SylabUZ
Course name | Introduction to Numerical Methods |
Course ID | 11.0-WK-MATP-WMN-Ć-S14_pNadGenVW6K7 |
Faculty | Faculty of Mathematics, Computer Science and Econometrics |
Field of study | Mathematics |
Education profile | academic |
Level of studies | First-cycle studies leading to Bachelor's degree |
Beginning semester | winter term 2020/2021 |
Semester | 5 |
ECTS credits to win | 7 |
Course type | optional |
Teaching language | polish |
Author of syllabus |
|
The class form | Hours per semester (full-time) | Hours per week (full-time) | Hours per semester (part-time) | Hours per week (part-time) | Form of assignment |
Class | 15 | 1 | - | - | Credit with grade |
Laboratory | 15 | 1 | - | - | Credit with grade |
Lecture | 30 | 2 | - | - | Exam |
The goal of this course is to teach the basic theories and fundamentals of numerical methods and to give the student knowledge how to implement these methods for computer solutions of mathematical problems. Most of the applications are based on the use of mathematical software package (Matlab, Octave or Scilab). The course also provides an introduction to Matlab as well as practice in computer programming. Topics include analysis of errors, numerical linear algebra, solution of linear systems of equations and
nonlinear equations, interpolation and approximation by polynomials and numerical integration. Examples are taken from a wide variety of engineering situations.
Students attending classes are expected to pass the following courses:
1. Mathematical Analysis 1,2;
2. Linear Algebra 1,2;
Lecture
1. Computer Arithmetic
2. Solution of Nonlinear Equations
3. Solving Systems of Linear Equations
4. Interpolation and Polynomial Approximation
5. Numerical Integration
Class
1. Computer Arithmetic
2. Solution of Nonlinear Equations
3. Solving Systems of Linear Equations
4. Interpolation and Polynomial Approximation
Laboratory
1. Computer Arithmetic
2. Solution of Nonlinear Equations
3. Solving Systems of Linear Equations
4. Interpolation and Polynomial Approximation
Solving appropriate selected problems in the class and laboratory students can familiarize themselves with numerical methods provided during the lectures.
Outcome description | Outcome symbols | Methods of verification | The class form |
Verifying the level of preparation of students and their activities during the classes and laboratories.
Tests with tasks of different difficulty which help to assess whether students have achieved effects of the course in a minimum degree.
The assessment of the course consists of the grades of the exercises and laboratories (40%) and the written exam (60%). Student takes an exam under the condition of a positive evaluation of the exercise.
Student passes the course under the condition of a positive evaluation of the exercise, laboratory and written exam.
1. A.Björck, G.Dahlquist, Numerical Methods in Scientific Computing, SIAM, 2008.;
2. R.L.Burden, J.D.Faires, Numerical analysis, Prindle, Weber & Schmidt, Boston, Massachusetts, 1981;
3. J.Stoer, R.Bulirsch, Introduction to Numerical Analysis, Springer, 1993;
1. A.Quarteroni, R.Sacco, F.Saleri, Numerical mathematics, Springer, 2002;
2. A.Quarteroni, F.Saleri, Scientific Computing with Matlab and Octave, Springer, 2006;
3. P.Deuflhard, A.Hohmann Numerical analysis in modern scientific computing. An introduction, Springer, 2003;
Modified by dr Alina Szelecka (last modification: 18-09-2020 13:45)