Add tutorial in docs about how to use benchmarks

docs/source/tutorials_for_users/how_to_use_benchmarks.rst

@@ -0,0 +1,154 @@
+How to Use Benchmarks
+=====================
+
+OptunaHub provides various benchmarks, and you can utilize them through a unified interface.
+In this tutorial, we will explain how to use benchmarks in OptunaHub.
+If you are interested in registering your own benchmark problems, please check  `Basic <https://optuna.github.io/optunahub/recipes/006_benchmarks_basic.html>`_ and `Advanced <https://optuna.github.io/optunahub/recipes/007_benchmarks_advanced.html>`_ tutorials.
+
+The following blog post also provides an overview of this feature:
+
+- `OptunaHub Benchmarks: A New Feature to Use/Register Various Benchmark Problems <https://medium.com/optuna/optunahub-benchmarks-a-new-feature-to-use-register-various-benchmark-problems-694401524ce0>`__
+
+Preparation
+-----------
+
+First, ensure the necessary packages are installed by executing the following command:
+
+.. code-block:: console
+
+    $ pip install optuna optunahub
+
+Examples
+--------
+
+We will use the `black-box optimization benchmarking (bbob) test suite <https://hub.optuna.org/benchmarks/bbob/>`__ in this tutorial.
+This is a wrapper of `COCO (COmparing Continuous Optimizers) experiment <https://github.com/numbbo/coco-experiment>`__ library.
+So you need to install COCO first.
+
+.. code-block:: console
+
+    $ pip install coco-experiment
+
+
+Test code is as follows:
+
+.. code-block:: python
+
+    import optuna
+    import optunahub
+
+
+    bbob = optunahub.load_module("benchmarks/bbob")
+    sphere2d = bbob.Problem(function_id=1, dimension=2, instance_id=1)
+
+    study = optuna.create_study(directions=sphere2d.directions, sampler=optuna.samplers.TPESampler(seed=42))
+    study.optimize(sphere2d, n_trials=100)
+
+    optuna.visualization.plot_optimization_history(study).show()
+
+.. figure:: ./images/optimization_history.png
+   :alt: Optimization History
+   :align: center
+   :width: 800px
+
+
+You can also use other optimizing frameworks to optimize the problem.
+:class:`~optunahub.benchmarks.BaseProblem` provides :meth:`~optunahub.benchmarks.BaseProblem.__call__` and :meth:`~optunahub.benchmarks.BaseProblem.evaluate` methods, which is used to evaluate the objective function.
+:meth:`~optunahub.benchmarks.BaseProblem.__call__` takes an :class:`optuna.Trial` object, while :meth:`~optunahub.benchmarks.BaseProblem.evaluate` takes a dictionary of input parameters.
+Therefore, you can use :meth:`~optunahub.benchmarks.BaseProblem.evaluate` to optimize the problem with other optimizing frameworks.
+Here, we use `scipy.optimize.minimize <https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html>`__ as an example.
+The properties ``initial_solution``, ``lower_bounds``, and ``upper_bounds`` are provided by `the bbob package <https://hub.optuna.org/benchmarks/bbob/>`__.
+
+.. code-block:: python
+
+    import optunahub
+    import scipy
+
+
+    bbob = optunahub.load_module("benchmarks/bbob")
+    sphere2d = bbob.Problem(function_id=1, dimension=2, instance_id=1)
+    result = scipy.optimize.minimize(
+        fun=lambda x: sphere2d.evaluate({f"x{d}": x[d] for d in range(sphere2d.dimension)}),
+        x0=sphere2d.initial_solution,
+        bounds=scipy.optimize.Bounds(
+            lb=sphere2d.lower_bounds, ub=sphere2d.upper_bounds
+        )
+    )
+
+
+Constrained Problem
+^^^^^^^^^^^^^^^^^^^
+
+Some benchmarks also include constraints. 
+These problems are implemented by inheriting :class:`~optunahub.benchmarks.ConstrainedMixin` class.
+:class:`~optunahub.benchmarks.ConstrainedMixin` provides :meth:`~optunahub.benchmarks.ConstrainedMixin.evaluate_constraints` and :meth:`~optunahub.benchmarks.ConstrainedMixin.constraints_func` methods.
+As same as objective functions, :meth:`~optunahub.benchmarks.ConstrainedMixin.constraints_func` takes an :class:`optuna.Trial` object, while :meth:`~optunahub.benchmarks.ConstrainedMixin.evaluate_constraints` takes a dictionary of input parameters.
+Those methods are used to evaluate the constraint functions.
+You can optimize these problems in the same way as usual, but you need to set the ``constraints_func`` argument in the sampler.
+
+.. code-block:: python
+
+    import optuna
+    import optunahub
+    import matplotlib.pyplot as plt
+
+    bbob_constrained = optunahub.load_module("benchmarks/bbob_constrained")
+    constrained_sphere2d = bbob_constrained.Problem(function_id=1, dimension=2, instance_id=1)
+
+    study = optuna.create_study(
+        sampler=optuna.samplers.TPESampler(
+            constraints_func=constrained_sphere2d.constraints_func,
+            seed=42
+        ),
+        directions=constrained_sphere2d.directions
+    )
+    study.optimize(constrained_sphere2d, n_trials=100)
+    optuna.visualization.plot_optimization_history(study).show()
+    plt.show()
+
+.. figure:: ./images/optimization_history_constrained.png
+   :alt: Optimization History
+   :align: center
+   :width: 800px
+
+
+Multi-Objective Problem
+^^^^^^^^^^^^^^^^^^^^^^^
+
+You can also try multi-objective optimization. 
+Here, we use the `the WFG Problem Collection <https://hub.optuna.org/benchmarks/wfg/>`__ as an example.
+In order to use this module, you need to install `optproblems <https://pypi.org/project/optproblems/>`__ and `diversipy <https://pypi.org/project/diversipy/>`__ packages.
+
+.. code-block:: console
+
+    $ pip install -U optproblems diversipy
+
+Example is as follows:
+
+.. code-block:: python
+
+    import optuna
+    import optunahub
+
+
+    wfg = optunahub.load_module("benchmarks/wfg")
+    wfg4 = wfg.Problem(function_id=4, n_objectives=2, dimension=3, k=1)
+
+    study = optuna.create_study(
+        study_name="TPESampler",
+        sampler=optuna.samplers.TPESampler(seed=42), directions=wfg4.directions
+    )
+    study.optimize(wfg4, n_trials=1000)
+
+    optuna.visualization.plot_pareto_front(study).show()
+
+.. figure:: ./images/pareto_front.png
+   :alt: Pareto Front
+   :align: center
+   :width: 800px
+
+Keep Exploring!
+---------------
+
+There are many kinds of benchmarks in OptunaHub.
+You can find them in the `OptunaHub Benchmarks <https://hub.optuna.org/?q=Benchmark>`__ page.

docs/source/tutorials_for_users/index.rst

@@ -4,4 +4,5 @@ Tutorials for Users
 .. toctree::
     :maxdepth: 1
 
-    plot-in-optuna-dashboard
+    plot-in-optuna-dashboard
+    how_to_use_benchmarks

docs/source/tutorials_for_users/plot-in-optuna-dashboard.rst

@@ -1,5 +1,5 @@
-Tutorial: How to Plot in Optuna-Dashboard
-=========================================
+How to Plot in Optuna-Dashboard
+===============================
 
 In this tutorial, we will explain how to display plots in `Optuna-Dashboard <https://optuna-dashboard.readthedocs.io/en/latest/index.html>`_.