minimize(method=’CG’)#
- scipy.optimize.minimize(fun, x0, args=(), method=None, jac=None, hess=None, hessp=None, bounds=None, constraints=(), tol=None, callback=None, options=None)
Minimization of scalar function of one or more variables using the conjugate gradient 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(x) * max(1, abs(x))
, possibly adjusted to fit into the bounds. Forjac='3-point'
the sign of h is ignored. If None (default) then step is selected automatically.- c1float, default: 1e-4
Parameter for Armijo condition rule.
- c2float, default: 0.4
Parameter for curvature condition rule.
Notes
Parameters c1 and c2 must satisfy
0 < c1 < c2 < 1
.