Solving linear programming problems with the Big-M method
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A uni project with the goal of creating a Big M solver for linear programming problems.
Follow these steps to use the fullstack Big-M.
Our Big M package is available on pypi via
pip install BigM
if you're going to only use the package Bigm, these steps are optional
- npm
npm install npm@latest -g
- python uvicorn
pip install uvicorn
- Clone the repo
git clone https://github.com/Capital2/Big-M.git
- Install NPM packages
npm install
- Install python dependencies
pip install -r requirements.txt
from BigM import BigM
from BigM import graph
from BigM import InputHandling
import numpy as np
lp = ["Max Z = x+5y","6x+5y<=60","x+2y<=14", "x<=9"]
bm = BigM.BigM()
formattedInput = np.array(InputHandling.formatUserInput(lp))
results = bm.runBigM(formattedInput)
graph.drawGraph(formattedInput, slider=True)
- Representation with graphs
- Resolution of R2 and R3 problems
See the open issues for a full list of proposed features (and known issues).
Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature
) - Commit your Changes (
git commit -m 'Add some AmazingFeature'
) - Push to the Branch (
git push origin feature/AmazingFeature
) - Open a Pull Request
Distributed under the GNU/GPLv3 Licence. See LICENSE.txt
for more information.
Project Link: https://github.com/Capital2/Big-M