For the range minimum query (RMQ) problem, we are given an array $A$ of size $n$ and two indices $i$ and $j$ with $0 \leq i \leq j < n$. We then want to determine the smallest element in $\bigl \lbrace A[i], A[i + 1], \ldots, A[j] \bigr \rbrace$. The program implements different RMQ algorithms. They are mostly taken from [1].

The algorithms all have two parts to it: a pre-processing and query. The pre-processing processes the given array A and then stores results that allow for a faster query. The query then determines the minimum in the specified rang with the help of the pre-processing results. Subsequently, these algorithms have two runtimes. We write that the runtime of an algorithm is in $\bigl \langle \mathcal{O} \bigl( f(n) \bigr), \mathcal{O} \bigl( g(n) \bigr) \bigr \rangle$ time if the pre-processing runs in $\mathcal{O} \bigl( f(n) \bigr)$ time, and a single query runs in $\mathcal{O} \bigl( g(n) \bigr)$ time.

• No Pre-Processing. As the name suggests, this algorithm does not do any pre-processing at all. Subsequently, it iterate over the requested range of $A$ in each query. Runtime: $\bigl \langle \mathcal{O}(0), \mathcal{O}(j - i) \bigr \rangle$.

• Naive. This is a straight forward implementation of an RMQ algorithm. It computes all $n^2$ possible results and stores them in a table $T$. A query then simply reads the value out of $T[i][j]$. Runtime: $\bigl \langle \mathcal{O} \bigl( n^2 \bigr), \mathcal{O}(1) \bigr \rangle$.

• Segment Tree. This algorithms builds a full binary tree on top of $A$ which has the elements of $A$ as leaves. Each node $u$ then stores the minimum of all leaves that have $u$ as ancestor. Such a tree is called Segment Tree. It allows to run queries in logarithmic time by searching for $i$ and $j$ in the tree. Runtime: $\bigl \langle \mathcal{O}(n), \mathcal{O}(\log n) \bigr \rangle$.

• Cache-Oblivious Segment Tree. This algorithm works overall the same way as the Segment Tree algorithm above. The difference, however, is the way the nodes are stored in memory. They follow a van Emde Boas layout as described in [2, 3]. Although the overall runtime is the same (asymptotically), there are asymptotically fewer cache misses when accessing nodes, leading to an overall better performance. Runtime: $\bigl \langle \mathcal{O}(n), \mathcal{O}(\log n) \bigr \rangle$.

• Sparse Table. This algorithm is similar to the naive implementation in the sense that it computes a lookup table. That table, however, is much smaller. It only stores $\log n$ many rows, resulting in $\mathcal{O}(n \log n)$ total memory usage. At the same time, it still allows to perform a query in constant time (although with non-trivial operations). Runtime: $\bigl\langle \mathcal{O}(n \log n), \mathcal{O}(1) \bigr\rangle$.

• ±1 RMQ. Consider an Euler tour over a tree where we store the height of each node whenever we encounter it. In the resulting sequence, subsequent elements differ by either +1 or -1. That is called the ±1 restriction, and the goal of this algorithm is to make use of that restriction. The overall approach is similar to the Sparse Table. However, the given array $A$ is first split into small blocks of size $(\log n) / 2$. Given that small size and the strong restrictions for the input (there are only $\sqrt{n}$ different blocks possible. The properties allow the following overall runtime: $\bigl\langle \mathcal{O}(n), \mathcal{O}(1) \bigr\rangle$.

We also implemented an algorithm that computes the lowest common ancestor of two nodes in a tree. The algorithm uses an Euler tour of the given tree and then performs a RMQ on that tour as described in [1]. For that it can use any of the RMQ algorithms described above.

[1] M.A. Bender, M. Farach-Colton: The LCA Problem Revisited. LATIN 2000, LNCS 1776, 88-94, 2000.

[2] M.A. Bender, E.D. Demaine, M. Farach-Colton: Cache-Oblivious B-Trees. SIAM J. Comput. 35(2), 341-358, 2005.

[3] H. Prokop: Cache-oblivious algorithms. Master’s thesis, MIT, June 1999.