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Course, academic year 2023/2024
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Practical Linear Algebra and Geometry - NALG086
Title: Praktická lineární algebra a geometrie
Guaranteed by: Department of Algebra (32-KA)
Faculty: Faculty of Mathematics and Physics
Actual: from 2018
Semester: summer
E-Credits: 8
Hours per week, examination: summer s.:4/2, C+Ex [HT]
Capacity: unlimited
Min. number of students: unlimited
4EU+: no
Virtual mobility / capacity: no
State of the course: cancelled
Language: Czech
Teaching methods: full-time
Teaching methods: full-time
Guarantor: doc. RNDr. Petr Somberg, Ph.D.
Classification: Mathematics > Algebra
Incompatibility : NMAI057, NMAI058
Interchangeability : NALG002
Is incompatible with: NUMP004
Annotation -
Last update: T_KA (23.05.2003)
Basic lecture of the first course bachelor study for mathematics - Financial Mathematics, Mathematic Methods of Information Security
Literature - Czech
Last update: T_KA (23.05.2003)

J. Bečvář, Vektorové prostory I, II, III, SPN Praha 1978, 1981, 1982

L. Bican, Lineární algebra a geometrie, Academia Praha 2000, ISBN 80-200-0843-8

L. Bican, Lineární algebra v úlohách, SPN Praha 1979

C. D. Meyer: Matrix Analysis and Applied Linear Algebra, SIAM 2000.

J. Tůma: Internetová skripta k přednášce Lineární algebra, http://adela.karlin.mff.cuni.cz/~tuma/linalg.htm .

Syllabus -
Last update: T_KA (19.05.2005)

1. Introduction to theory of linear codes. Generator and check matrix, weight and Hamming distance.

2. Bilinear and quadratic forms. Symmetric and antisymmetric bilinear forms, nullity, range, polar basis, Sylvester's law of inertia.

3. Inner product on real and complex vector spaces. Orthogonality, orthogonal complement, orthogonal and orthonormal basis, classical and modified Gram-Schmidt orthogonalization procedure, orthogonal matrix, unitary transformation, method of least squares.

4. Eigenvalues and eigenvectors. Eigenvalues and eigenvectors of a matrix and of a linear transformation, geometric interpretation and applications.

5. Decomposition of matrices. LU, QR, URV and SVD factorization. Cholesky factorization of a symmetric matrix.

6. Pseudoinverse matrices. Moore-Penrose pseudoinverse matrices.

7. Similarity of matrices and Jordan form of a matrix. Jordan block, existence and unicity of Jordan form.

8. Perron-Frobenius theory of non-negative matrices. Perron vector, primitive and stochastic matrices.

9. Analytic geometry in Euklidian spaces and affine spaces. Cartesian system of coordinates, angle, distance, subspaces and description of an affine space, relative position of subspaces.

 
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