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The Communication Complexity of Approximating Matrix Rank

Alexander A. Sherstov, Andrey A. Storozhenko

TL;DR

The communication complexity of approximating matrix rank, over any finite field F, is fully determine and the lower bound for any streaming algorithm with any streaming algorithm with <tex>$k$</tex> passes that approximates the rank of an input matrix is obtained.

Abstract

We fully determine the communication complexity of approximating matrix rank, over any finite field $\mathbb{F}$. We study the most general version of this problem, where $0\leq r<R\leq n$ are given integers, Alice and Bob's inputs are matrices $A,B\in\mathbb{F}^{n\times n}$, respectively, and they need to distinguish between the cases $\mathrm{rk}(A+B)=r$ and $\mathrm{rk}(A+B)=R$. We show that this problem has randomized communication complexity $Ω(1+r^{2}\log|\mathbb{F}|)$. This is optimal in a strong sense because $O(1+r^{2}\log|\mathbb{F}|)$ communication is sufficient to determine, for arbitrary $A,B$, whether $\mathrm{rk}(A+B)\leq r$. Prior to our work, lower bounds were known only for consecutive integers $r$ and $R$, with no implication for the approximation of matrix rank. Our lower bound holds even for quantum protocols and even for error probability $\frac{1}{2}-\frac{1}{4}|\mathbb{F}|^{-r/3}$, which too is virtually optimal because the problem has a two-bit classical protocol with error $\frac{1}{2}-Θ(|\mathbb{F}|^{-r})$. As an application, we obtain an $Ω(\frac{1}{k}\cdot n^{2}\log|\mathbb{F}|)$ space lower bound for any streaming algorithm with $k$ passes that approximates the rank of an input matrix $M\in\mathbb{F}^{n\times n}$ within a factor of $\sqrt{2}-δ$, for any $δ>0$. Our result is an exponential improvement in $k$ over previous work. We also settle the randomized and quantum communication complexity of several other linear-algebraic problems, for all settings of parameters. This includes the determinant problem (given matrices $A$ and $B$, distinguish between the cases $\mathrm{det}(A+B)=a$ and $\mathrm{det}(A+B)=b$, for fixed field elements $a\ne b)$ and the subspace sum and subspace intersection problem (given subspaces $S$ and $T$ of known dimensions $m$ and $\ell$, respectively, approximate the dimensions of $S+T$ and $S\cap T$).

The Communication Complexity of Approximating Matrix Rank

TL;DR

The communication complexity of approximating matrix rank, over any finite field F, is fully determine and the lower bound for any streaming algorithm with any streaming algorithm with <tex></tex> passes that approximates the rank of an input matrix is obtained.

Abstract

We fully determine the communication complexity of approximating matrix rank, over any finite field . We study the most general version of this problem, where are given integers, Alice and Bob's inputs are matrices , respectively, and they need to distinguish between the cases and . We show that this problem has randomized communication complexity . This is optimal in a strong sense because communication is sufficient to determine, for arbitrary , whether . Prior to our work, lower bounds were known only for consecutive integers and , with no implication for the approximation of matrix rank. Our lower bound holds even for quantum protocols and even for error probability , which too is virtually optimal because the problem has a two-bit classical protocol with error . As an application, we obtain an space lower bound for any streaming algorithm with passes that approximates the rank of an input matrix within a factor of , for any . Our result is an exponential improvement in over previous work. We also settle the randomized and quantum communication complexity of several other linear-algebraic problems, for all settings of parameters. This includes the determinant problem (given matrices and , distinguish between the cases and , for fixed field elements and the subspace sum and subspace intersection problem (given subspaces and of known dimensions and , respectively, approximate the dimensions of and ).

Paper Structure

This paper contains 44 sections, 81 theorems, 376 equations.

Key Result

Theorem 1.1

There is an absolute constant $c>0$ such that for all finite fields $\mathbb{F}$ and all integers $n,m,R,r$ with $\min\{n,m\}\geqslant R>r\geqslant0,$ In particular,

Theorems & Definitions (101)

  • Theorem 1.1: Lower bound for rank problem
  • Theorem 1.2: Upper bound for rank problem
  • Theorem 1.3
  • Corollary 1.4
  • Corollary 1.5
  • Theorem 1.6
  • Theorem 1.7
  • Theorem 1.8
  • Theorem 1.9
  • Theorem 1.10
  • ...and 91 more