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The Partition Principle Revisited: Non-Equal Volume Designs Achieve Minimal Expected Star Discrepancy

Machine Learning 2026-03-10 v2 Machine Learning Probability

Abstract

We study the expected star discrepancy under a newly designed class of non-equal volume partitions. The main contributions are twofold. First, we establish a strong partition principle for the star discrepancy, showing that our newly designed non-equal volume partitions yield stratified sampling point sets with lower expected star discrepancy than classical jittered sampling. Specifically, we prove that E(DN(Z))<E(DN(Y))\mathbb{E}(D^{*}_{N}(Z)) < \mathbb{E}(D^{*}_{N}(Y)), where YY and ZZ represent jittered sampling and our non-equal volume partition sampling, respectively. Second, we derive explicit upper bounds for the expected star discrepancy under our non-equal volume partition models, which improve upon existing bounds for jittered sampling. Our results provide a theoretical foundation for using non-equal volume partitions in high-dimensional numerical integration.

Keywords

Cite

@article{arxiv.2603.00202,
  title  = {The Partition Principle Revisited: Non-Equal Volume Designs Achieve Minimal Expected Star Discrepancy},
  author = {Xiaoda Xu},
  journal= {arXiv preprint arXiv:2603.00202},
  year   = {2026}
}

Comments

Wrong in critical steps