Sublinear Space Algorithms for the Longest Common Substring Problem

Given $m$ documents of total length $n$, we consider the problem of finding a longest string common to at least $d \geq 2$ of the documents. This problem is known as the \emph{longest common substring (LCS) problem} and has a classic $O(n)$ space and $O(n)$ time solution (Weiner [FOCS'73], Hui [CPM'92]). However, the use of linear space is impractical in many applications. In this paper we show that for any trade-off parameter $1 \leq τ\leq n$, the LCS problem can be solved in $O(τ)$ space and $O(n^2/τ)$ time, thus providing the first smooth deterministic time-space trade-off from constant to linear space. The result uses a new and very simple algorithm, which computes a $τ$-additive approximation to the LCS in $O(n^2/τ)$ time and $O(1)$ space. We also show a time-space trade-off lower bound for deterministic branching programs, which implies that any deterministic RAM algorithm solving the LCS problem on documents from a sufficiently large alphabet in $O(τ)$ space must use $Ω(n\sqrt{\log(n/(τ\log n))/\log\log(n/(τ\log n)})$ time.

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