minimize(method=’Newton-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 Newton-CG algorithm.
Note that the jac parameter (Jacobian) is required.
See also
For documentation for the rest of the parameters, see
scipy.optimize.minimize
- Options:
- ——-
- dispbool
Set to True to print convergence messages.
- xtolfloat
Average relative error in solution xopt acceptable for convergence.
- maxiterint
Maximum number of iterations to perform.
- epsfloat or ndarray
If hessp is approximated, use this value for the step size.
- return_allbool, optional
Set to True to return a list of the best solution at each of the iterations.
- c1float, default: 1e-4
Parameter for Armijo condition rule.
- c2float, default: 0.9
Parameter for curvature condition rule.
Notes
Parameters c1 and c2 must satisfy
0 < c1 < c2 < 1
.