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**systems of linear equations**

St.Petersburg State University Faculty of Physics Earth physics department Ferienakademie 2008 Multigrid methods for systems of linear equations Andrey Ponomarenko Sarntal

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**Iterative methods for linear systems, relaxation schemes **

OUTLINE The way to multigrid… Iterative methods for linear systems, relaxation schemes Coarse grid correction V-multigrid method Full-multigrid method Ferienakademie 2008

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**Efficiency of algorithm, Possibility of parallel computing**

The way to multigrid… Algorithm should not do a lot of empty work Slow process – a lot of calculations The convergence - it should be quite quick So, multigrid method – for high frequently components we use fine grids, for slow components we use coarse grids Efficiency of algorithm, Possibility of parallel computing Ferienakademie 2008

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**Iterative methods for linear systems**

1) At first, some basics: Consider Let v be an approximation to u Residual equation Residual correction Ferienakademie 2008

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**2) Iteration in the matrix form**

Let D – is diagonal matrix L and U – are lower and upper parts of A Then Now the iteration is - the iteration error Ferienakademie 2008

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**Now consider the iteration properties on the next equation in 1D:**

One-dimensional boundary problem Introduce the grid: Ferienakademie 2008

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**Then, with the help of Taylor series:**

Then our problem can be presented in grid form: Ferienakademie 2008

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**Now we have The discrete model problem:**

symmetric, positive defined So, we can used iteration scheme showed above Consider relaxation scheme: Ferienakademie 2008

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**Relaxation schemes The equation:**

1) Jacobi Method (simultaneous displacements): 2) Weighted Jacobi Method : Then as we have before D – is diagonal matrix L and U – are lower and upper parts of A Ferienakademie 2008

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**Short observation about eigenvalues and eigenvectors:**

λ – is eigenvalue of matrix B, w – its ascociated eigenvector Eigenvectors are linearly independent, they form the basis, and for any v it is possible to write (N×N matrix) Now we return to relaxation: - for one iteration - we’ll have after n iterations - initial error Ferienakademie 2008

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**Let’s make convergence analysis on weighted Jakobi on 1D model**

Ferienakademie 2008

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**Let’s make convergence analysis on weighted Jakobi on 1D model**

Expand initial error in the terms of eigenvectors: So, after M iterations we have: (remember ) The k-th mode of the error is reduced by at each iteration Ferienakademie 2008

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**Relaxation suppress eigenmodes unevenly**

If 0<w<1, Low frequencies are damped bad High frequencies: It can be shown, sm. fact. is the best when , it equals Ferienakademie 2008

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**So, many relaxation schemes has smoothing property, but smooth modes of error are damped very slowly**

For instance (weighted Jakobi method): initial error: after 35 iterations: Ferienakademie 2008

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**By using coarse grids we can use the smoothing property in good advantage!**

So why?? Relaxation on the coarse-grid is much cheaper (1/2 – 1D, ¼ - 2D e.t.c ) Relaxation on the coarse-grid has better convergence ( 1-O(4h2) inst.of 1-O(h2) ) After relaxing on the fine grid, the error will be smooth. On the coarse grid this error appears more oscillatory, and relaxation will be more effectively Ferienakademie 2008

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**Coarse Grid Correction**

We have now the idea of The question appears: how to map and Ferienakademie 2008

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**Mapping from the coarse grid to the fine grid:**

defined on Ferienakademie 2008

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**Mapping from the fine grid to the coarse grid:**

defined on (there are others methods of mapping, we don’t consider them) Ferienakademie 2008

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**The Coarse Grid Correction scheme:**

- is the “coarse-grid version” of operator A Ferienakademie 2008

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**V-Multigrid method IDEA: it is unnecessary to**

It is enough to make several relaxation and to obtain approximate solution We effectively relax high-frequency components To make slow frequencies smooth we use the next, coarse grid Gh G2h So, we have recursive use of CGC sheme. G4h G8h Ferienakademie 2008

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**Full-Multigrid method**

IDEA: the solution takes fewer iterations if the initial guess is good How to get a good initial guess: Interpolate coarse solution to the fine grid “Solve” the problem on the coarse grid first Use interpolated coarse solution as initial guess on fine grid Let’s use the V-multigrid cycle as the solver on each grid level. This defines the Full Multigrid (FMG) method Ferienakademie 2008

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**Some properties of convergence:**

Some properties of FMG method: 1) Full Multigtid method computes solution to the error of truncation The computational cost of FMG is O(Nd), where N – quantity of fine grid points, d – dimension of the problem Ferienakademie 2008

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**So, MULTIGRID METHODS - increasingly the right tool:**

Multigrid methods are effective algorithms Multigrid method FMG is an optimal (O(N)) Multigrid algotithms can be parallelized effecintly – it is another real interesting and big topic to present! Ferienakademie 2008

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References Briggs W.M. “Multigrid tutorial”. Presentation by Henson V.E. Center for applied scientific computing, Lawrence livermore National Laboratory. Stankova E., Zatevahin M. “Multigrid methods. Introduction in the standard methods.” St.Petersburg. In PDF, in russian. Borzi A. “Introduction to multigrid methods.” In PDF. Ferienakademie 2008

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**Thank you for attention!**

Ferienakademie 2008

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