Lower Bounds for Sparse Oblivious Subspace Embeddings

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 $m = \Omega(d^{2/(\epsilon2\delta))$,} 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 $\Omega(\epsilon² d^2)$ lower bound due to Nelson and Nguyen (ICALP 2014).