Cоnvex optimization methods
Course: Mathematical Methods of Artificial Intelligence
Structural unit: Faculty of Computer Science and Cybernetics
Title
Cоnvex optimization methods
Code
ДВС.3.01.03
Module type
Вибіркова дисципліна для ОП
Educational cycle
Second
Year of study when the component is delivered
2023/2024
Semester/trimester when the component is delivered
4 Semester
Number of ECTS credits allocated
4
Learning outcomes
LO14. Apply innovative approaches in computer science and
information technology.
Form of study
Full-time form
Prerequisites and co-requisites
1. Successful mastering of courses: discrete mathematics; linear algebra and analytic
geometry; Operations Research; mathematical analysis; functional analysis.
2. Knowledge: basic concepts and methods of mathematical programming; basics of convex
analysis; basic information on the theory of second-order curves; theory of boundaries and functions
of a real variable.
Course content
The aim of the discipline is mastering the knowledge and skills of theory and methods of non-smooth optimization in construction and analysis of algorithms for solving applied optimization problems.
As a result of studying the discipline the student must:
know: basic concepts of non-smooth optimization methods and conditions of their effective application in applied optimization problems;
be able to: formulate mathematical models of applied optimization problems and use subgradient methods to minimize non-smooth convex functions to solve them.
Recommended or required reading and other learning resources/tools
1. Glushkov V.M. Basics of Paperless Informatics. – М.: Nauka, 1982. – 552 p.
2. Shor N.Z. Minimization Methods for Non-Differentiable Function. – Kiev: Naukova Dumka, 1979. – 200 p.
3. Polyak B.T. Introduction to Optimization. – М.: Nauka, 1983. – 384 p.
4. Stetsyuk P.I. Ellipsoid Methods and r-Algorithms. – Chisinau: Evryka, 2014. – 488 p.
Planned learning activities and teaching methods
Lectures, exam.
Assessment methods and criteria
- Intermediate assessment:
1. Lection 1-13 Work: LO1.1, LO2.1, LO2.2, LO3.1, LO4.1 — 60 points.
- Final assessment (exam):
- maximum number of points: 40 points;
- learning outcomes to be assessed: LO1.1, LO1.2, LO2.1, LO2.2, LO3.1.
- exam form: written.
- 3 written assignments (3 theoretical questions).
Students that earned less than 36 points are not admitted to exam.
Language of instruction
Ukrainian
Lecturers
This discipline is taught by the following teachers
Departments
The following departments are involved in teaching the above discipline