---
id: SRQ
title: "Static Range Queries"
author: Benjamin Qi
description: "Range queries for any associative operation over a static array."
frequency: 1
---

import { Problem } from "../models";

export const metadata = {
  problems: {
    rmqSample: [
      new Problem("YS", "Static RMQ", "staticrmq", "Easy", false, [], "equivalent to [CSES Range Minimum Queries I](https://cses.fi/problemset/task/1647)"),
    ],
    diviSample: [      
      new Problem("ojuz", "JOI Secret", "JOI14_secret", "Easy", false, [], ""),
    ],
    general: [
      new Problem("CC", "Product on Segment", "SEGPROD", "Normal", false, [], ""),
      new Problem("DMOJ", "Continued Fractions", "dmopc19c7p4", "Hard", false, [], ""),
      new Problem("Plat", "Non-Decreasing Subsequences", "997", "Very Hard", false, [], ""),
    ],
  }
};

Given a static array $A[1],A[2],\ldots,A[N]$, you want to answer queries in the form $A[l]\ominus A[l+1]\ominus \cdots \ominus A[r]$ where $\ominus$ denotes any associative operation.

With $O(N\log N)$ time preprocessing, we can get $O(1)$ queries.

## [Range Minimum Query](https://en.wikipedia.org/wiki/Range_minimum_query)

First we'll consider the special case when $\ominus$ denotes `min`. 

<Problems problems={metadata.problems.rmqSample} />

### Resources

<Resources>
  <Resource source="CPH" title="9.1 - Minimum Queries" starred>diagrams</Resource>
  <Resource source="PAPS" title="11.2.2 - Sparse Tables" starred>code</Resource>
  <Resource source="cp-algo" title="RMQ" url="sequences/rmq.html"></Resource>
  <Resource source="cp-algo" title="Sparse Table" url="data_structures/sparse-table.html"></Resource>
</Resources>

<Optional title="Faster Preprocessing">

[CF: $O(1)$ Query RMQ with $O(N)$ build](https://codeforces.com/blog/entry/78931)
 
</Optional>

### Implementation

<IncompleteSection />

## Divide & Conquer

<Problems problems={metadata.problems.diviSample} />

**Divide & conquer** can refer to many different techniques. In this case, we use it to answer $Q$ queries offline in $O((N+Q)\log N)$ time. 

Suppose that all queries satisfiy $L\le l\le r\le R$ (initially, $L=1$ and $R=N$). Letting $M=\left\lfloor \frac{L+R}{2}\right\rfloor$, we can compute 
$$
lef[l]=A[l]\ominus A[l+1]\ominus \cdots \ominus A[M]
$$ 
for all $L\le l\le M$ and 
$$
rig[r]=A[M+1]\ominus A[M+2] \ominus \cdots\ominus A[r]
$$ 
for each $M< r\le R$. Then the answer for all queries satisfying $l\le M< r$ is simply $lef[l]\ominus rig[r]$ due to the associativity condition. After that, we recurse on all query intervals completely contained within $[L,M]$ and $[M+1,R]$ independently.

Actually, this can be adjusted to answer queries online in $O(1)$ time each. See my implementation [here](https://github.com/bqi343/USACO/blob/master/Implementations/content/data-structures/Static%20Range%20Queries%20(9.1)/RangeQuery.h).

<Optional title="Faster Preprocessing">

A data structure known as **sqrt-tree** can speed up preprocessing time to $O(N\log \log N)$.

 - [CF Blog Pt 1](http://codeforces.com/blog/entry/57046)
 - [CF Blog Pt 2](http://codeforces.com/blog/entry/59092)

</Optional>

### Implementation

<IncompleteSection />

## Problems

<Problems problems={metadata.problems.general} />