Course detail

Numerical Mathematics I

FSI-9NM1Acad. year: 2016/2017Winter semesterNot applicable.. year of study1  credit

The introductory course of numerical methods deals with following topics: scientific computing, direct and iterative methods for linear systems, interpolation, least squares, differentiation and quadrature, eigenvalues, zeros and roots,.

Learning outcomes of the course unit

Students will be made familiar with basic numerical methods of linear algebra, nonlinear equations, interpolation, differentiation and integration. Based on this knowledge they ought to be able to choose suitable software product (exceptionally to write their own program) and then succesfully apply it in solving their specific technical problems.

Prerequisites

Linear algebra, vector calculus, differential and integral calculus.

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

M.T. Heath: Scientific Computing. An Introductory Survey. Second edition. McGraw-Hill, New York, 2002.
L. Čermák, R. Hlavička: Numerické metody. Učební text FSI VUT Brno, CERM, 2005.
L. Čermák: Vybrané statě z numerických metod. http://mathonline.fme.vutbr.cz/Numericke-metody-I/sc-1150-sr-1-a-141
C.B. Moler: Numerical Computing with Matlab, Siam, Philadelphia, 2004.
G. Dahlquist, A. Bjork: Numerical Methods. Prentice-Hall, 1974
A. R. Ralston: Základy numerické matematiky. Academia, Praha, 1973.
E. Vitásek: Numerické metody. SNTL, Praha, 1987
A. Quarteroni, S. Sacco, F. Saleri: Numerical Mathematics, Springer-Verlag, New York, 2000.
C.F. Van Loan, G.H. Golub: Matrix Computations, 3th ed., the Johns Hopkins University Press, Baltimore, 1996.
K. Rektorys: Přehled užité matematiky. Prometheus, Praha, 1995
I. Horová, J. Zelinka: Numerické metody, učební text Masarykovy univerzity, Brno, 2004.

Planned learning activities and teaching methods

The course is taught through lectures explaining the basic principles and theory of the discipline.

Assesment methods and criteria linked to learning outcomes

The exam has an oral part only. The student has to answer three questions, one question from the range "numerical linear algebra", second question from the range "solving nonlinear equations" and third question from the range "interpolation, differentiation and integration".

Language of instruction

Czech, English

Work placements

Not applicable.

Aims

The objective of the course is to make students familiar with numerical methods of linear algebra, with solution methods for nonlinear equations and with methods of interpolation, numerical differentiation and integration.

Specification of controlled education, way of implementation and compensation for absences

Attendance at lectures is facultative, but highly recommended.

Type of course unit

 

Lecture

20 hours, optionally

Teacher / Lecturer

Syllabus

The course has 10 two-hours lessons.
1. Introduction to numerical mathematics: foundation of matrix analysis, errors, conditionning of problems and algorithms.
2. Direct methods for solving linear systems: Gaussian elimination method, pivoting, LU decomposition, Cholesky decomposition, conditioning.
3. Iterative methods for solving linear systems: classical iterative methods (Jacobi, Gauss-Seidel, SOR, SSOR), generalized minimum rezidual method, conjugate gradient method.
4. Interpolation: Lagrange, Newton and Hermite interpolation polynomial, interpolating splines.
5. Least squares method: data fitting, solving overdetermined systems (QR factorization, pseudoinverse, orthogonalization methods).
6. Numerical differentiation: basic formulas, Richardson extrapolation.
7. Numerical integration: Newton-Cotes formulas, Gaussian formulas, adaptive integration.
8. Solving nonlinear equations in one dimension (bisection method, Newton's method, secant method, false position method, inverse quadratic interpolation, Brent method); solving nonlinear systems (Newton's method and its variants, fixed point iteration).
9. Eigenvalues and eigenvectors: power method, QR method.
10. Eigenvalues and eigenvectors: Arnoldi method, Jacobi method, bisection method, computing the singular value decomposition.