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]]