Metadata-Version: 2.1
Name: gilp
Version: 0.0.1rc3
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
Description: # GILP (Geometric Interpretation of Linear Programming)
        
        ## Installation
        
        The quickest way to start using gilp is with a pip install
        
        ```pip install gilp```
        
        To develop and run tests, you will need to pip install with extra dependencies
        
        ```pip install gilp[dev]```
        
        ## 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/>
        
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
