Modern methods of computational mathematics

Course: Applied Mathematics

Structural unit: Faculty of Computer Science and Cybernetics

Title
Modern methods of computational mathematics
Code
Module type
Вибіркова дисципліна для ОП
Educational cycle
First
Year of study when the component is delivered
2021/2022
Semester/trimester when the component is delivered
8 Semester
Number of ECTS credits allocated
3
Learning outcomes
PLO 1. Possession of in-depth professional knowledge and practical skills to optimize the design of models of any complexity, to solve specific problems of designing intelligent information systems of different physical nature.
Form of study
Prerequisites and co-requisites
1. Know: Mathematical analysis, functional analysis, theory of differential equations, theory of optimal control, algebra and numerical methods in the volume of the first four years of university. 2. Be able to: create programs in at least one programming language, read and analyze mathematical texts, implement mathematical algorithms. Read literature in English. 3. Have skills: working with a computer, searching for information on the Internet, using translation tools, creating presentations.
Course content
Developing students' understanding of methods of applied mathematics, and the ability to apply them to various problems. Within the discipline, students gain knowledge of current methods of computational mathematics, develop skills necessary for the use of such methods, train in numerical modeling of various problems.
Recommended or required reading and other learning resources/tools
1. Lyashko S.I., Semenov V.V., Klyushyn D.A. Special"ni pytannya optymizaciyi. Kyyiv, VPC “Kyyivs"kyj universytet”, 2015 2. Vasil'ev F.P. Metody reshenija jekstremal'nyh zadach. – M.: Nauka, 1981. 3. Iosida K. Funkcional'nyj analiz. – M.: Mir, 1967. 4. Lyashko S. I. Generalized optimal control of linear systems with distributed parameters. Boston / Dordrecht / London: Kluwer Academic Publishers, 2002. 466 p. 5. Nesterov Ju.E. Vvedenie v vypukluju optimimzaciju. – M.: MCNMO, 2010. 6. Sea Zh. Optimizacija. Teorija i algoritmy. – M.: Mir, 1973. 7. Gaevskij H., Grjoger K., Zaharias K. Nelinejnye operatornye uravnenija i operatornye differencial'nye uravnenija. – M.: Mir, 1978
Planned learning activities and teaching methods
Lectures, laboratory work, independent work.
Assessment methods and criteria
- semester assessment: 1. Test 1 - 25 points / 15 points 2. Test 2 - 25 points / 15 points 3. Laboratory work 1 - 25 points / 15 points 4. Laboratory work 2– 25 points / 15 points - final grade is based on the results of work in the semester. A student receives a grade if he scored 60 or more points in the semester, successfully passing at least one module test and defending one laboratory work. Organization of evaluation: 1. Modular test 1: up to 10 weeks of the semester. 2. Modular test 2: until the end of the semester. 2. Laboratory work 1: up to 10 weeks of the semester. 2. Laboratory work 2: until the end of the semester.
Language of instruction
Ukrainian

Lecturers

This discipline is taught by the following teachers

Serhii Ivanovych Lyashko
Computational Mathematics
Faculty of Computer Science and Cybernetics

Departments

The following departments are involved in teaching the above discipline

Computational Mathematics
Faculty of Computer Science and Cybernetics