An oblivious subspace embedding (OSE), characterized by parameters $m,n,d,\epsilon,\delta$, is a random matrix $\Pi\in \mathbb{R}^{m\times n}$ such that for any $d$-dimensional subspace $T\subseteq \mathbb{R}^n$, $\Pr_\Pi[\forall x\in T, (1-\epsilon)\|x\|_2 \leq \|\Pi x\|_2\leq (1+\epsilon)\|x\|_2] \geq 1-\delta$. For $\epsilon$ and $\delta$ at most a small constant, we show that any OSE with one nonzero entry in each column must satisfy that $m = \Omega(d^2/(\epsilon^2\delta))$, establishing the optimality of the classical Count-Sketch matrix. When an OSE has $1/(9\epsilon)$ nonzero entries in each column, we show it must hold that $m = \Omega(\epsilon^{O(\delta)} d^2)$, improving on the previous $\Omega(\epsilon^2 d^2)$ lower bound due to Nelson and Nguyen (ICALP 2014).

翻译：以 $,n,d,\ epsilon,\ pi2\leq (1\\ epsilon),\\\\\\\\\\ delta$)为特征的隐形子空间嵌入(OSE)是一个随机基质 $\ Pi\\ in\mathbb{R\\\\\\\\\\\\\n$,对于任何以美元为维的子空间 $,T\ subseteq\ \ subbb{\\\\\\\\\\\\\\ n$,$\\ pi\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\ 美元为特征的参数。当 OSESeu $/\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\