ShanghaiTech |
Lecture Notes | Lecture Notes on Numerical Analysis | Peter J. Olver | Minnesota |
Lecture Notes | Introduction to Numerical Analysis | Doron Levy | Maryland |
Online Course | Introduction to Numerical Analysis | Laurent Demanet | MIT |
Lecture Notes | Lecture Notes on Numerical Methods | Tiejun Li | PKU |
Matlab Tutorial | Numerical Computing with MATLAB | Cleve Moler | MathWorks |