.. note::
    :class: sphx-glr-download-link-note

    Click :ref:`here <sphx_glr_download_advanced_mathematical_optimization_auto_examples_plot_noisy.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_advanced_mathematical_optimization_auto_examples_plot_noisy.py:


Noisy optimization problem
===========================

Draws a figure explaining noisy vs non-noisy optimization




.. image:: /advanced/mathematical_optimization/auto_examples/images/sphx_glr_plot_noisy_001.png
    :class: sphx-glr-single-img





.. code-block:: python

    import numpy as np
    import matplotlib.pyplot as plt

    np.random.seed(0)

    x = np.linspace(-5, 5, 101)
    x_ = np.linspace(-5, 5, 31)

    def f(x):
        return -np.exp(-x**2)

    # A smooth function
    plt.figure(1, figsize=(3, 2.5))
    plt.clf()

    plt.plot(x_, f(x_) + .2*np.random.normal(size=31), linewidth=2)
    plt.plot(x, f(x), linewidth=2)

    plt.ylim(ymin=-1.3)
    plt.axis('off')
    plt.tight_layout()
    plt.show()


**Total running time of the script:** ( 0 minutes  0.040 seconds)


.. _sphx_glr_download_advanced_mathematical_optimization_auto_examples_plot_noisy.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download

     :download:`Download Python source code: plot_noisy.py <plot_noisy.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: plot_noisy.ipynb <plot_noisy.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_