Simplicial homology global optimisation
Project description
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:
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:
$ pip install shgo
Latest:
$ git clone https://bitbucket.org/upiamcompthermo/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
>>> 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.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.