Metadata-Version: 2.1
Name: gilp
Version: 0.0.1rc2
Summary: A Python package for visualizing the geometry of linear programs.
Home-page: https://github.com/henryrobbins/gilp.git
Author: Henry Robbins
Author-email: hwr26@cornell.edu
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: typing
Requires-Dist: scipy
Requires-Dist: plotly

# GILP (Geometric Interpretation of Linear Programming)

## Installation

The quickest way to start using gilp is with a pip install

```pip install gilp```

## Example

The LP class creates linear programs from (3) numpy arrays: A, b, and c which define the LP in standard inequality form.

max  c^Tx<br/>
s.t. Ax <= b<br/>
      x >= 0<br/>

Consider the following input.

```A = np.array([[1,0], [1, 2]])```<br/>
```b = np.array([[2],[4]])```<br/>
```c = np.array([[1],[1]])```<br/>
```lp = LP(A,b,c)```<br/>

The corresponding LP is:

max  1x_1 + 1x_2<br/>
s.t  1x_1 + 0x_2 <= 2<br/>
     1x_1 + 2x_2 <= 4<br/>
      x_1,   x_2 >= 0<br/>

To visualize the simplex algorithm on an LP, first create a plotly figure
and then use ```.show()``` to open up an HTML file or ```.write_html()```
to write an HTML file with a given name.

```fig = simplex_visual(lp)```<br/>
```fig.show()```<br/>
```fig.write_html('example.html)```<br/>


