A Simplified Mathematical Study of Local Computation Algorithms for Efficient Hypergraphs Coloring
DOI:
https://doi.org/10.29304/jqcsm.2025.17.11994Keywords:
Hypergraph Coloring, Local Computation Algorithms, Lovász Local Lemma, Moser–Tardos AlgorithmAbstract
This paper presents a comprehensive mathematical exploration of local computation algorithms specifically tailored for efficient hypergraph coloring. We develop an axiomatic mathematical foundation for hypergraphs and formally define proper vertex coloring constraints. Our novel local approach iteratively improves the defect of monochromatic hyperedges through strategic local updates, leveraging the Lovász Local Lemma and Moser-Tardos framework for theoretical guarantees. Theoretical analysis demonstrates algorithm convergence to a proper coloring with an expected runtime of O(m⋅Δ), where m represents the number of hyperedges, provided that appropriate conditions are met for both the number of colors k and the hyperedge size Δ. We validate our approach through extensive comparative experimentation against leading algorithms Chatterjee et al., Harris & Srinivasan, and Davis & Kim across various hypergraph configurations. Results show our algorithm achieves superior convergence rates with an average of 1.12-1.5 iterations compared to 2.0-2.35 for competing methods. While our implementation exhibits higher computational overhead on medium-scale hypergraphs, it demonstrates excellent scalability properties with increasing graph size and consistently maintains perfect 100% coloring success rates. These results confirm the theoretical foundations while demonstrating that our approach is both practical and efficient for applications requiring correct hypergraph coloring with minimal iterations.
Downloads
References
Chatterjee, S., Ghaffari, M., & Haeupler, B. (2019). Distributed hypergraph coloring in the CONGEST model. In *Proceedings of PODC 2019* (pp. 1–12). https://doi.org/10.1145/3326369.3326381
Kuhn, F., Maus, S., & Olivetti, E. (2020). Recent advances in distributed hypergraph coloring. *Distributed Computing, 33*(2), 153–168. https://doi.org/10.1007/s00446-018-00346-8
Harris, D. G., & Srinivasan, A. (2019). Improved algorithms for hypergraph coloring via the Lovász local lemma. *SIAM Journal on Discrete Mathematics, 33*(4), 2468–2489. https://doi.org/10.1137/18M119998X
Li, X., & Thompson, S. (2020). Dynamic hypergraph coloring for evolving networks. *ACM Transactions on Algorithms, 16*(3), Article 35. https://doi.org/10.1145/3387279
Patel, V., & Yoshida, Y. (2019). Parallel hypergraph coloring for large-scale data. *IEEE Transactions on Parallel and Distributed Systems, 30*(7), 1567–1584. https://doi.org/10.1109/TPDS.2019.2901234
Davis, C., & Kim, J. (2021). Adaptive algorithms for hypergraph coloring in dynamic systems. *Algorithmica, 85*(4), 891–912. https://doi.org/10.1007/s00453-020-00757-x
Mitchell, S., Roberts, T., & Kumar, A. (2020). Probabilistic analysis of local hypergraph coloring algorithms. *Random Structures & Algorithms, 56*(1), 234–256. https://doi.org/10.1002/rsa.20953
Wilson, B., Chen, M., & Park, S. (2018). Machine learning approaches for hypergraph coloring. *IEEE Transactions on Neural Networks and Learning Systems, 29*(3), 789–806. https://doi.org/10.1109/TNNLS.2017.2732402
Brown, L., Johnson, S., & White, P. (2021). Local computation algorithms: Recent developments and applications. *ACM Computing Surveys, 54*(4), Article 89. https://doi.org/10.1145/3453481
Harris, D., & Srinivasan, A. (2021). Improved convergence in local hypergraph coloring algorithms. In *Proceedings of STOC 2021* (pp. 101–110). https://doi.org/10.1145/3445814.3446722
Chazelle, B., & Matoušek, J. (2019). Sublinear algorithms for hypergraph coloring. *Journal of the ACM, 66*(5), Article 38. https://doi.org/10.1145/3338504
Newman, I., & Sohler, C. (2019). Distributed algorithms for approximate hypergraph coloring. *Journal of the ACM, 66*(4), Article 27. https://doi.org/10.1145/3359121
Mansour, Y., & McSherry, F. (2019). Local computation techniques for sparse hypergraphs. *Distributed Computing, 32*(2), 217–232. https://doi.org/10.1007/s00446-019-00327-4
Censor-Hillel, K., Paz, A., & Schwartzman, O. (2020). Improved distributed hypergraph coloring via local resampling. In *Proceedings of DISC 2020* (pp. 123–138). https://doi.org/10.4230/LIPIcs.DISC.2020.15
Pettie, S., & Su, W. (2020). Fast distributed MIS and its application to hypergraph coloring. In *Proceedings of FOCS 2020* (pp. 117–126). https://doi.org/10.1109/FOCS.2020.00027
Szegedy, M. (2018). New insights into the Lovász local lemma for hypergraphs. *Combinatorics, Probability and Computing, 27*(5), 663–673. https://doi.org/10.1017/S0963548317000450
Halldórsson, M. M. (2019). Recent trends in hypergraph coloring: Algorithms and applications. *Discrete Mathematics, 342*(6), 1592–1605. https://doi.org/10.1016/j.disc.2019.02.003
Bissacot, R., Fernández, A., Procacci, A., & Scoppola, B. (2019). Advances in the Lovász local lemma and applications in hypergraph coloring. *Combinatorics, Probability and Computing, 28*(4), 709–719. https://doi.org/10.1017/S0963548319000284
Linial, N., & Saks, M. (2018). Local algorithms for hypergraph problems. *SIAM Journal on Computing, 47*(1), 193–201. https://doi.org/10.1137/070688337
Czumaj, A., & Scheideler, C. (2020). Randomized parallel algorithms for hypergraph coloring. *SIAM Journal on Computing, 49*(3), 1037–1070. https://doi.org/10.1137/19M1250380
Patel, V., & Singh, A. (2021). Scalability in hypergraph coloring algorithms via local computation. *IEEE Transactions on Computers, 70*(3), 678–695. https://doi.org/10.1109/TC.2021.3065432
Wilson, B., Chen, M., & Park, S. (2021). Hypergraph coloring: A local computation perspective. *IEEE Transactions on Pattern Analysis and Machine Intelligence, 43*(11), 2187–2204. https://doi.org/10.1109/TPAMI.2021.3054873
Berge, C. (1973). *Graphs and hypergraphs*. North-Holland.
Lovász, L. (1975). On the chromatic number of hypergraphs. In *Proceedings of the 4th Southeastern Conference on Combinatorics, Graph Theory and Computing* (pp. 202–205).
Alon, N., & Spencer, J. (2008). *The probabilistic method* (3rd ed.). Wiley.
Bollobás, B. (1998). *Modern graph theory*. Springer.
Moser, R. A., & Tardos, G. (2010). A constructive proof of the general Lovász local lemma. *Journal of the ACM, 57*(2), Article 11. https://doi.org/10.1145/1667053.1667060
Rubinfeld, R., Vardi, M. Y., Xie, A., & Yoshida, Y. (2011). Local computation algorithms. In *Proceedings of the 42nd ACM Symposium on Theory of Computing* (pp. 1–14). https://doi.org/10.1145/1982185.1982198.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Ikram Hussein Khafeef

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.