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Last update: G_M (16.05.2012)
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Last update: G_M (27.04.2012)
a review of basic computational tools, practical excersises |
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Last update: doc. RNDr. Václav Kučera, Ph.D. (29.10.2019)
Credit is obtained for participation in exercises and a computer test. The nature of the examination of the subject excludes repetition of the examination, |
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Last update: doc. RNDr. Václav Kučera, Ph.D. (29.10.2019)
Deuflhard P. and Hohmann A.: Introduction to Scientific Computing, 2nd edition, Springer, 2002
Quarteroni A., Sacco R. and Saleri F.: Numerical mathematics, Springer, 2000
Tebbens J., Hnětýnková I., Plešinger M., Strakoš Z. and Tichý P.: Analýza metod pro maticové výpočty. Základní metody. Matfyz press, Praha, 2012 |
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Last update: G_M (27.04.2012)
The course consists of lectures in a lecture hall and exercises in a computer laboratory. |
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Last update: G_M (27.04.2012)
Examination according to the syllabus. |
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Last update: G_M (27.04.2012)
Solving liner systems, direct methods: Gauss elimination, LU-decomposition, pivoting, Cholesky decompositon.
Least Squares: data fitting, linear least squares, normal equation, pseudoinverse, QR-decomposition.
Nonlinear systems: Fixed Point Theorem (contraction mapping), Newton's Method, Newton-like methods.
Function minimization: Nelder-Mead Method, Method of Steepest Descent, Conjugate Gradient Method.
Interpolation: Lagrange Interpolating Polynomial, Chebyshev Polynomial, splines.
Ordinary Differential Equations: initial value problem, Euler Method, implicit Euler Method, Runge-Kutta Method.
Eigenvalue problems: a primer (eigenvalue, eigenvector, Characteristic Polynomial, multiplicity, Similar Matrices, Jordan canonical form), Power Method, Inverse iteration, QR algoritmus.
Iterative Methods (linear systems): large sparse matrices, Gauss-Seidel Method, Successive Overrelaxation Method, Conjugate Gradient Method, preconditioning. |
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Last update: prof. RNDr. Vladimír Janovský, DrSc. (22.02.2019)
basic knowledge of calculus and linear algebra |