minimize(method=’BFGS’)#
- scipy.optimize.minimize(fun, x0, args=(), method='BFGS', jac=None, tol=None, callback=None, options={'gtol': 1e-05, 'norm': inf, 'eps': 1.4901161193847656e-08, 'maxiter': None, 'disp': False, 'return_all': False, 'finite_diff_rel_step': None})
Minimization of scalar function of one or more variables using the BFGS algorithm.
See also
For documentation for the rest of the parameters, see
scipy.optimize.minimize- Options
- dispbool
Set to True to print convergence messages.
- maxiterint
Maximum number of iterations to perform.
- gtolfloat
Gradient norm must be less than gtol before successful termination.
- normfloat
Order of norm (Inf is max, -Inf is min).
- epsfloat or ndarray
If jac is None the absolute step size used for numerical approximation of the jacobian via forward differences.
- return_allbool, optional
Set to True to return a list of the best solution at each of the iterations.
- finite_diff_rel_stepNone or array_like, optional
If jac in [‘2-point’, ‘3-point’, ‘cs’] the relative step size to use for numerical approximation of the jacobian. The absolute step size is computed as
h = rel_step * sign(x0) * max(1, abs(x0)), possibly adjusted to fit into the bounds. Formethod='3-point'the sign of h is ignored. If None (default) then step is selected automatically.