scipy.optimize.minimize(fun, x0, args=(), method='SLSQP', jac=None, bounds=None, constraints=(), tol=None, callback=None, options={'func': None, 'maxiter': 100, 'ftol': 1e-06, 'iprint': 1, 'disp': False, 'eps': 1.4901161193847656e-08})

Minimize a scalar function of one or more variables using Sequential Least SQuares Programming (SLSQP).

See also

For documentation for the rest of the parameters, see scipy.optimize.minimize

ftol : float

Precision goal for the value of f in the stopping criterion.

eps : float

Step size used for numerical approximation of the Jacobian.

disp : bool

Set to True to print convergence messages. If False, verbosity is ignored and set to 0.

maxiter : int

Maximum number of iterations.