Numerical analysis
Course: Applied Mathematics
Structural unit: Faculty of Computer Science and Cybernetics
Title
Numerical analysis
Code
ННД.20
Module type
Обов’язкова дисципліна для ОП
Educational cycle
First
Year of study when the component is delivered
2023/2024
Semester/trimester when the component is delivered
7 Semester
Number of ECTS credits allocated
4
Learning outcomes
LO15. To be able to organize one's own activities and obtain a result within a limited time.
Form of study
Full-time form
Prerequisites and co-requisites
To successfully study the discipline "Numerical analysis", a student must meet the following requirements:
Successful completion of courses
1. Mathematical analysis.
2. Algebra and geometry
3. Programming
4. Differential equations
5. Object-oriented programming
6. Basics of calculation methods
Knowledge:
1. Basic definitions and theorems of mathematical analysis, algebra and geometry, operations research, theory of differential equations, basics of calculation methods.
2. Procedural and object-oriented programming.
Skill:
1. To solve the problems of mathematical analysis, algebra and geometry, operations research, the theory of differential equations, the basics of calculation methods.
2. Program in procedural and object-oriented styles.
Course content
1 Topic 1. Approximation of functions.
Element of best approximation in linear normalized and Hilbert spaces.
2 Topic 2. Approximation of discrete functions. The method of least squares and its application.
3 Topic 3. Approximate calculation of integrals. Quadrature formulas of the interpolation type.
4 Topic 4. Quadrature formulas of the highest algebraic degree of accuracy. Approximate calculation of improper integrals, integrals of rapidly oscillating functions.
5 Topic 5. Approximate methods of solving the Cauchy problem. Approximate analytical methods. Numerical one-step methods.
6 Topic 6. Multi-step methods of solving the Cauchy problem and methods of their implementation.
7 Topic 7. Numerical methods for solving boundary value problems for systems of ordinary differential equations.
Recommended or required reading and other learning resources/tools
1. M.M.Moskal-kov, A.І.Rizhenko, S.O.Voitsekhovs-kii ta іn. Praktikum z
metodіv obchislen-. Kiїv, MAUP. 2008.
2. Sharyi S.P. Kurs vychislitel-nykh metodov. NGU, 2018.
3. Bakhvalov N.S., Zhidkov N.P., Kobel-kov G.N. Chislennye metody.
BINOM, 2011.
4. Makarov V.L., Gavriliuk I.P. Metodi obchislen-. Kiїv, Vishcha shkola, 1995.
5. Golubeva K. M., Denisov S. V., Kashpur O. F., Kliushin D. A., Rizhenko A. І.
Chisel-nі metodi іntegruvannia. K., 2019.
6. Kalitkin, N.N. Chislennye metody. - BHV, 2014.
7. Volkov A.F. Chislennye metody. – Lan-, 2008.
..
Planned learning activities and teaching methods
Lectures, laboratory classes, independent work
Assessment methods and criteria
Semester assessment:
1. Control work #1: PH 1.1, PH 1.2– 10/6 points.
2. Control work #2: PH 1.3, PH 1.4 – 10/6 points.
3. Laboratory work No. 1: RN 1.1, RN 2.1, RN 3.1, RN 3.2, RN 4.1, RN 4.2 – 10/6 points.
4. Laboratory work No. 2: RN 1.2, RN 2.2, RN 3.1, RN 3.2, RN 4.1, RN 4.2 – 10/6 points.
5. Laboratory work No. 3: RN 1.3, RN 2.3, RN 3.1, RN 3.2, RN 4.1, RN 4.2 – 8/5 points.
6. Laboratory work No. 4: RN 1.4, RN 2.4, RN 3.1, RN 3.2, RN 4.1, RN 4.2 – 7/4 points.
7. Assessment of independent work: PH 1.1, PH 1.2, PH 1.3, PH 1.4, PH2.1, PH 2.2, PH 2.3, PH 2.4, PH 4.1, PH 4.2 - 5/3 points.
Final evaluation (in the form of an exam):
1. The maximum number of points that can be obtained by a student: 40/24 points/(s).
2. Learning outcomes that will be assessed: PH 1.1, PH 1.2, PH 1.3, PH 1.4, PH 2.1, PH 2.2, PH 2.3, PH 2.4.
3. Form of conduct: written work.
4. Types of tasks: 4 written tasks (1 theoretical question and 3 practical tasks).
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Language of instruction
Ukrainian
Lecturers
This discipline is taught by the following teachers
Departments
The following departments are involved in teaching the above discipline