lp_visu is a simple Python module using matplotlib to visualize the progress of the simplex algorithm. You can use for instance scipy.optimize.linprog and its callback function to visualize the algorithm used by linprog. It can also be used for visualizing how to put cuts for Integer Linear Programming. It works with problems with 2 variables.

The dependencies are the following:

• Python >= 3.8
• SciPy >= 1.2

There are three examples in the project:

• lp_visu_ex.py which is a simple example in which you can see how to draw the objective function with a particular value or to draw a pivot point (press enter in the console to continue the script)
• ilp_visu_ex.py which is a simple example for ILP in which I add some cuts to the initial problem (press enter in the console to continue the script)
• lp_visu_ex_interactive which uses scipy.optimize.linprog callback function to move the pivot along the edges of the polygon, showing the simplex running. Press any key to go to the next step.

This code surely needs some refactoring, do not hesitate to give me suggestions or to send me pull requests! Documentation in the code should be sufficient to understand how to use lp_visu.

lp_visu_notebook.py and ilp_visu_notebook.py show how to use lp_visu in Jupyter notebooks. In this case, you have to define cuts, objective function visualization etc. when creating the LPVisu objects, the methods defined to add or remove cuts etc. are not working.

This project is licensed under the terms of the GNU GPL license. You should find the complete license in the LICENSE.md file.