Here is a list of typical problems and how to execute with Python in Combinatorial optimization. ([Let's run](# Let's run)) For a more detailed explanation, refer to the book "Combinatorial Optimization".
--Nodes not selected in the solution of the maximum stable set problem become the solution of the vertex cover problem. ――Delivery optimization is often called XX plan like delivery plan, but XX plan is old name.
Install Docker Toolbox, and from Kitematic, Docker image tsutomu7 / typical_optimizationPlease run the.
Once done, open http: // localhost: 8888. The password for Jupyter Notebook is jupyter.
For installing Docker, please also refer to Until you start Jupyter with Docker.
Please install the following software. After installation, you can run the code linked to each of the above issues.
--Installation of Python and pip: Select the target OS in the Environment Construction Guide and install it.
--We recommend the latest version of 3 series. However, some libraries may not work with the latest version, in which case the older version will be more stable.
--Library installation:
pip install pandas matplotlib jupyter more-itertools scipy networkx ortoolpy
--ortoolpy: For typical problems. Some have minimal functionality and some are inefficient. PuLP (modeler and solver for mathematical optimization) is also installed.
--Reference: There is also a method using Anaconda, but the management of the library is different from the above method (Anaconda integrates various packages for Python and science and technology). This is the distribution that I did).
-Mathematical Optimization Modeler PuLP Cheat Sheet
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