Question

According to the SciPy documentation, it is possible to minimize functions with multiple variables, yet it doesn’t say how to optimize such functions.

from scipy.optimize import minimize
from math import *

def f(c):
  return sqrt((sin(pi/2) + sin(0) + sin(c) - 2)**2 + (cos(pi/2) + cos(0) + cos(c) - 1)**2)

print(minimize(f, 3.14/2 + 3.14/7))

The above code try to minimize the function f, but for my task I need to minimize with respect to three variables.

Simply introducing a second argument and adjusting minimize accordingly yields an error:

TypeError: f() takes exactly 2 arguments (1 given)

How does minimize work when minimizing with multiple variables?

Answer 1

Pack the multiple variables into a single array:

import scipy.optimize as optimize

def f(params):
    # print(params)  # <-- you'll see that params is a NumPy array
    a, b, c = params # <-- for readability you may wish to assign names to the component variables
    return a**2 + b**2 + c**2

initial_guess = [1, 1, 1]
result = optimize.minimize(f, initial_guess)
if result.success:
    fitted_params = result.x
    print(fitted_params)
else:
    raise ValueError(result.message)

yields

[ -1.66705302e-08  -1.66705302e-08  -1.66705302e-08]