[SciPy-User] scipy.interpolate.rbf sensitive to input noise ?

[denis]

Robert, Josef,
  thanks much for taking the time to look at RBF some more.
Summary, correct me:
    A - smooth*I in rbf.py is a sign error (ticket ?)
    for gauss, start with A + 1e-6*I  to move eigenvalues away from 0
    others have pos/neg eigenvalues, don't need smooth.

Looking at Wikipedia Tikhonov (thanks Josef) reminded me
of lstsq_near on advice.mechanicalkern:
minimize |Ax-b| and w|x| together, i.e. Tikhonov with Gammamatrix = I.

But for RBF, why minimize |x| ?  don't we really want to minimize
    |Ax-b|2 + w2 xRx
where R is a roughness penalty ?
On a regular grid Rij ~ Laplacian smoother, not much like I.
Here I'm over my head;
any ideas for a q+d roughness matrix for scattered data ?
(just for fun -- for RBF we've reached the point of diminishing
returns).

cheers
  -- denis