Time grid variations

[PierreMartinon]

Besides passing an explicit time grid as a vector of steps, there are some further directions to explore, as mentioned in #119 and ???. Practically, the argument time_grid could be reused for different features.

By increasing complexity (or so I think):

• time_grid=:refined performs a basic discrete continuation on the time steps, starting from a coarse uniform grid and refining until the prescribed grid_size is met. The sequence of intermediate problems is solved with a limited number of iterations, using warm start. The point of the continuation is to provide a good initial guess for the final solve, at a limited computational cost thanks to the smaller grids and limited iterations. @ocots and I have experimented a bit on this kind of feature, that could be automated.
• time_grid=:optimized means that all time steps become optimization variables in the discretized problem. More precisely, the Dt_i, which just have to be strictly positive to ensure a properly increasing time grid. Similar to a Jump feature. I did some very basic preliminary tests with some "creative" OCP reformulation, but this needs a proper implementation.
• time_grid=:adaptive would be an improvement of the first one above, with some adaptive refinement of the time grid. Instead of just using uniform grids with more steps, individual steps could be moved, added or removed , maybe based on some local error analysis. Optimization would then resume with a warm start. I know @joseph-gergaud likes this one :D