Tight Lower Bound for Multicolor Discrepancy

📅 2025-04-25
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🤖 AI Summary
This paper investigates lower bounds on the *k*-color multicolor discrepancy of hypergraphs. For *n*-vertex hypergraphs under *k* ≥ 2 colorings, it establishes the first asymptotically tight Ω(√*n*) lower bound—eliminating the log *k* factor present in prior work by Caragiannis et al. and achieving theoretical optimality. Methodologically, the proof combines probabilistic construction, refined probabilistic analysis, and hypergraph combinatorics; crucially, it employs precise discrete probability estimates to avoid the logarithmic penalties inherent in classical approaches. Notably, the bound is independent of *k*, rendering it substantially stronger than all previous results—improving upon them by a factor of approximately √log *k*. This represents the strongest known theoretical lower bound for multicolor discrepancy, with direct implications for applications such as group-fair allocation and load balancing.

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📝 Abstract
We prove the following asymptotically tight lower bound for $k$-color discrepancy: For any $k geq 2$, there exists a hypergraph with $n$ vertices such that its $k$-color discrepancy is at least $Omega(sqrt{n})$. This improves on the previously known lower bound of $Omega(sqrt{n/log k})$ due to Caragiannis et al. (arXiv:2502.10516). As an application, we show that our result implies improved lower bounds for group fair division.
Problem

Research questions and friction points this paper is trying to address.

Prove tight lower bound for multicolor discrepancy
Improve previous bound from Ω(√(n/log k)) to Ω(√n)
Apply result to enhance group fair division bounds
Innovation

Methods, ideas, or system contributions that make the work stand out.

Proves tight lower bound for multicolor discrepancy
Improves previous bound by Caragiannis et al
Applies to group fair division bounds
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