Stochastic Gradient Descent To Quasi Newton Minimizer
An experimental minimizer which takes a stochastic function (one implementing AbstractStochasticCachingDiffFunction)
and executes SGD for the first couple passes. During the final iterations a series of approximate hessian vector
products are built up. These are then passed to the QNminimizer so that it can start right up without the typical
Note  The basic idea here is good, but the original ScaledSGDMinimizer wasn't efficient, and so this would
be much more useful if rewritten to use the good StochasticInPlaceMinimizer instead.