streaming algorithm designers

Abstract: We study the space complexity of diameter estimation for a set of points in Euclidean space in the dynamic (turnstile) streaming model. The seminal work of Indyk (SODA 2003) gives a $c$-approximation to the Euclidean diameter of $n$ vectors using $n^{O(1/c^2)}$ space. Our main contribution is giving an essentially matching lower bound. Any dynamic streaming algorithm which can $c$-approximate the diameter of $n$ Euclidean vectors must use $n^{\tildeΩ(1/c^2)}$ space.

Abstract: We study the canonical \textsf{Maximum Clique} and \textsf{Maximum Independent Set} problems in the one-pass edge-arrival graph streaming setting. Here, the edges of some input graph $G = (V,E)$ are presented one at a time (possibly including deletions), before an algorithm needs to produce either a large clique or independent set at the end of the stream, with the focus being on space complexity. We are interested in finding $β$-approximate solutions, for any $β\geq 1$. Previous work gave an algorithm using $\tilde{O}\left(n^2/β^2\right)$ bits of space, together with a corresponding $\tildeΩ\left(n^2/β^2\right)$ two-party communication lower bound [Halldórsson et al., ICALP'12], seeming to resolve the problem. However, their algorithm crucially relies on randomness, and the best known deterministic algorithm remains a folklore derandomisation using $O\left(n^2/β\right)$ bits of space, leaving a (deterministic) gap of size $\tilde{O}(β)$. We resolve this deterministic gap with an (almost) tight lower bound: any deterministic algorithm for either problem must use $Ω\left(\frac{n^2}{β\cdot\log n}\right)$ bits of space. Our proof is via a two-party one-way communication lower bound, and highlights the power of randomness when approaching either of these problems.