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Alice has a vector $x \in [r]^d$ and Bob has $y \in [r]^d$, where $[r] \stackrel{\rm def}{=} \{0,1,\dots,r\}$. Alice send a message $M(x)$ to Bob and Bob wants to estimate the inner product $\left<x,y\right>$ with multiplicative error $\epsilon$ with probability $1 - \delta$.

My question is. Is there any known lower bound on $|M(x)|$? My intuition tells me that Alice pretty much has to estimate each coordinate of $x$ up to $\epsilon$ precision since Bob can have an input that zeros out all but one of the entry. Does that give you something like $\Omega(d \log 1/\epsilon)$?

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  • $\begingroup$ What is $[r]$? Do you mean, e.g., $[0,r]$? $\endgroup$ – Neal Young Apr 10 at 20:54
  • $\begingroup$ I should have clarified, it means integers from 0 to r. $\endgroup$ – Dawei Huang Apr 10 at 20:55
  • $\begingroup$ Then surely your bound should depend on $r$? E.g., even with $\epsilon=d=1$ and $\delta=1/2$, Bob needs to know something about the magnitude of $x_1$? $\endgroup$ – Neal Young Apr 10 at 20:57
  • $\begingroup$ Then maybe it should be something like $d(\log \log r + \log 1/\epsilon)$? This is the number of bits you need to if we round $x_i$ to the nearest exponent of $1+\epsilon$. $\endgroup$ – Dawei Huang Apr 10 at 21:06
  • $\begingroup$ I agree that Alice can do it deterministically with that many bits, and can't do better deterministically. If the inner product is at least some $T$, it would be enough for Alice to choose some $k=O(\log(1/\delta) d r^2/(T \epsilon^2))$ indices $i\in[d]$ at random, and just send the approximate $x_i$ for each of those. (Bob would compute the corresponding random sample.) This would be better when $\log(1/\delta) r^2 \ll \epsilon^2 T$. $\endgroup$ – Neal Young Apr 10 at 21:39
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In the indexing problem Alice has a vector $x \in \{0,1\}^d$ and Bob has a number $i$, and Bob wants to learn $x_i$. The randomized one-way communication complexity of this problem is $\Omega(d)$ (see Section 3 of this paper), and it can be solved by approximating $x_i = \langle x, e_i\rangle$ up to any multiplicative factor where $e_i$ is the vector with $1$ in the $i$-th coordinate and $0$'s everywhere else. So your problem has $\Theta(d)$ randomized one-way communication complexity for $r=1$.

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  • $\begingroup$ Thanks! I think this paper also contain a bound $\Omega(n/(T\epsilon^2))$ that also show dependence on $\epsilon$. I wonder whether it makes sense or not to ask how the bound generalizes to general $r$. $\endgroup$ – Dawei Huang Apr 11 at 18:04

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