Minimize function with parameters
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 + cIn [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]: 633If 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.