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Nearly Tight Lower Bounds for Succinct Range Minimum Query

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arxiv 2111.02318 v2 pith:UUUF73RQ submitted 2021-11-03 cs.DS cs.CC

classification cs.DScs.CC
keywords bitsquerytimelowerspacebounddataminimum
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

Given an array of distinct integers $A[1\ldots n]$, the Range Minimum Query (RMQ) problem requires us to construct a data structure from $A$, supporting the RMQ query: given an interval $[a,b]\subseteq[1,n]$, return the index of the minimum element in subarray $A[a\ldots b]$, i.e. return $\text{argmin}_{i\in[a,b]}A[i]$. The fundamental problem has a long history. The textbook solution which uses $O(n)$ words of space and $O(1)$ time by Gabow, Bentley, Tarjan (STOC 1984) and Harel, Tarjan (SICOMP 1984) dates back to 1980s. The state-of-the-art solution is presented by Fischer, Heun (SICOMP 2011) and Navarro, Sadakane (TALG 2014). The solution uses $2n-1.5\log n+n/\left(\frac{\log n}{t}\right)^t+\tilde{O}(n^{3/4})$ bits of space and $O(t)$ query time, where the additive $\tilde{O}(n^{3/4})$ is a pre-computed lookup table used in the RAM model, assuming the word-size is $\Theta(\log n)$ bits. On the other hand, the only known lower bound is proved by Liu and Yu (STOC 2020). They show that any data structure which solves RMQ in $t$ query time must use $2n-1.5\log n+n/(\log n)^{O(t^2\log^2t)}$ bits of space, assuming the word-size is $\Theta(\log n)$ bits. In this paper, we prove nearly tight lower bound for this problem. We show that, for any data structure which solves RMQ in $t$ query time, $2n-1.5\log n+n/(\log n)^{O(t\log^2t)}$ bits of space is necessary in the cell-probe model with word-size $\Theta(\log n)$ bits. We emphasize that, in terms of time complexity, our lower bound is tight up to a polylogarithmic factor.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Optimal Time-Space Tradeoff for Dynamic Difference-Encoded Dictionaries

    cs.DS 2026-08 conditional novelty 8.0 of 10

    For any epsilon, a dictionary storing S uses gap(S)(1+O(epsilon)) + O(n log gap(S)) bits and answers membership in O(log(1/epsilon)/log log(1/epsilon)) time, with a matching static lower bound.

  2. Near-Optimal and Efficient Encoding for Two-Dimensional Range Minimum Queries

    cs.DS 2026-07 accept novelty 7.0 of 10

    For any κ in [1, log log n], an m×n array admits an O(κ mn (log m + log log n))-bit 2D-RMQ encoding with O(log^{1/κ} n) query time.

  3. Optimal Static Dictionary with Worst-Case Constant Query Time

    cs.DS 2024-12 conditional novelty 7.0 of 10

    A static dictionary can now use OPT + n^ε bits for any constant ε > 0 and still answer every query in worst-case constant time.

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