Communication Complexity of Inner Product in Symmetric Normed Spaces

Abstract: We introduce and study the communication complexity of computing the inner product of two vectors, where the input is restricted w.r.t. a norm $N$ on the space $\mathbb{R}^n$. Here, Alice and Bob hold two vectors $v,u$ such that $|v|N\le 1$ and $|u|{N^*}\le 1$, where $N^*$ is the dual norm. They want to compute their inner product $\langle v,u \rangle$ up to an $\varepsilon$ additive term. The problem is denoted by $\mathrm{IP}N$. We systematically study $\mathrm{IP}_N$, showing the following results: - For any symmetric norm $N$, given $|v|_N\le 1$ and $|u|{N^*}\le 1$ there is a randomized protocol for $\mathrm{IP}N$ using $\tilde{\mathcal{O}}(\varepsilon^{-6} \log n)$ bits -- we will denote this by $\mathcal{R}{\varepsilon,1/3}(\mathrm{IP}{N}) \leq \tilde{\mathcal{O}}(\varepsilon^{-6} \log n)$. - One way communication complexity $\overrightarrow{\mathcal{R}}(\mathrm{IP}{\ell_p})\leq\mathcal{O}(\varepsilon^{-\max(2,p)}\cdot \log\frac n\varepsilon)$, and a nearly matching lower bound $\overrightarrow{\mathcal{R}}(\mathrm{IP}{\ell_p}) \geq \Omega(\varepsilon^{-\max(2,p)})$ for $\varepsilon^{-\max(2,p)} \ll n$. - One way communication complexity $\overrightarrow{\mathcal{R}}(N)$ for a symmetric norm $N$ is governed by embeddings $\ell\infty^k$ into $N$. Specifically, while a small distortion embedding easily implies a lower bound $\Omega(k)$, we show that, conversely, non-existence of such an embedding implies protocol with communication $k^{\mathcal{O}(\log \log k)} \log^2 n$. - For arbitrary origin symmetric convex polytope $P$, we show $\mathcal{R}(\mathrm{IP}_{N}) \le\mathcal{O}(\varepsilon^{-2} \log \mathrm{xc}(P))$, where $N$ is the unique norm for which $P$ is a unit ball, and $\mathrm{xc}(P)$ is the extension complexity of $P$.