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Course, academic year 2025/2026
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Linear Algebra 2 - NMAI058
Title: Lineární algebra 2
Guaranteed by: Department of Applied Mathematics (32-KAM)
Faculty: Faculty of Mathematics and Physics
Actual: from 2019
Semester: summer
E-Credits: 5
Hours per week, examination: summer s.:2/2, C+Ex [HT]
Capacity: unlimited
Min. number of students: unlimited
4EU+: no
Virtual mobility / capacity: no
State of the course: taught
Language: Czech, English
Teaching methods: full-time
Guarantor: prof. Mgr. Milan Hladík, Ph.D.
doc. Mgr. Petr Kolman, Ph.D.
Class: Informatika Bc.
Classification: Mathematics > Algebra
Is incompatible with: NALG086, NUMP004, NUMP003
Annotation -
Continuation of MAI057 - special matrices, determinants, eigenvalues, examples of applications of linear algebra.
Last update: FIALA/MFF.CUNI.CZ (17.02.2010)
Course completion requirements -

The relevant information for the English section of this course in summer semester of 2025 can be found on the course web page: https://iuuk.mff.cuni.cz/~ipenev/NMAI058S2025.html

Last update: Penev Irena, Ph.D. (18.02.2025)
Literature -

W. Gareth. Linear Algebra with Applications. Jones and Bartlett Publishers, Boston, 4th edition, 2001.

C. D. Meyer. Matrix analysis and applied linear algebra. SIAM, Philadelphia, PA, 2000. http://www.matrixanalysis.com/DownloadChapters.html

G. Strang. Linear algebra and its applications. Thomson, USA, 4rd edition, 2006.

Last update: Hladík Milan, prof. Mgr., Ph.D. (22.11.2012)
Requirements to the exam -

The relevant information for the English section of this course in summer semester of 2025 can be found on the course web page: https://iuuk.mff.cuni.cz/~ipenev/NMAI058S2025.html

Last update: Penev Irena, Ph.D. (18.02.2025)
Syllabus -

Inner product spaces:

  • norm induced by an inner product
  • Pythagoras theorem, Cauchy-Schwarz inequality, triangle inequality
  • orthogonal and orthonormal system of vectors, Fourier coefficients, Gram-Schmidt orthogonalization
  • orthogonal complement, orthogonal projection
  • the least squares method
  • orthogonal matrices

Determinants:

  • basic properties
  • Laplace expansion of a determinant, Cramer's rule
  • adjugate matrix
  • geometric interpretation of determinants

Eigenvalues and eigenvectors:

  • basic properties, characteristic polynomial
  • Cayley-Hamilton theorem
  • similarity and diagonalization of matrices, spectral decomposition, Jordan normal form
  • symmetric matrices and their spectral decomposition
  • (optionally) companion matrix, estimation and computation of eigenvalues: Gershgorin discs and power method

Positive semidefinite and positive definite matrices:

  • characterization and properties
  • methods: recurrence formula, Cholesky decomposition, Gaussian elimination, Sylvester's criterion
  • relation to inner products

Bilinear and quadratic forms:

  • forms and their matrices, change of a basis
  • Sylvester's law of inertia, diagonalization, polar basis

Topics on expansion (optionally):

  • eigenvalues of nonnegative matrices
  • matrix decompositions: Householder transformation, QR, SVD, Moore-Penrose pseudoinverse of a matrix

Last update: Hladík Milan, prof. Mgr., Ph.D. (28.03.2022)
 
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