Cell-Probe Lower Bounds and Complexity-Preserving Reductions for Suffix Array Queries
2026-08-19 • Data Structures and Algorithms
Data Structures and Algorithms
AI summaryⓘ
The authors study how quickly and efficiently one can answer suffix-array queries, which are used to sort suffixes of a text, especially focusing on binary strings. They prove new lower bounds showing that small data structures can't give instant answers, solving a long-standing open problem. Additionally, they show that these suffix-array queries are fundamentally as hard as certain prefix query problems, with precise equivalences that keep space and time costs similar. This means improvements in one area directly reflect in the other, without hidden time delays.
suffix arrayinverse suffix arrayFM-indexcompressed data structurescell-probe modelquery time lower boundspace complexityprefix queriesbinary stringsdata structure reductions
Authors
Dominik Kempa, Tomasz Kociumaka
Abstract
For a text $T$ of length $n$ over an alphabet of size $σ$, its suffix array lists the starting positions of the suffixes of $T$ in lexicographic order, and its inverse suffix array gives the lexicographic rank of the suffix starting at each position. Since the introduction of the FM-index and the compressed suffix array in 2000, both queries have been supported in $O((\log_σn)^ε)$ time using $O(n\logσ)$ bits, for any constant $ε>0$. Yet no nontrivial time-space lower bound for suffix-array queries was known. We give the first such lower bound. Specifically, we show that, in the cell-probe model with $Θ(\log n)$-bit words, every $S$-bit data structure answering suffix-array queries on binary strings of length at most $n$ has query time $Ω(\log\log n/\log((S/n)\log\log n))$. Consequently, every structure using $O(n(\log\log n)^{O(1)})$ bits requires $Ω(\log\log n/\log\log\log n)$ query time, while constant query time requires $Ω(n\log^εn)$ bits for some constant $ε>0$. In particular, no $O(n)$-bit suffix-array representation for binary texts supports constant-time queries, answering the 25-year-old question of Grossi and Vitter. We also give exact complexity-preserving equivalences between suffix-array access and simpler prefix queries on short strings. For every $2\leqσ\leq n$, suffix-array queries are equivalent to prefix-select queries, and inverse-suffix-array queries are equivalent to prefix-special-rank queries. The reductions in both directions preserve all four standard measures up to constant factors: space, query time, preprocessing time, and preprocessing space. Unlike previous reductions, they incur no additive $O(\log\log n)$ query-time term. Thus, the corresponding prefix-query problems capture suffix-array and inverse-suffix-array access without asymptotic loss in any of the four measures.