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Last update: doc. RNDr. Václav Kučera, Ph.D. (15.01.2019)
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Last update: prof. Ing. Miroslav Tůma, CSc. (08.10.2017)
To understand basic ideas related to sparse matrices in direct methods as well as approximate factorizations used as preconditioners in iterative methods. |
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Last update: prof. Ing. Miroslav Tůma, CSc. (25.09.2020)
Needed to get credits:
• students will independently prepare one or two presentations for the other students based on the agreement with the lecturer It necessary, everything will be distant.
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Last update: doc. RNDr. Václav Kučera, Ph.D. (15.01.2019)
T. Davis. Direct Methods for Sparse Linear Systems. Fundamentals of Algorithms, 2. Society for Industrial and Applied Mathematics (SIAM), Philadelphia, PA, 2006.
G. Meurant. Computer Solution of Large Linear Systems. Studies in Mathematics and its Applications, 28. North-Holland Publishing Co., Amsterdam, 1999.
I.S. Duff, A. Erisman, J. Reid. Direct methods for Sparse Matrices, Clarenton Press, Oxford University Press, 1986.
J. Dongarra, I.S. Duff, D. Sorensen, H. A. van der Vorst. Solving Linear Systems on Vector and Shared Memory Computers, SIAM, 1991.
A.George, J. Liu: Computer Solution of Sparse Positive Definite Systems, Prentice-Hall, 1981.
J. Liu: The role of elimination trees in sparse factorization, SIAM. J. Matrix Anal. Appl. 11 (1990), 134-172.
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Last update: prof. Ing. Miroslav Tůma, CSc. (25.09.2020)
Lectures and tutorials in a distant way. |
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Last update: prof. Ing. Miroslav Tůma, CSc. (25.09.2020)
Examination according to the syllabus. Posssibly distant.
• students will be asked one thematically general question • students will have enough time to prepare their answer • they should show basic understanding to parallel matrix computations • examining persons can pose subquestions related to the main question
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Last update: prof. Ing. Miroslav Tůma, CSc. (03.10.2017)
1. Basic terminology from computers, factorizzations and computational complexity.
2. Direct methods, their representation by graphs and sparse matrices in applications.
3. Graph interpretation of Cholesky factorization and LU decomposition. Theoretical basis and
algorithmic synthesis of sparse direct solvers.
4. Direct and approximate methods. The use of approximate decompositions in preconditioning.
Sparse QR decomposition. Sparse decompositions of symmetric indefinite systems.
5. Implementations of direct and approximate solvers.
The exam will test basic understanding to the subject described in this sylabus. The exam can preceed getting credits. The credits will be given based on the student activity. In order to ge the credits, at least one talk based on an independent work offered by the lecturer should be given. |
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Last update: prof. Ing. Miroslav Tůma, CSc. (16.05.2018)
Premiliminary for this course is only a basic knowledge of linear algebra that corresponds, for example, to NMAG101. Some knowledge of basic graph theory is an advantage but not a necessity. |