Closed-form gradient for generic case

[klamike]

Theorem 3 of https://arxiv.org/pdf/2402.02596 shows that the gradient we are looking for (the objective of the completion wrt y) is always equal to $b - Ax$ where $x$ is a dual-optimal solution to the completion problem (corresponding to the equality constraints). That paper is only for LP but I think it holds more generally. If so we can compute the gradient without relying DiffOpt in the generic case.

That lets us continue despite jump-dev/DiffOpt.jl#282 not being merged yet. Note also that L2ODLL would rather use the QuadraticProgram for which objective sensitivity is not available (nor being worked on, AFAICT)

cc @andrewrosemberg @mtanneau

Need to be careful in the quadratic objective case.

[klamike]

The docs now have some sentences on this. Namely, need to watch out for when we predict quadratic slack variables $w$ -- in that case the gradient should be $(Q+Q^\top)w - Q^\top x$