Sharp spectral norm concentration of sparse random tensors

2026-09-17

AI Summary

研究解决了稀疏随机张量谱范数的精确集中问题，通过改进Kahn-Szemerédi分解法并去除对数因子，得到更紧致的概率界限。

Abstract

We prove a sharp concentration inequality for the spectral norm of sparse random tensors with independent Bernoulli entries. Let $T$ be an order-$k$ tensor of dimension $n\times\cdots\times n$ with independent Bernoulli$(p)$ entries, where $k$ is fixed. For any $c,r>0$, we show that $\|T-\mathbb E T\|\le C_{k,r,c}\sqrt{np}$ with probability at least $1-n^{-r}$ whenever $np\ge c\log n$. We extend this bound to inhomogeneous Bernoulli sampling with deterministic entrywise weights. This removes the logarithmic factor in the work of Zhou and Zhu (2021). The proof follows the Kahn--Szemerédi light--heavy decomposition with a refined estimate on the heavy tuple part. We also obtain a log-free second eigenvalue bound for the random hypergraph model of Friedman and Wigderson (1995).

Problem

• spectral norm
• sparse random tensors
• Bernoulli entries
• concentration inequality

Innovation

• sharp concentration inequality
• sparse random tensors
• spectral norm
• Bernoulli entries
• log-free bound