[Ipopt] conditional gradient

[Nicolas Essis-Breton]

Hi,

I would like to experiment with conditional gradient in ipopt.
Normal ipopt flow is:

query user hessian -> find search direction -> filter line search

I would like to do:

query user hessian -> find search direction -> let user modify search
direction -> filter line search

I can overload IpoptAlgorithm::ComputeSearchDirection, but is this enough?
For example, for a second-order correction
FilterLSAcceptor::TrySecondOrderCorrection recalculates the search
direction.
Would it be better to overload PDSystemSolver::Solve?

The idea of conditional gradient is to modify any computed search
direction, before it is actually used by the optimization algorithm.
This is sometimes called 'projected gradient' or 'frank-wolfe' method.
To be sure I have overloaded all the necessary parts, your help would be
great.

Thanks,

Nicolas
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