This makes the methods AverageDistanceAssembler::assembleHessian
compile again.  And it should be a wee bit faster two, because
matrix symmetry is now exploited better.

These Hessians are always symmetric, and we should really
exploit this.

Additionally, it seems that the method was never used anyway.

[[Imported from SVN: r9588]]

[[Imported from SVN: r9584]]

[[Imported from SVN: r9441]]

[[Imported from SVN: r9296]]

[[Imported from SVN: r9295]]

simplify the code by making the AverageDistanceAssembler accept the interpolation weights in a std::vector of FieldVectors

[[Imported from SVN: r7885]]

[[Imported from SVN: r7838]]

We need the extra flexibility for higher-order functions.
Run-time loss is around 1% -- that's okay.

[[Imported from SVN: r7836]]

[[Imported from SVN: r7611]]

and barycentric coordinates.  This is easy because we know the
number of element corners at compile time (I currently don't
see support for non-simplex elements at all).  This saves 
time, but not much: about 1%.

[[Imported from SVN: r7550]]

[[Imported from SVN: r7353]]

[[Imported from SVN: r7348]]

[[Imported from SVN: r7286]]

rename method assembleHessian of AverageDistanceAssembler to assembleEmbeddedHessian, to make clearer what it does

[[Imported from SVN: r7175]]

method to compute the actual Hesse matrix.  For some strange reason this slows down convergence, rather than speeding it up.  to be investigated

[[Imported from SVN: r7173]]

a separate name.  We cannot rely on overloading for different arguments,
because embedded and non-embedded tangent vectors may have the same type.

[[Imported from SVN: r7098]]

... instead of coordinates in the embedding space.  The usual problem
is that when working in the embedding space, a projection onto the
trust-region makes the iterate leave the tangent space.

[[Imported from SVN: r7035]]

[[Imported from SVN: r5931]]

[[Imported from SVN: r5667]]

remove old code that uses the derivative of the distance functions (instead of the derivative of the squared distance function)

[[Imported from SVN: r5666]]

[[Imported from SVN: r5664]]

[[Imported from SVN: r5599]]

[[Imported from SVN: r5597]]

[[Imported from SVN: r5595]]

rewrite the gradient assembler such as to demand the derivative of the _squared_ distance from the target space.  Unlike the distance itself, the squared distance is differentiable at zero

[[Imported from SVN: r5546]]

implement matrix assembly by simply returning the identity matrix.  This should a sort of projected gradient descent.  It is too late at night for me to compute the actual matrix

[[Imported from SVN: r5545]]

[[Imported from SVN: r5542]]

[[Imported from SVN: r5531]]

[[Imported from SVN: r5529]]

[[Imported from SVN: r5527]]

[[Imported from SVN: r5516]]

[[Imported from SVN: r5513]]

[[Imported from SVN: r5324]]

[[Imported from SVN: r4138]]