# This code is part of Qiskit.
#
# (C) Copyright IBM 2018, 2022.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# 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.

"""
Optimizers (:mod:`qiskit.algorithms.optimizers`)
=====================================================
It  contains a variety of classical optimizers for use by quantum variational algorithms,
such as :class:`~qiskit.algorithms.VQE`.
Logically, these optimizers can be divided into two categories:

`Local Optimizers`_
  Given an optimization problem, a **local optimizer** is a function
  that attempts to find an optimal value within the neighboring set of a candidate solution.

`Global Optimizers`_
  Given an optimization problem, a **global optimizer** is a function
  that attempts to find an optimal value among all possible solutions.

.. currentmodule:: qiskit.algorithms.optimizers

Optimizer Base Class
====================

.. autosummary::
   :toctree: ../stubs/
   :nosignatures:

   OptimizerResult
   OptimizerSupportLevel
   Optimizer
   Minimizer

Steppable Optimizer Base Class
==============================

.. autosummary::
   :toctree: ../stubs/

   optimizer_utils

.. autosummary::
   :toctree: ../stubs/
   :nosignatures:

   SteppableOptimizer
   AskData
   TellData
   OptimizerState



Local Optimizers
================

.. autosummary::
   :toctree: ../stubs/
   :nosignatures:

   ADAM
   AQGD
   CG
   COBYLA
   L_BFGS_B
   GSLS
   GradientDescent
   GradientDescentState
   NELDER_MEAD
   NFT
   P_BFGS
   POWELL
   SLSQP
   SPSA
   QNSPSA
   TNC
   SciPyOptimizer
   UMDA

Qiskit also provides the following optimizers, which are built-out using the optimizers from
the `scikit-quant` package. The `scikit-quant` package is not installed by default but must be
explicitly installed, if desired, by the user - the optimizers therein are provided under various
licenses so it has been made an optional install for the end user to choose whether to do so or
not. To install the `scikit-quant` dependent package you can use
`pip install scikit-quant`.

.. autosummary::
   :toctree: ../stubs/
   :nosignatures:

   BOBYQA
   IMFIL
   SNOBFIT

Global Optimizers
=================
The global optimizers here all use NLopt for their core function and can only be
used if their dependent NLopt package is manually installed.

.. autosummary::
   :toctree: ../stubs/
   :nosignatures:

   CRS
   DIRECT_L
   DIRECT_L_RAND
   ESCH
   ISRES

"""

from .adam_amsgrad import ADAM
from .aqgd import AQGD
from .bobyqa import BOBYQA
from .cg import CG
from .cobyla import COBYLA
from .gsls import GSLS
from .gradient_descent import GradientDescent, GradientDescentState
from .imfil import IMFIL
from .l_bfgs_b import L_BFGS_B
from .nelder_mead import NELDER_MEAD
from .nft import NFT
from .nlopts.crs import CRS
from .nlopts.direct_l import DIRECT_L
from .nlopts.direct_l_rand import DIRECT_L_RAND
from .nlopts.esch import ESCH
from .nlopts.isres import ISRES
from .steppable_optimizer import SteppableOptimizer, AskData, TellData, OptimizerState
from .optimizer import Minimizer, Optimizer, OptimizerResult, OptimizerSupportLevel
from .p_bfgs import P_BFGS
from .powell import POWELL
from .qnspsa import QNSPSA
from .scipy_optimizer import SciPyOptimizer
from .slsqp import SLSQP
from .snobfit import SNOBFIT
from .spsa import SPSA
from .tnc import TNC
from .umda import UMDA

__all__ = [
    "Optimizer",
    "OptimizerSupportLevel",
    "SteppableOptimizer",
    "AskData",
    "TellData",
    "OptimizerState",
    "OptimizerResult",
    "Minimizer",
    "ADAM",
    "AQGD",
    "CG",
    "COBYLA",
    "GSLS",
    "GradientDescent",
    "GradientDescentState",
    "L_BFGS_B",
    "NELDER_MEAD",
    "NFT",
    "P_BFGS",
    "POWELL",
    "SciPyOptimizer",
    "SLSQP",
    "SPSA",
    "QNSPSA",
    "TNC",
    "CRS",
    "DIRECT_L",
    "DIRECT_L_RAND",
    "ESCH",
    "ISRES",
    "SNOBFIT",
    "BOBYQA",
    "IMFIL",
    "UMDA",
]