qiskit_algorithms/optimizers/__init__.py

# This code is part of a Qiskit project.
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# (C) Copyright IBM 2018, 2023.
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# 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
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"""
Optimizers (:mod:`qiskit_algorithms.optimizers`)
================================================
Classical Optimizers.

This package contains a variety of classical optimizers and were designed for use by
qiskit_algorithm's 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 Classes
----------------------

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

   OptimizerResult
   Optimizer
   Minimizer

Steppable Optimization
----------------------

.. 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
`scikit-quant <https://scikit-quant.readthedocs.io/en/latest/>`_. 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, hence it has been made an optional install.
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 <https://nlopt.readthedocs.io/en/latest/>`_ for their
core function and can only be used if the optional dependent ``NLOpt`` package is installed.
To install the ``NLOpt`` dependent package you can use ``pip install nlopt``.

.. 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",
]