# shgo

**Repository Path**: mirrors_lepy/shgo

## Basic Information

- **Project Name**: shgo
- **Description**: Simplicial Homology Global Optimization
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-09-25
- **Last Updated**: 2025-07-02

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README


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.