3.9.09

Was able to get the modal SPGD working using a least-squares estimate of the gradient, instead of the stochastic nonsense in the original algorithm. Its not clear yet if this is a superior method or not, but as far as I know it doesn't depend as explicitly on the statistics of the perturbations (the original algorithm required delta correlated perturbations).

Convergence seems to be very dependent on the number of modes used. Even lowering it from 31 to 25 results in crap steady-state performance. I might try feeding back the non-modal wavefront norm as the cost function, rather than the wavefront projection onto a number of modes, since its possible the problem is there, rather than the gradient estimation. This might make more sense since there's no advantage to approximating the cost with a limited number of modes when you can get the complete value for free.

In general though steady-state performance isn't as good as with the integral controller, and seems to depend heavily on the gain. This seems to indicate that either (a) the cost function truly isn't truly convex or (b) the estimate of the gradient is too shitty to converge to the theoretical minimum. The real advantage, if there is one, will be in optimizing from a cost function that can be measured from a regular camera, and not a WFS. There are some papers where this is done, so I should be able to do it eventually.

Ideally, when I have the better sensor, an accurate poke matrix will let me calculate the optimal actuator command without any iterating at all.