Added New Hypergraph Matching Algorithms

[rotshira]

This pull request introduces several enhancements and new features to the HyperNetX library:

1. New Matching Algorithms:

   • Implemented three new hypergraph matching algorithms that provide better approximations than the greedy matching algorithm.
   • Each algorithm accepts an additional parameter for a custom maximum matching function, allowing for flexibility in usage.

2. Iterated Sampling Algorithm:

   • An implementation of the iterated sampling algorithm for hypergraph matching. This algorithm uses a probabilistic approach to find an approximate matching in hypergraphs.

3. HEDCS-Based Approximation Algorithm:

   • Added the HEDCS-based approximation algorithm for hypergraph matching. This algorithm constructs a Hyper-Edge Degree Constrained Subgraph (HEDCS) to find a maximal matching.

4. Greedy Matching Algorithm:

   • Enhanced the greedy matching algorithm to be more efficient and scalable.

5. Logging and Debugging:

   • Added extensive logging to all algorithms to aid in debugging and performance analysis.

6. Experiments and Performance Comparison:

   • Created scripts to run performance comparisons of the algorithms on large random inputs using the experiments_csv library.
   • Generated and analyzed results to compare the size of the match and the running time of each algorithm.

Testing:

• Thoroughly tested the new algorithms with various input sizes and configurations.
• Added doctests to ensure the correctness and robustness of the algorithms.

Documentation:

• Updated the documentation to include descriptions and usage examples for the new algorithms.

Instructions for Reviewers:

• Please review the new algorithms for correctness and performance.
• Verify the logging outputs for clarity and usefulness.

Thank you for considering this contribution to the HyperNetX library.

[brendapraggastis]

@rotshira Thank you for the generous contribution. We are reviewing your request to see how best to integrate it. Do you have a Python tutorial (jupyter notebook) that could highlight your work and reference your publication? This will be required to add it to the library - we haven't stipulated this requirement yet in the contributions description as all contributors have done this automatically. We will update it now to support new contributors. It may take a week or two for us to review and comment. Feel free to send up comments or additional code/notebook in the meantime.