ote that this function
    may violate the limit because of evaluating gradients by numerical
    differentiation.
eta : float, optional
    Severity of the line search. If < 0 or > 1, set to 0.25.
    Defaults to -1.
stepmx : float, optional
    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, optional
    Relative precision for finite difference calculations. If
    <= machine_precision, set to sqrt(machine_precision).
    Defaults to 0.
fmin : float, optional
    Minimum function value estimate. Defaults to 0.
ftol : float, optional
    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, optional
    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.
pgtol : float, optional
    Precision goal for the value of the projected gradient in
    the stopping criterion (after applying x scaling factors).
    If pgtol < 0.0, pgtol is set to 1e-2 * sqrt(accuracy).
    Setting it to 0.0 is not recommended. Defaults to -1.
rescale : float, optional
    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.
callback : callable, optional
    Called after each iteration, as callback(xk), where xk is the
    current parameter vector.

Returns
-------
x : ndarray
    The solution.
nfeval : int
    The number of function evaluations.
rc : int
    Return code, see below

See also
--------
minimize: Interface to minimization algorithms for multivariate
    functions. See the 'TNC' `method` in particular.

Notes
-----
The underlying algorithm is truncated Newton, also called
Newton Conjugate-Gradient. This method differs from
scipy.optimize.fmin_ncg in that

1. it wraps a C implementation of the algorithm
2. it allows each variable to be given an upper and lower bound.

The algorithm incorporates the bound constraints by determining
the descent direction as in an unconstrained truncated Newton,
but never taking a step-size large enough to leave the space
of feasible x's. The algorithm keeps track of a set of
currently active constraints, and ignores them when computing
the minimum allowable step size. (The x's associated with the
active constraint are kept fixed.) If the maximum allowable
step size is zero then a new constraint is added. At the end
of each iteration one of the constraints may be deemed no
longer active and removed. A constraint is considered
no longer active is if it is currently active
but the gradient for that variable points inward from the
constraint. The specific constraint removed is the one
associated with the variable of largest index whose
constraint is no longer active.

Return codes are defined as follows:

- ``-1`` : Infeasible (lower bound > upper bound)
- ``0`` : Local minimum reached (:math:`|pg| \approx 0`)
- ``1`` : Converged (:math:`|f_n-f_(n-1)| \approx 0`)
- ``2`` : Converged (:math:`|x_n-x_(n-1)| \approx 0`)
- ``3`` : Max. number of function evaluations reached
- ``4`` : Linear search failed
- ``5`` : All lower bounds are equal to the upper bounds
- ``6`` : Unable to progress
- ``7`` : User requested end of minimization

References
----------
Wright S., Nocedal J. (2006), 'Numerical Optimization'

Nash S.G. (1984), "Newton-Type Minimization Via the Lanczos Method",
SIAM Journal of Numerical Analysis 21, pp. 770-778

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