Minimizing an objective function using Python

Thanks largely to physicists, Python has very good support for efficient scientific computing. The following code shows how to use the brute-force optimization function of scipy to minimize the value of some objective function with 4 parameters. Since it is a grid-based method, it's likely that you may have to rerun the optimization with a smaller parameter space.

import numpy
import scipy.optimize

def my_objective_fn(params):
   print params,
   value_of_objectivefn = calc_objectivefn_value(params)
   print value_of_objectivefn
   return value_of_objectivefn

if __name__=="__main__":
   # Method 1 (same number of steps for each parameter)
   myranges = ((21,49), (0,0.1), (95,123), (1.6, 2.0))
   scipy.optimize.brute(my_objective_fn, myranges, Ns=5)

   # Method 2 (can have different number of steps for
   # different parameters)    
   slice_obj = numpy.s_[21:56:7, 0:0.1:0.02,
                        95:130:7, 1.6:2.1:0.1]
   scipy.optimize.brute(my_objective_fn, slice_obj)

A number of cleverer optimization functions are also available in scipy, including constrained optimization and stochastic methods. For more information, install scipy (on Windows or Linux), and type the following at the Python prompt:

>>> import scipy.optimize
>>> help(scipy.optimize)
DESCRIPTION
    Optimization Tools
    ==================

     A collection of general-purpose optimization routines.

       fmin        --  Nelder-Mead Simplex algorithm
                         (uses only function calls)
       fmin_powell --  Powell's (modified) level set method
                       (uses only function calls)
...