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Course detail

Numerical Mathematics I

Course unit code: FSI-9NM1
Academic year: 2016/2017
Year of study: Not applicable.
Semester: winter
Number of ECTS credits:
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.
Mode of delivery:
Not applicable.
Linear algebra, vector calculus, differential and integral calculus.
Not applicable.
Recommended optional programme components:
Not applicable.
Course contents (annotation):
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,.
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.
Course curriculum:
Not applicable.
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: doc. RNDr. Libor Čermák, CSc.
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.

The study programmes with the given course