Research Data Engineering

Tusnády's problem and online two-sample thinning

Nov 5, 2026 Source ↗

Key Insights

We determine the sharp order of discrepancy in Tusnády's problem in every fixed dimension $d\ge 2$.
The largest combinatorial discrepancy of $N$ points in $\mathbb{R}^d$ with respect to axis-parallel.

DataOps

Cite this synthesis DOI: 10.48550/arXiv.2611.08455 ↗

Show formatted citation

@misc{ acaciadata-research-tusn-dys-problem-and-online-two-sample-thinning,
  title = { Tusnády's problem and online two-sample thinning },
  author = { Leszek },
  year = { 2026 },
  doi = { 10.48550/arXiv.2611.08455 },
  url = { http://arxiv.org/abs/2611.08455v1 },
  note = {Summarized and classified by AcaciaFund}
}

TY  - GEN
TI  - Tusnády's problem and online two-sample thinning
AU  - Leszek
PY  - 2026
DO  - 10.48550/arXiv.2611.08455
UR  - http://arxiv.org/abs/2611.08455v1
ER  -

Edit on GitHub — registry.json

Overview

We determine the sharp order of discrepancy in Tusnády's problem in every fixed dimension $d\ge 2$. The largest combinatorial discrepancy of $N$ points in $\mathbb{R}^d$ with respect to axis-parallel

Key Topics

Distributed Systems: A theme covered by this item within the data engineering context.

Why It Matters

This item adds to the data engineering knowledge base. Practitioners can use it to stay current on Tusnády's problem and online two-sample thinning, but should validate its claims against primary sources and more recent work before relying on it in production decisions.

Key Takeaways

Understand how Tusnády's problem and online two-sample thinning relates to data engineering workflows and controls.
Assess evidence quality and freshness before acting on the findings.
Use the tagged topics to connect this item to related content in the library.

Article Metadata

Evergreen Synthesized

distributed-systems dataops

DataOps

SQI 0.750