Currently I have the following code that defines the function f.
a = #something
b = #something
c = #something
def f(x):
"""Evaluates some function that depends on parameters a, b, and c"""
someNumber = #some calculation
return someNumber
Ideally I would do def f(x, a, b, c), BUT I am minimizing f with respect to x and SciPy's optimization toolbox doesn't allow for one to minimize functions with parameters in the arguments. That said I would like to run my minimization code for multiple values of a, b and c. Is there a way I can do this?
You can specify additional arguments in args
from scipy.optimize import minimize
minimize(f, x0, args=(a, b, c))
This is a straightforward question and answer about using minimize. In case other users need something more concrete, here's a simple example.
A generalized quadratic equation:
In [282]: def fun(x, a,b,c):
...: return a*x**2 + b*x + c
In [283]: optimize.minimize(fun, 10, args=(1,0,0))
Out[283]:
fun: 1.7161984122524196e-15
hess_inv: array([[ 0.50000001]])
jac: array([ -6.79528891e-08])
message: 'Optimization terminated successfully.'
nfev: 15
nit: 4
njev: 5
status: 0
success: True
x: array([ -4.14270251e-08])
In [284]: optimize.minimize(fun, 10, args=(1,1,1))
Out[284]:
fun: 0.7500000000000221
hess_inv: array([[ 0.49999999]])
jac: array([ 3.12924385e-07])
message: 'Optimization terminated successfully.'
nfev: 12
nit: 2
njev: 4
status: 0
success: True
x: array([-0.49999985])
The function could take arrays as input as well, but still needs to return a single (scalar) value:
In [289]: optimize.minimize(fun, [10,10,10], args=(np.array([1,2,3]), 1, 1))
Out[289]:
fun: 2.541666666667115
hess_inv: array([[ 0.50021475, -0.00126004, 0.00061239],
[-0.00126004, 0.25822101, -0.00259327],
[ 0.00061239, -0.00259327, 0.16946887]])
jac: array([ -8.94069672e-08, 4.47034836e-07, -2.20537186e-06])
message: 'Optimization terminated successfully.'
nfev: 55
nit: 9
njev: 11
status: 0
success: True
x: array([-0.50000006, -0.2499999 , -0.16666704])
In [286]: def fun(x, a,b,c):
...: return (a*x**2 + b*x + c).sum()
It's a good idea to make sure the function runs with the proposed x0 and args, e.g.
In [291]: fun(np.array([10,10,10]), np.array([1,2,3]), 1, 1)
Out[291]: 633
If you can't call the objective function, or are confused as to how its arguments work, minimize isn't a magic bullet. This minimization is only as good as your understanding of the objective function.