# shgo Release 0.4.5

Simplicial homology global optimisation

Keywords
optimization, black-box, black-box-model, black-box-optimization, clustering-algorithm, global-optimization, mathematics, nonlinear, nonlinear-optimization, science
MIT
Install
``` pip install shgo==0.4.5 ```

### Documentation  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.