Rescale allocation layer

Nobody has claimed this yet.

Numerical allocation layers (e.g. `NumericalMarkowitz`) might generate weights that do not satisfy the constraints. There are the following reasons for it
* small floating point differences (e.g. w_i=-1e-8 with **w>=0** constraint)
* algorithm did not converge and the underlying solver did not raise any exception

It can become an issue when using a loss that is not defined for certain values of **w**.

The cleanest solution would be to have a new allocation layer that would be called after the original allocation layer. Ideally,
it would do the following
* Perform checks and potentially raise exception
* Rescale weights to satisfy the original constraints

Start by locating the allocation-layer entry point and the NumericalMarkowitz implementation mentioned in the issue, then inspect how constraints and solver convergence are currently handled. Done means a post-allocation layer checks constraint violations, handles unconverged results, and rescales weights where possible without accepting invalid values.

Written by the indexing model from the issue text.