I was looking at manual and it suggested that for some models, for example the stochastic volatility model, that using the full mass matrix rather than the diagonal mass matrix could speed things up, though this does not scale for larger problems.
If I have some sense of the covariance structure of my problem, it seems like it could be possible to specify which elements to keep track of and save on computational efficiency. Or would this not work due to the mass matrix needing to be inverted?
Alternatively, would it be possible to use a low rank approximation to the mass matrix similar to what is done in L-BFGS? I realize all of this will likely involve digging into the Stan internals, and wanted to get a sense if this sounds reasonable before digging in.