Paper VI visual guide · Visual 18 of 42 · DIRICHLET GRID / THE MISSING FACTOR OF h

A Green kernel is an inverse with units

Traditional Part I: Theorem 11.1. Exact nodal Green reconstruction for this one-dimensional stencil.

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Take three interior nodes \(x_i=i/4\) on \([0,1]\), with zero boundary values and \(h=1/4\). The discrete negative second derivative is

\[ L_h=\frac1{h^2}{\begin{pmatrix}2&-1&0\\-1&2&-1\\0&-1&2\end{pmatrix}}. \]

For the continuous Dirichlet problem, the Green function is \(G(x,y)=\min(x,y)-xy\). At these nodes the exact comparison is

\[ L_h^{-1}=h\,[G(x_i,x_j)]_{i,j=1}^3 =\frac1{64}{\begin{pmatrix}3&2&1\\2&4&2\\1&2&3\end{pmatrix}}. \]
A piecewise-linear Green function is paired with a scaled inverse matrix.
Original drawing. Read the description below; expand the labels and data for selectable text.

Reading the drawing

The left graph samples one Green-function column at grid nodes. The matrix at right is the displayed scaled finite inverse. The equation beneath it keeps the grid-spacing factor between the nodal Green values and the inverse operator.

Drawing labels and data 21 labels

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  • 0
  • 0.25
  • 0.5
  • 0.75
  • 1
  • 0
  • 0.125
  • 0.25
  • \(x\)
  • \(G(x,1/2)\)
  • \(64 L_h^{-1}\), \(h=1/4\)
Drawing grid 1 · rows top to bottom, columns left to right
Row / columnColumn 1Column 2Column 3
Row 1\(3\)\(2\)\(1\)
Row 2\(2\)\(4\)\(2\)
Row 3\(1\)\(2\)\(3\)
  • \(L_h^{-1}=hG_{\rm nodes}\)

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Evidence note from the source edition

The tent is the central column of the continuous Green kernel. It has nodal values \((1,2,1)/8\); its discrete inverse column is \((1,2,1)/32\), smaller by the factor \(h\).

A forcing vector is not a quadrature-weighted measure

For sampled forcing \(f_j\), the finite solution is

\[ u_i=\sum_j(L_h^{-1})_{ij}f_j =h\sum_jG(x_i,x_j)f_j. \]

The factor \(h\) converts samples into the quadrature weights appropriate to this formula. Dropping it multiplies the computed solution by four on the displayed grid.

A discrete unit coordinate vector and a discretized unit point mass use different normalizations. For \(f=e_2\), the result is the second inverse column. For \(f=e_2/h\), it is the unscaled tent sampled at the nodes. Either convention can be useful, but it must be specified before comparing answers.

Exact checks multiply \(L_h\) by the proposed inverse at \(1,3,7,\) and \(15\) interior nodes. The general identity is the manuscript theorem. These finite checks do not extend it to arbitrary meshes, dimensions, or boundary conditions.

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An inverse kernel carries a measure convention. A correct-looking tent can give a wrong solution if the grid weight is omitted.

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