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# 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:
    ——-
    disp bool

      Set to True to print convergence messages.

    xtol float

      Average relative error in solution xopt acceptable for convergence.

    maxiter int

      Maximum number of iterations to perform.

    eps float or ndarray

      If hessp is approximated, use this value for the step size.

    return_all bool, optional

      Set to True to return a list of the best solution at each of the iterations.

    c1 float, default: 1e-4

      Parameter for Armijo condition rule.

    c2 float, default: 0.9

      Parameter for curvature condition rule.

    workers int, map-like callable, optional

      A map-like callable, such as multiprocessing.Pool.map for evaluating any numerical differentiation in parallel. This evaluation is carried out as `workers(fun, iterable)`.

      Added in version 1.16.0.

  Notes

  Parameters c1 and c2 must satisfy `0 < c1 < c2 < 1`.