# On the Triangle Inequality for the Jaccard Distance in Arbitrary Lattices

arXiv: https://arxiv.org/abs/2608.18194 · HTML: https://mlsanity.com/paper/2608.18194/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 18 Aug 2026.

## Builds on (most cited first, 5 of 5)

- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [A note on the triangle inequality for the Jaccard distance](https://mlsanity.com/paper/1612.02696/) (2016)
- [A (Slightly) Improved Approximation Algorithm for Metric TSP](https://mlsanity.com/paper/2007.01409/) (2020)
- [A Triangle Inequality for Cosine Similarity](https://mlsanity.com/paper/2107.04071/) (2021)
- [Fast and Private Max-Sum Diversification](https://mlsanity.com/paper/2607.17196/) (2026)

## Abstract (arXiv)

This paper presents new theoretical results on generalizing the Jaccard distance for lattices and real valuations. We demonstrate that when the valuation is strictly positive, monotone, and modular, the Jaccard distance satisfies the triangle inequality on arbitrary lattices, effectively generalizing earlier results that depended heavily on distributivity. Moving to relatively complemented distributive lattices (which safely drop the requirement for the global bounds found in Boolean algebras), we prove the triangle inequality holds as long as the valuation is positive, monotone, supermodular, and log-submodular. Additionally, we adapt the symmetric-difference Jaccard formulation for submodular valuations to sectionally complemented distributive lattices. Shifting to necessary conditions, we prove that supermodularity is a strict requirement for the standard generalized Jaccard distance to operate as a valid metric. Finally, we map the practical value of relaxing these structural constraints to computational fields like quantum information theory, formal concept analysis, and machine learning, closing with a brief look at open mathematical problems.