shgo 0.4.8

Simplicial homology global optimisation

.. 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.