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  Abbreviation unit / Course abbreviation Title Variant
Item shown in detail - course KMA/7TEMA  KMA / 7TEMA Matrix Theory Show course Matrix Theory 2023/2024

Course info KMA / 7TEMA : Course description

  • Course description , selected item
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Department/Unit / Abbreviation KMA / 7TEMA Academic Year 2023/2024
Academic Year 2023/2024
Title Matrix Theory Form of course completion Exam
Form of course completion Exam
Accredited / Credits Yes, 6 Cred. Type of completion Combined
Type of completion Combined
Time requirements lecture 2 [Hours/Week] practical class 2 [Hours/Week] Course credit prior to examination No
Course credit prior to examination No
Automatic acceptance of credit before examination No
Included in study average YES
Language of instruction Czech, English
Occ/max Status A Status A Status B Status B Status C Status C Automatic acceptance of credit before examination No
Summer semester 0 / - 4 / - 0 / 0 Included in study average YES
Winter semester 0 / - 0 / - 0 / - Repeated registration NO
Repeated registration NO
Timetable Yes Semester taught Summer semester
Semester taught Summer semester
Minimum (B + C) students not determined Optional course Yes
Optional course Yes
Language of instruction Czech, English Internship duration 0
No. of hours of on-premise lessons Evaluation scale A|B|C|D|E|F
Periodicity every year
Specification periodicity Fundamental theoretical course No
Fundamental course Yes
Fundamental theoretical course No
Evaluation scale A|B|C|D|E|F
Substituted course None
Preclusive courses KMA/TEMAT
Prerequisite courses N/A
Informally recommended courses N/A
Courses depending on this Course N/A
Histogram of students' grades over the years: Graphic PNG ,  XLS
Course objectives:
The course is devoted to advanced properties of matrices and to their applications.

Requirements on student
The course is completed with an exam, which consists of a written test (aimed at solving problems and examining basic definitions and theorems) and an oral part (devoted to discussing the test and to theoretical questions).



Content
1. Elementary operations with matrices.
2. Matrix norms.
3. Eigenvalues and eigenvectors.
4. Matrix similarity. Diagonalization.
5. Projections. Spectral decomposition.
6. Jordan normal form.
7. Matrix functions.
8. Singular value decomposition.
9. Pseudoinverse.
9. Special classes of matrices and their properties.
10. Positive matrices. Perron-Frobenius theorem.
12. Examples of applications of matrices in mathematics and physics.

Activities
Fields of study


Guarantors and lecturers
  • Guarantors: doc. Ing. Ondřej Turek, Ph.D. (100%), 
  • Lecturer: doc. Ing. Ondřej Turek, Ph.D. (100%), 
  • Tutorial lecturer: Mgr. Věra Ferdiánová, Ph.D. (100%), 
Literature
  • Basic: Fiedler, M. Speciální matice a jejich použití v numerické matematice. SNTL Praha, 1981. ISBN cnb000135300.
  • Basic: J. Kostra, M. Pomp. Teorie matic. skriptum Ostravské univerzity. ISBN 80-7042-743-4.
  • Recommended: V. Havel, J. Holenda. Lineární algebra. Praha, 1984. ISBN cnb000010019.
  • Recommended: C.D. Meyer. Matrix Analysis and Applied Linear Algebra. SIAM, 2000. ISBN 978-0-898714-54-8.
  • Recommended: Borůvka, O. Základy teorie matic. Academia, 1974. ISBN cnb000429496.
  • On-line library catalogues
Time requirements
All forms of study
Activities Time requirements for activity [h]
Being present in classes 52
Consultation of work with the teacher/tutor (incl. electronic) 10
Self-tutoring 10
Scientific text studying in the Czech language 20
Continuous tasks completion (incl. correspondence tasks) 24
Scientific text studying in a foreign language 10
Preparation for an exam 24
Total 150

Prerequisites

Competences - students are expected to possess the following competences before the course commences to finish it successfully:
The student has a basic knowledge of linear algebra.

Learning outcomes

Knowledge - knowledge resulting from the course:
Student knows matrix norms and relations between them, understands the eigenvalue and eigenvector problem, knows and understands spectral decomposition, understands the notion of matrix similarity, is familiar with Jordan normal form of a matrix and its derivation, knows the Perron-Frobenius theorem, understands matrix functions, knows examples of practical applications of matrices, and is able to study related specialized literature.
Skills - skills resulting from the course:
Student can localize eigenvalues, is able to find spectral decomposition of a given matrix, can transform a matrix to its Jordan form, is able to compute matrix functions and to use it for solving practical problems.

Assessment methods

Knowledge - knowledge achieved by taking this course are verified by the following means:
Continuous analysis of student´s achievements
Dialogue
Written examination

Teaching methods

Knowledge - the following training methods are used to achieve the required knowledge:
Monologic (explanation, lecture, briefing)
Dialogic (discussion, dialogue, brainstorming)
Working with text (coursebook, book)
 

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