[SciPy-User] What optimization function should I use?
Mon Feb 8 23:12:42 CST 2010
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.
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