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. 
+Check details `here <https://medium.com/optuna/optunahub-benchmarks-a-new-feature-to-use-register-various-benchmark-problems-694401524ce0>`__ . 
+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.
+
+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.
+Here, we use `scipy.optimize.minimize <https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html>`__ as an example.
+
+.. 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. 
+In such cases, the constrained functions are handled through the ``evaluate_constraints`` method.
+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)
+
+    try:
+        print(study.best_trial.params, study.best_trial.value)
+    except Exception as e:
+        print(e)
+
+    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 by using the `the WFG Problem Collection <https://hub.optuna.org/benchmarks/wfg/>`__ module.
+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_pareto = optuna.create_study(
+        study_name="ParetoFront", directions=wfg4.directions
+    )
+    for x in wfg4.get_optimal_solutions(1000):
+        study_pareto.enqueue_trial(params={
+            f"x{i}": x.phenome[i] for i in range(3)
+        })
+    study_pareto.optimize(wfg4, n_trials=1000)
+
+    study_tpe = optuna.create_study(
+        study_name="TPESampler",
+        sampler=optuna.samplers.TPESampler(seed=42), directions=wfg4.directions
+    )
+    study_tpe.optimize(wfg4, n_trials=1000)
+
+    optunahub.load_module("visualization/plot_pareto_front_multi").plot_pareto_front(
+        [study_pareto, study_tpe]
+    ).show()
+
+.. figure:: ./images/pareto_front.png
+   :alt: Pareto Front
+   :align: center
+   :width: 800px

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>`_.