python - numpy/scipy analog of matlab's fminsearch

Question

I am converting some Matlab code into python using numpy. Everything worked pretty smoothly but recently I encountered fminsearch function.

So, to cut it short: is there an easy way to make in python something like this:

banana = @(x)100*(x(2)-x(1)^2)^2+(1-x(1))^2;
[x,fval] = fminsearch(banana,[-1.2, 1])

which will return

x = 1.0000    1.0000
fval = 8.1777e-010

Up till now I have not found anything that looks similar in numpy. The only thing that I found similar is scipy.optimize.fmin. Based on the definition it

Minimize a function using the downhill simplex algorithm.

But right now I can not find to write the above-mentioned Matlab code using this function

Answer 1

It's just a straight-forward conversion from Matlab syntax to python syntax:

import scipy.optimize

banana = lambda x: 100*(x[1]-x[0]**2)**2+(1-x[0])**2
xopt = scipy.optimize.fmin(func=banana, x0=[-1.2,1])

with output:

Optimization terminated successfully.
         Current function value: 0.000000
         Iterations: 85
         Function evaluations: 159
array([ 1.00002202,  1.00004222])