Scipy.optimize.minimize returning incorrect results

--------------------------------------------------
Hire the world's top talent on demand or became one of them at Toptal: https://topt.al/25cXVn
and get $2,000 discount on your first invoice
--------------------------------------------------

Take control of your privacy with Proton's trusted, Swiss-based, secure services.
Choose what you need and safeguard your digital life:
Mail: https://go.getproton.me/SH1CU
VPN: https://go.getproton.me/SH1DI
Password Manager: https://go.getproton.me/SH1DJ
Drive: https://go.getproton.me/SH1CT

Music by Eric Matyas
https://www.soundimage.org
Track title: Light Drops

--

Chapters
00:00 Scipy.Optimize.Minimize Returning Incorrect Results
00:55 Accepted Answer Score 6
01:28 Answer 2 Score 5
01:39 Thank you

--

Full question
https://stackoverflow.com/questions/2747...

--

Content licensed under CC BY-SA
https://meta.stackexchange.com/help/lice...

--

Tags
#python #numpy #scipy

#avk47

ACCEPTED ANSWER

Score 6

If you use the function scipy.optimize.minimize_scalar you get the expected result:

results = minimize_scalar(error_p, tol=0.00001)
print results['x'], results['fun']
>>> 1.88536329298 0.000820148069544

Why does scipy.optimize.minimize not work? My guess is that your function error_p is malformed from a numpy perspective. Try this:

MU = np.linspace(0,20,100)
error_p(MU)

and you'll see that it fails. Your function isn't tailored to take in an array of inputs and spit out an array of outputs, which I think is what minimize is looking for.

ANSWER 2

Score 5

Change

theory = [poisson.pmf(x, mu) for x in x]

theory = poisson.pmf(x, mu)

and it works as expected.