# Quellcode fÃ¼r qiskit.algorithms.optimizers.tnc

```# This code is part of Qiskit.
#
# (C) Copyright IBM 2018, 2020.
#
# obtain a copy of this license in the LICENSE.txt file in the root directory
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.

"""Truncated Newton (TNC) optimizer."""
from __future__ import annotations

from .scipy_optimizer import SciPyOptimizer

[Doku]class TNC(SciPyOptimizer):
"""
Truncated Newton (TNC) optimizer.

TNC uses a truncated Newton algorithm to minimize a function with variables subject to bounds.
It differs from the :class:`CG` method as it wraps a C implementation and allows each variable
to be given upper and lower bounds.

Uses scipy.optimize.minimize TNC
For further detail, please refer to
See https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html
"""

_OPTIONS = ["maxiter", "disp", "accuracy", "ftol", "xtol", "gtol", "eps"]

# pylint: disable=unused-argument
def __init__(
self,
maxiter: int = 100,
disp: bool = False,
accuracy: float = 0,
ftol: float = -1,
xtol: float = -1,
gtol: float = -1,
tol: float | None = None,
eps: float = 1e-08,
options: dict | None = None,
max_evals_grouped: int = 1,
**kwargs,
) -> None:
"""
Args:
maxiter: Maximum number of function evaluation.
disp: Set to True to print convergence messages.
accuracy: Relative precision for finite difference calculations.
If <= machine_precision, set to sqrt(machine_precision). Defaults to 0.
ftol: 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: 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: 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.
tol: Tolerance for termination.
eps: Step size used for numerical approximation of the Jacobian.
options: A dictionary of solver options.
max_evals_grouped: Max number of default gradient evaluations performed simultaneously.
"""
if options is None:
options = {}
for k, v in list(locals().items()):
if k in self._OPTIONS:
options[k] = v
super().__init__(
"TNC",
options=options,
tol=tol,
max_evals_grouped=max_evals_grouped,
**kwargs,
)
```