English

Tight Bounds for Sketching the Operator Norm, Schatten Norms, and Subspace Embeddings

Data Structures and Algorithms 2022-02-22 v1

Abstract

We consider the following oblivious sketching problem: given ϵ(0,1/3)\epsilon \in (0,1/3) and nd/ϵ2n \geq d/\epsilon^2, design a distribution D\mathcal{D} over Rk×nd\mathbb{R}^{k \times nd} and a function f:Rk×RndRf: \mathbb{R}^k \times \mathbb{R}^{nd} \rightarrow \mathbb{R}, so that for any n×dn \times d matrix AA, PrSD[(1ϵ)Aopf(S(A),S)(1+ϵ)Aop]2/3,\Pr_{S \sim \mathcal{D}} [(1-\epsilon) \|A\|_{op} \leq f(S(A),S) \leq (1+\epsilon)\|A\|_{op}] \geq 2/3, where Aop\|A\|_{op} is the operator norm of AA and S(A)S(A) denotes SAS \cdot A, interpreting AA as a vector in Rnd\mathbb{R}^{nd}. We show a tight lower bound of k=Ω(d2/ϵ2)k = \Omega(d^2/\epsilon^2) for this problem. Our result considerably strengthens the result of Nelson and Nguyen (ICALP, 2014), as it (1) applies only to estimating the operator norm, which can be estimated given any OSE, and (2) applies to distributions over general linear operators SS which treat AA as a vector and compute S(A)S(A), rather than the restricted class of linear operators corresponding to matrix multiplication. Our technique also implies the first tight bounds for approximating the Schatten pp-norm for even integers pp via general linear sketches, improving the previous lower bound from k=Ω(n26/p)k = \Omega(n^{2-6/p}) [Regev, 2014] to k=Ω(n24/p)k = \Omega(n^{2-4/p}). Importantly, for sketching the operator norm up to a factor of α\alpha, where α1=Ω(1)\alpha - 1 = \Omega(1), we obtain a tight k=Ω(n2/α4)k = \Omega(n^2/\alpha^4) bound, matching the upper bound of Andoni and Nguyen (SODA, 2013), and improving the previous k=Ω(n2/α6)k = \Omega(n^2/\alpha^6) lower bound. Finally, we also obtain the first lower bounds for approximating Ky Fan norms.

Keywords

Cite

@article{arxiv.2202.09797,
  title  = {Tight Bounds for Sketching the Operator Norm, Schatten Norms, and Subspace Embeddings},
  author = {Yi Li and David P. Woodruff},
  journal= {arXiv preprint arXiv:2202.09797},
  year   = {2022}
}

Comments

Appeared in the Proceedings of RANDOM/APPROX 2016. The current version corrects the proof of Corollary 7