.. image:: https://travis-ci.org/Stefan-Endres/shgo.svg?branch=master
:target: https://travis-ci.org/Stefan-Endres/shgo
.. image:: https://coveralls.io/repos/github/Stefan-Endres/shgo/badge.png?branch=master
:target: https://coveralls.io/github/Stefan-Endres/shgo?branch=master
Repository: https://github.com/Stefan-Endres/shgo
Description
-----------
Finds the global minimum of a function using simplicial homology global
optimisation (shgo_). Appropriate for solving general purpose NLP and blackbox
optimisation problems to global optimality (low dimensional problems).
The general form of an optimisation problem is given by:
.. _shgo: https://stefan-endres.github.io/shgo/
::
minimize f(x) subject to
g_i(x) >= 0, i = 1,...,m
h_j(x) = 0, j = 1,...,p
where x is a vector of one or more variables. ``f(x)`` is the objective
function ``R^n -> R``, ``g_i(x)`` are the inequality constraints.
``h_j(x)`` are the equality constrains.
Installation
------------
Stable:
.. code::
$ pip install shgo
Latest:
.. code::
$ git clone https://github.com/Stefan-Endres/shgo
$ cd shgo
$ python setup.py install
$ python setup.py test
Documentation
-------------
The project website https://stefan-endres.github.io/shgo/ contains more detailed examples, notes and performance profiles.
Quick example
-------------
Consider the problem of minimizing the Rosenbrock function. This function is implemented in ``rosen`` in ``scipy.optimize``
.. code:: python
>>> from scipy.optimize import rosen
>>> from shgo import shgo
>>> bounds = [(0,2), (0, 2), (0, 2), (0, 2), (0, 2)]
>>> result = shgo(rosen, bounds)
>>> result.x, result.fun
(array([ 1., 1., 1., 1., 1.]), 2.9203923741900809e-18)
Note that bounds determine the dimensionality of the objective function and is therefore a required input, however you can specify empty bounds using ``None`` or objects like numpy.inf which will be converted to large float numbers.
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