Explore the Multiple Grid Method: Get Accurate Solution Faster

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SUMMARY

The discussion focuses on the Multiple Grid Method, a numerical technique used to solve linear systems of differential equations efficiently. Participants confirm that the method combines coarse and fine grids to achieve both accuracy and fast convergence. The finite difference method is employed to discretize the boundary problem, leading to a linear equation system represented by matrix A_h. The conversation also touches on the Jacobi method for iterative approximation and the significance of eigenvalues and eigenvectors derived from Fourier analysis.

PREREQUISITES
  • Understanding of linear systems and differential equations
  • Familiarity with finite difference methods for discretization
  • Knowledge of eigenvalues and eigenvectors in matrix theory
  • Basic principles of Fourier analysis and spectrum decomposition
NEXT STEPS
  • Study the Jacobi method for iterative solutions of linear systems
  • Learn about the application of Fourier transforms in numerical methods
  • Explore the theory behind the Multiple Grid Method in numerical analysis
  • Investigate error analysis techniques in numerical approximations
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Mathematicians, numerical analysts, and engineers involved in computational mathematics, particularly those focused on solving differential equations and optimizing numerical methods for efficiency.

  • #31
mathmari said:
Ahh. What have I done differently as at the V-cycle shema?

So, what I did has no specific shema?

We applied the 2-grid method (ZG or Zweigitterverfahren) that is specified on page 363.
The multigrid method (MV or Mehrgitter-V-cyclus) is specified on page 366. (Thinking)
 
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  • #32
I like Serena said:
We applied the 2-grid method (ZG or Zweigitterverfahren) that is specified on page 363.
The multigrid method (MV or Mehrgitter-V-cyclus) is specified on page 366. (Thinking)

Yes, but which is the difference with what I did? (Wondering)
 
  • #33
mathmari said:
Yes, but which is the difference with what I did? (Wondering)

The Mehrgitter-V-cyclus doesn't have a residue $r$ or an error $e$ does it?
Instead it just has an approximation $v_H$ that is refined through coarser and finer grids. (Thinking)
 
  • #34
I like Serena said:
The Mehrgitter-V-cyclus doesn't have a residue $r$ or an error $e$ does it?
Instead it just has an approximation $v_H$ that is refined through coarser and finer grids. (Thinking)

Do we not have the residue problem at the step 2 if we are not on the coarsest grid, i.e. if we don't have $H=2^jh$ and so we calculate $f_{2h}$ and $v_{2h}$? (Wondering)
 
  • #35
Could you explain to me the natrices $I_h^{2h}$ and $I_{2h}^h$ and especially the graphs Fig. 25 and Fig. 26? (Wondering)

Consider the Fig. 25: Do we consider three points on the fine grid and we take from there the average and the result is then one point on the coarse grid, i.e. the first box? (Wondering)
 
  • #36
mathmari said:
Do we not have the residue problem at the step 2 if we are not on the coarsest grid, i.e. if we don't have $H=2^jh$ and so we calculate $f_{2h}$ and $v_{2h}$? (Wondering)

Ah yes. I overlooked that. (Tmi)

Turns out that $v_{2h}$ is actually a residue instead of an actual approximation on the coarser grid.

mathmari said:
Could you explain to me the natrices $I_h^{2h}$ and $I_{2h}^h$ and especially the graphs Fig. 25 and Fig. 26? (Wondering)

Consider the Fig. 25: Do we consider three points on the fine grid and we take from there the average and the result is then one point on the coarse grid, i.e. the first box? (Wondering)

In figure 25 we want to keep only about half of the points, so that the calculations become easier.
But we don't just want to discard the information that is contained in the points.
So we pick which points we want to keep, and we take a weighted average at a ratio of 1:2:1 with the neighboring points that are discarded.
In the example of figure 25 we keep points 2 and 4 while discarding points 1, 3, and 5.
Since we don't just want to throw the information away, we replace points 2 and 4 by a weighted average that includes points 1, 3, and 5. (Thinking)

In figure 26 we go into the other direction: coarser to finer grid.
In the example we have only 2 points, but we need to get back to 5 points.
To find the missing points we interpolate linearly between the points we have.
Points 2 and 4 remain the same, point 1 is found as the average of 0 and point 2, and point 3 is found by taking the average of point 2 and 4. (Thinking)
 
  • #37
Thank you so much for your help! (Bow) (Sun)
 

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