# minimize(method=’TNC’)

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

  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 or ndarray

      If jac is None the absolute step size used for numerical approximation of the jacobian via forward differences.

    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, optional

      Maximum number of function evaluations. This keyword is deprecated in favor of maxfun. Only if maxfun is None is this keyword used.

    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 stopping 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.

    finite_diff_rel_step None 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. For `method='3-point'` the sign of h is ignored. If None (default) then step is selected automatically.

    maxfun int

      Maximum number of function evaluations. If None, maxfun is set to max(100, 10*len(x0)). Defaults to None.

minimize(method=’L-BFGS-B’) minimize(method=’COBYLA’)