[[Imported from SVN: r9669]]

[[Imported from SVN: r9668]]

[[Imported from SVN: r9667]]

[[Imported from SVN: r9666]]

That way, they can at least be looked at.  You won't see the intra-element
details, but frequently they are not even all this important.  Proper
subsampling of higher-order functions is more difficult and has to wait
for another day.

[[Imported from SVN: r9665]]

[[Imported from SVN: r9664]]

[[Imported from SVN: r9663]]

This will be generalized to take any Lagrange basis, to allow some form
of visualization for higher-order functions.

[[Imported from SVN: r9662]]

[[Imported from SVN: r9661]]

[[Imported from SVN: r9660]]

[[Imported from SVN: r9659]]

[[Imported from SVN: r9658]]

[[Imported from SVN: r9657]]

This prepares for a later switch to a second-order basis

[[Imported from SVN: r9656]]

[[Imported from SVN: r9655]]

[[Imported from SVN: r9654]]

[[Imported from SVN: r9653]]

Use its size rather than the number of grid vertices

[[Imported from SVN: r9652]]

Adjust values of the shearing/wrinkling problem to the values given in the Taylor/Bertoldi/Steigmann paper

[[Imported from SVN: r9651]]

This move more than halfs the size of this file, and makes it *much* easier
to understand.  If we ever need the gradient code we can still recover it
from the 2.3-2 branch.

[[Imported from SVN: r9650]]

[[Imported from SVN: r9649]]

[[Imported from SVN: r9648]]

[[Imported from SVN: r9618]]

[[Imported from SVN: r9617]]

[[Imported from SVN: r9616]]

[[Imported from SVN: r9615]]

[[Imported from SVN: r9614]]

[[Imported from SVN: r9601]]

[[Imported from SVN: r9588]]

[[Imported from SVN: r9587]]

[[Imported from SVN: r9586]]

Assuming that the AverageDistanceAssembler can provide it.

This is for the Gram-Schmidt solver only.  The old Gauss-Seidel
code will still use a full matrix.

[[Imported from SVN: r9585]]

[[Imported from SVN: r9584]]

viz.:

field_type
blocklevel
operator=(scalar)

[[Imported from SVN: r9583]]

[[Imported from SVN: r9582]]

CG seems to converge to quickly.  If too few iterations are performed,
then ADOL-C has trouble determining the correct second derivatives.
Since the Gram-Schmidt solver is a direct solver, OTOH, the derivatives
should be perfect.  It is also faster than the Gauss-Seidel solver I
was previously using.

[[Imported from SVN: r9581]]

Nice, because it works for rank-deficient matrices.

And still fast enough for the systems I am considering here.

[[Imported from SVN: r9580]]

[[Imported from SVN: r9579]]

I hoped that this would be faster.  However, it seems to deterioate the
convergence of the outer trust region algorithm.  Apparently, the steps
taken by a CG make it difficult for ADOL-C to compute the correct
derivatives.

[[Imported from SVN: r9576]]

We have the correct value in the variable 'oldEnergy' anyway.

[[Imported from SVN: r9575]]