scipy is not optimizing and returns “Desired error not necessarily achieved due to precision loss”

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I copied your example and tried a little bit. Looks like if you stick with BFGS solver, after a few iteration the mu+ alpha * r will have some negative numbers, and that’s how you get the RuntimeWarning.

The easiest fix I can think of is to switch to Nelder Mead solver.

res = minimize(loglikelihood, (0.01, 0.1,0.1), method = 'Nelder-Mead',args = (atimes,))

And it will give you this result:

28.3136498357
  status: 0
    nfev: 159
 success: True
     fun: 27.982451280648817
       x: array([ 0.01410906,  0.68346023,  0.90837568])
 message: 'Optimization terminated successfully.'
     nit: 92

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