APPROXIMATION METHODS B
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- Assessment methods
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Basic course in numerical analysis.
Oral examination and project implemented in Matlab.
Students will acquire the basic knowledge to solve linear systems from real applications.
- Stationary iterative methods: regular splittings, M-matrices, Jacobi and Gauss-Seidel methods with relaxation and their block variants.
- Multigrid methods: two-grid method, geometric and algebraic formulation, Galerkin conditions, convergence and optimality results, Ruge-Stuben theory, V-cycle and W-cycle.
- Krylov methods: Richardson method, preconditioning, conjugate gradients, MINRES, GMRES, Lanczos and Arnoldi factorizations.
- Stationary iterative methods: regular splittings, M-matrices, Jacobi and Gauss-Seidel methods with relaxation and their block variants.
- Multigrid methods: two-grid method, geometric and algebraic formulation, Galerkin conditions, convergence and optimality results, Ruge-Stuben theory, V-cycle and W-cycle.
- Krylov methods: Richardson method, preconditioning, conjugate gradients, MINRES, GMRES, Lanczos and Arnoldi factorizations.
Front lectures with whiteboard.
Meeting by appointment.
Professors
Borrowers
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Degree course in: MATHEMATICS