Multiple variables in SciPy's optimize.minimize
PythonMathScipyPython Problem Overview
According to the SciPy documentation it is possible to minimize functions with multiple variables, yet it doesn't tell how to optimize on 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 does 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.
Python Solutions
Solution 1 - Python
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]