This is a collection of many interacting patches that help to ensure
monotonicity of the TNNMG iteration and add more diagnostic output to
track the latter:

* Implement `ScalarSumFunctional::operator()` in a way that is
  consistent with the global energy evaluation. Unfortunately
  this means we cannot use the evaluation of the individual
  constrained functionals.
* Implement `derivative(ScalarSumFunctional)`. The functional
  is in general not differentiable, hence we return the minimal
  norm entry of the subdifferential or, equivalently, the projection
  of zero into the subdifferential. Finding a root of the
  subdifferential is equivalent to find a root of this single-valued
  map. Furthermore a subdifferential-aware bisection is equivalent
  to a bisection for the single-valued map.
* To ensure monotonicity of the coarse grid correction add a
  postprocessing step to the line search: After an approximate
  root of the derivative has been found, check if this decreases energy.
  If not, then half the step size until it does. Since we have a descent
  direction this must terminate. This step is necessary because the
  functional is not convex.
* When comparing energies take care to always evaluate it exactly the
  same way, to avoid non-monotonicity due to inconsistent evaluation
  algorithms.
* Track energy decrease of nonlinear smoother and coarse grid
  correction. While the latter is guaranteed by the above changes,
  it may happen that the smoother is not monotone - either because
  of local solves with uncontrolled inexactness (called exact in our terminalogy, arg...)
  or because of round-off-errors.
* Add more diagnostic output to the table:
  * Number of subdifferential evaluations during line search.
    Hint on interpretation: This will be at least 2+#bisectionSteps.
  * Number of functional evaluations during line search.
    Hint on interpretation: This will be 2+#reductionSteps,
    where #reductionSteps is the number of halving steps done
    to ensure monotonicity (see above).
  * Energy after TNNMG step with 16 digits.
  * Energy after nonlinear pre-smoothing with 16 digits.
  * If the nonlinear pre-smoother is not monotone:
    Relative monotonicity violation of the smoother with an additional
    `X` marker for easier grepping.