Advanced functional analysis. Module 1. Applied functional analysis. Module 2. Convex and nonlinear
Course: Applied mathematics
Structural unit: Faculty of Computer Science and Cybernetics
Title
Advanced functional analysis. Module 1. Applied functional analysis. Module 2. Convex and nonlinear
Code
Module type
Вибіркова дисципліна для ОП
Educational cycle
Second
Year of study when the component is delivered
2021/2022
Semester/trimester when the component is delivered
1 Semester
Number of ECTS credits allocated
6
Learning outcomes
PLO3. Gaining knowledge for the ability to evaluate existing technologies and on the basis of analysis to form requirements for the development of advanced information technologies.
Form of study
Prerequisites and co-requisites
Know the basic concepts and facts of mathematical analysis, functional analysis and linear algebra. Be able to solve typical problems in mathematical analysis, functional analysis and linear algebra. Have basic skills of searching for information on the Internet.
Course content
Obtaining basic knowledge of applied functional analysis, mastering the techniques of solving applied problems using the methods of functional analysis. Obtaining knowledge of modern convex and nonlinear functional analysis. As a result of studying the discipline the student must: know the theory of duality, basic facts about convex sets and functions, methods of approximation of fixed points; be able to apply the studied methods and results in the study of typical issues related to the analysis of mathematical models: the existence, correctness, construction of an approximate method.
Recommended or required reading and other learning resources/tools
Kadets V.M. Functional Analysis Course. Kh.: KhNU im. V.N. Karazina, 2006.
Aleksandryan R.A., Mirzakhanyan E.A. General topology. Moscow: Vischaya schkola, 1979. Kantorovich L.V., Akilov G.P. Functional analysis. Moscow: Nauka, 1984.
Bauschke H.H., Combettes P.L. Convex Analysis and Monotone Operator Theory in Hilbert Spaces. Springer, 2011.
Goebel K., Kirk W.A. Topics in metric fixed point theory. Cambridge University Press, 1990.
Aubin J.-P., Ekland I. Applied nonlinear analysis. Moscow: Mir, 1988.
Kinderlehrer D., Stampacchia G. Introduction to variational inequalities and their applications. Moscow: Mir, 1983.
Ekland I., Temam R. Convex analysis and variational problems. Moscow: Mir, 1979.
Planned learning activities and teaching methods
Lectures, independent work, recommended literature processing, homeworks, test works, exam.
Assessment methods and criteria
Maximum number of points that can be obtained by a student: 100/60 points.
- Intermediate assessment:
1. Test work 1: LO 1.1., LO 1.2 - 10 points / 6 points.
2. Test work 2: LO 1.1., LO 1.2 - 10 points / 6 points.
3. Independent work 1: LO 1.1, LO 2.1, LO 3.1 - 20 points / 12 points.
4. Independent work 2: LO 1.1, LO 2.1, LO 3.1 - 20 points / 12 points.
- Final assessment (in the form of exam):
- the maximum number of points that can be obtained by a student: 40 points;
- learning outcomes that will be evaluated: LO 1.1, LO 1.2, LO 2.1;
- form and types of tasks: writing.
Types of tasks: 4 writing tasks.
Language of instruction
Ukrainian
Lecturers
This discipline is taught by the following teachers
Dmytro
Anatoliiovych
Klyushin
Computational Mathematics
Faculty of Computer Science and Cybernetics
Faculty of Computer Science and Cybernetics
Volodymyr
Viktorovych
Semenov
Computational Mathematics
Faculty of Computer Science and Cybernetics
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
Computational Mathematics
Faculty of Computer Science and Cybernetics