| Course Unit Code | 470-2201/01 |
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| Number of ECTS Credits Allocated | 4 ECTS credits |
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| Type of Course Unit * | Compulsory |
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| Level of Course Unit * | First Cycle |
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| Year of Study * | First Year |
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| Semester when the Course Unit is delivered | Summer Semester |
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| Mode of Delivery | Face-to-face |
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| Language of Instruction | Czech |
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| Prerequisites and Co-Requisites | Course succeeds to compulsory courses of previous semester |
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| Name of Lecturer(s) | Personal ID | Name |
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| JAH02 | RNDr. Pavel Jahoda, Ph.D. |
| KAL0063 | prof. RNDr. René Kalus, Ph.D. |
| Summary |
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| Linear algebra is a basic tool of formulation and effective solution of technical problems. The students will get knowledge of basic concepts and computational skills of linear algebra. |
| Learning Outcomes of the Course Unit |
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| Many engineering problems lead to solution of large-scale systems of linear equations. The aim of this course is to introduce fundamental notions of linear algebra and relate them to applications in electrical engineering. First we shall learn how to solve real and complex systems of linear equations by Gauss elimination method. The systems arises in the analysis of electrical circuits. In an intuitive manner we shall introduce notions such as base of a vector space, linear transformation and using them we will formulate basic linear problems. In the second part of the course, we shall focus on quadratic forms, which are closely related e.g. to electrical potential energy. Further we shall study orthogonality of functions, on which e.g. Fourier analysis of signals rely. Finally, we shall introduce spectral theory with applications to analysis of resonances. |
| Course Contents |
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Systems of linear algebraic equations (SLAE)
Matrix calculus
Linear vector spaces (LVS)
Solvability of SLAE
Linear mappings of LVS
Linear, bilinear(, and multilinear) forms
Quadratic forms
Determinants
Scalar product, orthogonality of vectors
Orthogonal projections
Spectral analysis of square matrices |
| Recommended or Required Reading |
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| Required Reading: |
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G. Strang, Video lectures of Linear Algebra on MIT.
R.A. Horn, C.R. Johnson, Matrix Analysis. Cambridge University Press 1990.
Y. Saad. Iterative Methods for Sparse Linear Systems. SIAM 2003.
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Z. Dostál, Lineární algebra, VŠB-TU Ostrava 2000.
G. Strang, Video lectures of Linear Algebra on MIT.
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| Recommended Reading: |
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G.H. Golub, C.F. Van Loan, Matrix Computations. The Johns Hopkins University Press 2013.
L.N. Trefethen, D. Bau. Numerical Linear Algebra. SIAM 1997.
J. Liesen, Z. Strakoš, Krylov Subspace Methods: Principles and Analysis. Oxford University Press 2012. |
Z. Dostál, L. Šindel, Lineární algebra pro kombinované a distanční studium, VŠB-TU Ostrava 2003
G.H. Golub, C.F. Van Loan, Matrix Computations. The Johns Hopkins University Press 2013 |
| Planned learning activities and teaching methods |
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| Lectures, Tutorials |
| Assesment methods and criteria |
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| Task Title | Task Type | Maximum Number of Points
(Act. for Subtasks) | Minimum Number of Points for Task Passing |
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| Credit and Examination | Credit and Examination | 100 (100) | 51 |
| Credit | Credit | 30 (30) | 10 |
| Test 1 | Written test | 15 | 0 |
| Test 2 | Written test | 15 | 0 |
| Examination | Examination | 70 | 21 |