Modern problems of computational mathematics
Course: Applied mathematics
Structural unit: Faculty of Computer Science and Cybernetics
Title
Modern problems of computational mathematics
Code
ДВС.1.01
Module type
Обов’язкова дисципліна для ОП
Educational cycle
Second
Year of study when the component is delivered
2022/2023
Semester/trimester when the component is delivered
3 Semester
Number of ECTS credits allocated
8
Learning outcomes
PLO1. Be able to use 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.
PLO10. Be able to build models of physical and production processes, design of storage and data space, knowledge base, using charting techniques and standards for information systems development.
Form of study
Prerequisites and co-requisites
To successfully learn the discipline “Modern problems of computational mathematics” the student should satisfy the following requirements.
They have successfully passed the courses Calculus and Linear Algebra.
They know (a) fundamentals of methods for solving systems of linear algebraic equations.
They can (a) apply fundamentals of methods for solving systems of linear algebraic equations to solve practical problems.
They should be able to (a) seek information in the Internet.
Course content
Block 1. Fundamentals of iterative methods
Direct and iterative methods
Types of convergence of iterative methods
Basic iterative methods
Jacobi and Gauss–Seidel methods
Successive over-relaxation method
Symmetric Successive over-relaxation method
Control work
Модуль 2. Acceleration procedures
Polynomial acceleration
Optimal Chebyshev acceleration
Rate of convergence of optimal Chebyshev acceleration
Chebyshev acceleration with estimations of eigenvalues
Adaptive Chebyshev acceleration using special norms
Computation of new parameters in adaptive Chebyshev acceleration using special norms
Steepest descent method
Arbitrary directions of descent
Conjugate gradients method
Procedures ORTHOMIN, ORTODIR, ORTHORES
Versions of conjugate gradient method
Technology for sparse matrices
Applications of applied iterative methods
Control work
Recommended or required reading and other learning resources/tools
1. Kheigeman L., Iang D. Prikladnye iteratsionnye metody. — M.: Mir, 1986.
2. Golub Dzh., Van Loun. Matrichnye vychisleniia. — M.: Mir, 1999.
3. Ortega Dzh. Vvedenie v parallel-nye i vektornye metody resheniia sistem. — M.: Mir, 1991.
4. Saad Iu. Iteratsionnye metody dlia razrezhennykh lineinykh sistem. V 2-kh tomakh — M.: Izdatel-stvo Moskovskogo universiteta, 2013.
5. Kelley C.T. Iterative Methods for Linear and Nonlinear Equations. ¬ In: Frontiers in Applied Mathematics —. SIAM, Philadelphia, N 16, 1995.
6. Kelley C.T. Iterative Methods for Optimization. ¬ In: Frontiers in Applied Mathematics —. SIAM, Philadelphia, N 18, 1999.
7. Nurminskii E. A. Chislennye metody resheniia determinirovannykh i stokhasticheskikh minimaksnykh zadach. Kiev: Nauk. dumka, 1979. 159 s.
8. Lyashko S. I. Generalized optimal control of linear systems with distributed parameters. Boston/Dordrecht / London: Kluwer Academic Publishers, 2002. 466 p.
..
Planned learning activities and teaching methods
Lectures, independent work, recommended literature processing, homework.
Assessment methods and criteria
Intermediate assessment:
The maximal number of available points is 60.
Test work no. 1: RN 1.1, RN 1.2 – 30/18 points.
Test work no. 2: RN 1.1, RN 1.2 – 30/18 points.
Final assessment (in the form of final test):
The maximal number of available points is 40.
The results of study to be assessed are RN 1.1, RN 1.2, RN 2.1, and RN 3.1.
The form of final test: writing.
The types of assignments are 3 writing assignments (2 theoretical and 1 practical).
Language of instruction
Ukrainian
Lecturers
This discipline is taught by the following teachers
Departments
The following departments are involved in teaching the above discipline