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Compact Partial Symmetry Breaking for Graph Search Problems

17 pages•Published: October 23, 2026

Abstract

Symmetry breaking in graph search problems remains challenging: complete symmetry breaking constraints are typically exponential in size, while partial approaches trade pruning strength for scalability.
We introduce a strength-driven, redundancy-aware methodology for constructing compact partial symmetry breaking constraints based on graph patterns. Each pattern corresponds to a simple clause over equality literals and can be integrated directly into CP or SAT models.
Our approach incrementally selects strong, non-redundant patterns, yielding partial symmetry breaks that achieve substantial pruning while remaining compact. This provides a principled framework for deriving lightweight symmetry breaking constraints beyond previously studied constructions.
We evaluate the proposed constraints on graph instances with up to 25 vertices using a redundancy ratio metric. The resulting partial symmetry breaks constitute only a small fraction of the size of complete symmetry breaking constraints, while delivering significant performance improvements across several graph search benchmarks compared to previously defined partial symmetry breaking methods.

Keyphrases: cegar, graph symmetry, ramsey graphs

In: Erika Ábrahám, Nikolaj Bjørner and Michael Rawson (editors). Proceedings of the 26th Conference on Logic for Programming, Artificial Intelligence, and Reasoning, vol 112, pages 401-417.

BibTeX entry
@inproceedings{LPAR-26:Compact_Partial_Symmetry_Breaking,
  author    = {Michael Codish and Mikoláš Janota and Peter J. Stuckey},
  title     = {Compact Partial Symmetry Breaking for Graph Search Problems},
  booktitle = {Proceedings of the 26th Conference on Logic for Programming, Artificial Intelligence, and Reasoning},
  editor    = {Erika Ábrahám and Nikolaj Bjørner and Michael Rawson},
  series    = {EPiC Series in Computing},
  volume    = {112},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/zWKL},
  doi       = {10.29007/xvnx},
  pages     = {401-417},
  year      = {2026}}
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