Hello,

I am writing an adaptive MCMC sampler for PyMC and I have found that providing a good starting point improves its performance dramatically, so I have used scipy.optimize.fmin_cfg to find a mode of the posterior distribution to use as the starting point. However, I don't know much about different optimization routines, so I don't know if this is a good good choice or not.

Does anyone have advice on which of the optimization routines I should use? I don't care too much about performance because I only have to run the optimization routine a few times at the start of sampling. I should also mention that I have access to the analytical gradient. My algorithm is intended to be for general use, so I can't say too much about the functions on which it will be used, other than they will likely be approximately normal (so the function will be approximately multi-quadradic) near the mode. I don't care if it does not work well for multiple optima.

John