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# minimize(method=’TNC’)

`scipy.optimize.``minimize`(fun, x0, args=(), method='TNC', jac=None, bounds=None, tol=None, callback=None, options={'disp': False, 'minfev': 0, 'scale': None, 'rescale': -1, 'offset': None, 'gtol': -1, 'eps': 1e-08, 'eta': -1, 'maxiter': None, 'maxCGit': -1, 'mesg_num': None, 'ftol': -1, 'xtol': -1, 'stepmx': 0, 'accuracy': 0})

  Minimize a scalar function of one or more variables using a truncated Newton (TNC) algorithm.

  See also

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

  Options:

  eps : float

  > Step size used for numerical approximation of the jacobian.

  scale : list of floats

  > Scaling factors to apply to each variable. If None, the factors are up-low for interval bounded variables and 1+|x] fo the others. Defaults to None

  offset : float

  > Value to subtract from each variable. If None, the offsets are (up+low)/2 for interval bounded variables and x for the others.

  disp : bool

  > Set to True to print convergence messages.

  maxCGit : int

  > Maximum number of hessian*vector evaluations per main iteration. If maxCGit == 0, the direction chosen is -gradient if maxCGit < 0, maxCGit is set to max(1,min(50,n/2)). Defaults to -1.

  maxiter : int

  > Maximum number of function evaluation. if None, maxiter is set to max(100, 10*len(x0)). Defaults to None.

  eta : float

  > Severity of the line search. if < 0 or > 1, set to 0.25. Defaults to -1.

  stepmx : float

  > Maximum step for the line search. May be increased during call. If too small, it will be set to 10.0. Defaults to 0.

  accuracy : float

  > Relative precision for finite difference calculations. If <= machine_precision, set to sqrt(machine_precision). Defaults to 0.

  minfev : float

  > Minimum function value estimate. Defaults to 0.

  ftol : float

  > Precision goal for the value of f in the stoping criterion. If ftol < 0.0, ftol is set to 0.0 defaults to -1.

  xtol : float

  > Precision goal for the value of x in the stopping criterion (after applying x scaling factors). If xtol < 0.0, xtol is set to sqrt(machine_precision). Defaults to -1.

  gtol : float

  > Precision goal for the value of the projected gradient in the stopping criterion (after applying x scaling factors). If gtol < 0.0, gtol is set to 1e-2 * sqrt(accuracy). Setting it to 0.0 is not recommended. Defaults to -1.

  rescale : float

  > Scaling factor (in log10) used to trigger f value rescaling. If 0, rescale at each iteration. If a large value, never rescale. If < 0, rescale is set to 1.3.