# Compact-Table: Efficiently Filtering Table Constraints with Reversible Sparse Bit-Sets

arXiv: https://arxiv.org/abs/1604.06641 · HTML: https://mlsanity.com/paper/1604.06641/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2016.

## Built on by (most cited first, 6 of 6)

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- [ACE, a generic constraint solver](https://mlsanity.com/paper/2302.05405/) (2023)
- [Constrained Machine Learning: The Bagel Framework](https://mlsanity.com/paper/2112.01088/) (2021)
- [Faster Exact MPE and Constrained Optimization with Deterministic Finite State Automata](https://mlsanity.com/paper/2108.03899/) (2021)
- [A Constraint Programming Model for Scheduling the Unloading of Trains in Ports: Extended](https://mlsanity.com/paper/2312.13682/) (2023)
- [GPU Accelerated Compact-Table Propagation](https://mlsanity.com/paper/2507.18413/) (2025)

## Abstract (arXiv)

In this paper, we describe Compact-Table (CT), a bitwise algorithm to enforce Generalized Arc Consistency (GAC) on table con- straints. Although this algorithm is the default propagator for table constraints in or-tools and OscaR, two publicly available CP solvers, it has never been described so far. Importantly, CT has been recently improved further with the introduction of residues, resetting operations and a data-structure called reversible sparse bit-set, used to maintain tables of supports (following the idea of tabular reduction): tuples are invalidated incrementally on value removals by means of bit-set operations. The experimentation that we have conducted with OscaR shows that CT outperforms state-of-the-art algorithms STR2, STR3, GAC4R, MDD4R and AC5-TC on standard benchmarks.