# Numerical analysis with laboratory (2009/2010)

Course code
4S02755
Credits
12
Coordinator
Leonard Peter Bos
Other available courses
Other available courses
MAT/08 - NUMERICAL ANALYSIS
Language of instruction
Italian
Teaching is organised as follows:
Activity Credits Period Academic staff Timetable
Teoria 9 1st Semester, 2nd Semester Leonard Peter Bos
Laboratorio 3 1st Semester, 2nd Semester Manolo Venturin

### Lesson timetable

1st Semester
Activity Day Time Type Place Note
Teoria Thursday 4:30 PM - 5:30 PM lesson Lecture Hall E
Laboratorio Tuesday 4:30 PM - 6:30 PM lesson Lecture Hall E, Delta
2nd Semester
Activity Day Time Type Place Note
Teoria Monday 11:30 AM - 1:30 PM lesson Lecture Hall E
Teoria Friday 8:30 AM - 10:30 AM lesson Lecture Hall E
Laboratorio Wednesday 8:30 AM - 10:30 AM laboratorio Laboratory Delta

### Learning outcomes

Module: Theory
-------
In this course we will study the most important methods for the numerical solution of the classical problems of Mathematical Analysis. Besides the required theoretical background material needed for understanding the subject, particular emphasis will be placed on algorithms, including their implementation, their complexity and efficiency, as well as the purely numerical problems of convergence and stability. The objective is thus not just to provide the student with a knowledge of the methods, but also to cultivate a "numerical intution" which is important for solving real world problems.

Module: Laboratory
-------
The implementation in Matlab and/or GNU Octave of the basic algorithms of Numerical Analysis.

### Syllabus

Module: Theory
-------
* Error Analysis
Representation of numbers. Absolute error and relative error. Machine numbers and associated errors. Algoriths for evaluating an expression. Conditioning of problems and the stability of methods.
* Non-linear Equations
The bisection method. Fixed point methods: generality, convergence and stopping criteria. The secant method, Newton's method and Aitken acceleration. Algebraic polynomials: Horner's rule.
* Linear Systems
Direct methods: LU factorization and pivoting, forward and back substitution, fast algorithms for tridiagonal systems.
Iterative methods: Jacobi iteration, Gauss-Seidel and SOR. Iterative improvement. Richardson's method and the gradient method. Sparse and banded systems. Solution of over and under determined systems. Solution of poorly conditioned systems.
* Eigenvalues and Eigenvectors
Localization of eigenvalues: Gerschgorin circles. The power method and the inverse power method. The QR method and its variants. Eigenvalues of tridiagonal matrices: Schur's method.
* Interpolation and Approximation of Functions and Data.
Polynomial interpolation: the Newton and Lagrange form. Approximation error estimates. Trigonometric interpolation and the the Fast Fourier Transform (FFT). Piecewise polynomial interpolation and splines. Least squares and the SVD.
* Numerical Differentiation and Integration.
Simple formulas for approximating derivatives and their relative error.