Numerical methods of approximation
Course: Econophysics
Structural unit: Faculty of Radiophysics, Electronics and Computer Systems
Title
Numerical methods of approximation
Code
ОК 13
Module type
Обов’язкова дисципліна для ОП
Educational cycle
First
Year of study when the component is delivered
2022/2023
Semester/trimester when the component is delivered
3 Semester
Number of ECTS credits allocated
3
Learning outcomes
Learning outcomes involve the study of basic numerical methods and their characteristics: methods of approximation, differentiation and integration of functions, solving equations and systems of equations, solving ordinary differential equations and partial differential equations. Acquisition of practical skills of their application. Mastering the basic methods and rules of analysis of experimental results, working with the most common specialized mathematical packages, the basics of structuring large volumes of data.
Form of study
Full-time form
Prerequisites and co-requisites
The student must have basic knowledge of higher mathematics and practical skills in programming.
Course content
Approximation and interpolation of functions. Principles of approximation of deriva-tives. Principles of construction of formulas of numerical integration. Runge's princi-ple of approximate determination of the error of the numerical method. Methods for solving linear and nonlinear equations: bisection methods, chords, tangents, iteration method. Iterative methods for solving systems of equations. Basic methods for solving the Cauchy problem and the boundary value problem for ordinary differential equations. Differential schemes for equations of parabolic, elliptical and hyperbolic types with partial derivatives.
Basic methods of data processing in physics. Estimation of measurement error. Least squares method. An overview of modern means of automating numerical and sym-bolic calculations and data processing - computer algebra systems, software packages for statistical data processing and data visualization. Fundamentals of Data Science. Structuring data using cluster analysis.
Recommended or required reading and other learning resources/tools
1. Гаврилюк І.П., Макаров В.Л. Методи обчислювань: Підруч. для студ. вузів, які навч. за спец.
"Прикладна математика". – К. : Вища школа, 1995. — Ч. 1 . - 368 с.
2. Довгий Б.П., Ловейкін А.В., Вакал Є.С., Вакал Ю.Є. Сплайн-функції та їх застосування. – К.:
Видавничо-поліграфічний центр "Київський університет", 2017. – 122 с
3. Лященко М.Я., Головань М.С. Чисельні методи. — К.: Либідь, 2016. – 356с.
4. Фельдман Л.П., Петренко А.І., Дмитрієва О.А. Чисельні методи в інформатиці. – К.: Видавнича
група BHV, 2006. – 480 c.
5. Цегелик Г.Г. Чисельні методи. – Л: Видавничий центр ЛНУ ім. Івана Франка, 2004. – 408с.
Planned learning activities and teaching methods
Lectures, computational work, self-dependent work of students
Assessment methods and criteria
The grade for studying the course consists of grades for computational work (up to 80 points) and final modular tests (up to 20 points)
Language of instruction
Ukrainian
Lecturers
This discipline is taught by the following teachers
Tetiana
Mykhailivna
Proshchenko
Department of mathematics and Theoretical Radio Physics
Faculty of Radiophysics, Electronics and Computer Systems
Faculty of Radiophysics, Electronics and Computer Systems
Departments
The following departments are involved in teaching the above discipline
Department of mathematics and Theoretical Radio Physics
Faculty of Radiophysics, Electronics and Computer Systems