Introduction to Monotonic Queues

 A monotonic queue is a data structure that supports efficient insertion, deletion, and retrieval of elements in a specific order, typically in increasing or decreasing order.

The monotonic queue can be implemented using different data structures, such as a linked list, stack, or deque. The most common implementation is using a deque (double-ended queue) container. The deque container allows efficient insertion and deletion of elements from both the front and back of the queue, which is useful for implementing a monotonic queue.

There are two main types of monotonic queues:

• Increasing Monotonic Queue: It only keeps elements in increasing order, and any element that is smaller than the current minimum is removed.
• Decreasing Monotonic Queue: It only keeps elements in decreasing order, and any element that is larger than the current maximum is removed.

Implement the idea below to solve the Increasing Monotonic Queue problem:

• The function starts by initializing an empty deque called q.
• Then, it loops through the input array. For each element in the array, it checks if the deque is not empty and if the last element in the deque is greater than the current element in the array.
• If this condition is true, the last element in the deque is popped out. This is because we only want to keep elements in increasing order and any element that is smaller than the current minimum is removed.
• After that, the current element in the array is pushed into the deque.
• This process is repeated for all elements in the input array
• At the end of the function, the deque containing the increasing monotonic queue is returned.

Here’s an example of an increasing monotonic queue implemented in C++:

C++

// C++ code for the above approach:
#include <bits/stdc++.h>
using namespace std;

// Function to solve Increasing
// Monotonic queue
deque<int> increasing_monotonic_queue(int arr[], int n)
{

    deque<int> q;
    for (int i = 0; i < n; i++) {

        // If recently added element is
        // greater current element
        while (!q.empty() && q.back() > arr[i]) {

            q.pop_back();
        }

        q.push_back(arr[i]);
    }

    return q;
}

// Driver code
int main()
{

    int arr[] = { 1, 2, 3, 4, 5, 6 };
    int n = sizeof(arr) / sizeof(arr[0]);

    // Function call
    deque<int> q = increasing_monotonic_queue(arr, n);

    for (int i : q) {
        cout << i << " ";
    }
    return 0;
}

Java

// Java code for the above approach:
import java.util.*;

class GFG {
    // Function to solve Increasing
    // Monotonic queue
    static Deque<Integer>
    increasing_monotonic_queue(int arr[], int n)
    {

        Deque<Integer> q = new LinkedList<Integer>();
        for (int i = 0; i < n; i++) {

            // If recently added element is
            // greater current element
            while (!q.isEmpty() && q.getLast() > arr[i]) {

                q.removeLast();
            }

            q.addLast(arr[i]);
        }

        return q;
    }

    // Driver code
    public static void main(String[] args)
    {

        int arr[] = { 1, 2, 3, 4, 5, 6 };
        int n = arr.length;

        // Function call
        Deque<Integer> q
            = increasing_monotonic_queue(arr, n);

        Iterator it = q.iterator();
        while (it.hasNext()) {
            System.out.print(it.next() + " ");
        }
    }
}

Python3

from collections import deque

def increasing_monotonic_queue(arr, n):
    q = deque()
    for i in range(n):
        while len(q) > 0 and q[-1] > arr[i]:
            q.pop()
        q.append(arr[i])
    return q

arr = [1, 2, 3, 4, 5, 6]
n = len(arr)
q = increasing_monotonic_queue(arr, n)
for i in q:
    print(i, end=' ')

C#

// C# code for the above approach:

using System;
using System.Collections.Generic;

public class GFG {

    // Function to solve Increasing Monotonic queue
    static Queue<int> IncreasingMonotonicQueue(int[] arr)
    {
        Queue<int> q = new Queue<int>();
        for (int i = 0; i < arr.Length; i++) {
            // If recently added element is greater than the
            // current element
            while (q.Count > 0 && q.Peek() > arr[i]) {
                q.Dequeue();
            }

            q.Enqueue(arr[i]);
        }

        return q;
    }

    static public void Main()
    {

        // Code
        int[] arr = { 1, 2, 3, 4, 5, 6 };
        // Function call
        Queue<int> q = IncreasingMonotonicQueue(arr);

        foreach(int i in q) { Console.Write(i + " "); }
    }
}

// This code is contributed by karthik.

Javascript

// Function to solve Increasing
// Monotonic queue
function increasing_monotonic_queue(arr, n) {
    const q = [];

    for (let i = 0; i < n; i++) {
        // If recently added element is greater than the current element
        while (q.length > 0 && q[q.length - 1] > arr[i]) {
            q.pop();
        }

        q.push(arr[i]);
    }

    return q;
}

// Driver code
const arr = [1, 2, 3, 4, 5, 6];
const n = arr.length;
const q = increasing_monotonic_queue(arr, n);

q.forEach((i) => {
    process.stdout.write(i + " ");
});

Output

1 2 3 4 5 6 

Implement the idea below to solve the Decreasing Monotonic Queue problem:

• The function starts by initializing an empty deque called q.
• Then, it loops through the input array. For each element in the array, it checks if the deque is not empty and if the last element in the deque is smaller than the current element in the array.
• If this condition is true, the last element in the deque is popped out. This is because we only want to keep elements in decreasing order and any element that is larger than the current maximum is removed.
• After that, the current element in the array is pushed into the deque.
• This process is repeated for all elements in the input array
• At the end of the function, the deque containing the decreasing monotonic queue is returned.

Here is an example of a decreasing monotonic queue implemented in C++:

C++

// C++ code for the above approach
#include <deque>
#include <iostream>
using namespace std;

// Function to calculate Decreasing
// Monotonic queue
deque<int> decreasing_monotonic_queue(int arr[], int n)
{

    deque<int> q;
    for (int i = 0; i < n; i++) {

        // If recently addded element is
        // smaller than current element
        while (!q.empty() && q.back() < arr[i]) {

            q.pop_back();
        }

        q.push_back(arr[i]);
    }

    return q;
}

// Driver Code
int main()
{
    int arr[] = { 6, 5, 4, 3, 2, 1 };
    int n = sizeof(arr) / sizeof(arr[0]);

    // Function call
    deque<int> q = decreasing_monotonic_queue(arr, n);

    for (int i : q) {
        cout << i << " ";
    }

    return 0;
}

Java

// Java code for the above approach
import java.io.*;
import java.util.*;

class GFG {

  // Function to calculate Decreasing Monotonic queue
  public static Deque<Integer>
    decreasing_monotonic_queue(int[] arr)
  {
    Deque<Integer> q = new ArrayDeque<>();
    int n = arr.length;
    for (int i = 0; i < n; i++)
    {

      // If recently added element is smaller than
      // current element
      while (!q.isEmpty() && q.peekLast() < arr[i]) {
        q.pollLast();
      }
      q.offerLast(arr[i]);
    }
    return q;
  }

  public static void main(String[] args)
  {
    int[] arr = { 6, 5, 4, 3, 2, 1 };

    // Function call
    Deque<Integer> q = decreasing_monotonic_queue(arr);
    for (int i : q) {
      System.out.print(i + " ");
    }
  }
}

// This code is contributed by sankar.

Python

from collections import deque

# Function to calculate Decreasing
# Monotonic queue
def decreasing_monotonic_queue(arr):
    n = len(arr)
    q = deque()
    for i in range(n):

        # If recently added element is
        # smaller than current element
        while q and q[-1] < arr[i]:

            q.pop()

        q.append(arr[i])

    return q

# Driver Code
arr = [6, 5, 4, 3, 2, 1]

# Function call
q = decreasing_monotonic_queue(arr)

for i in q:
    print(i)

Output

6 5 4 3 2 1 

Applications of monotonic queue include:

• Finding the maximum or minimum element in a sliding window
• Solving dynamic programming problems such as LIS (longest increasing subsequence) and LDS (longest decreasing subsequence)

Advantages of the monotonic queue:

• It is efficient in terms of both time and space complexity.
• It is easy to implement.

Disadvantages of the monotonic queue:

• It is not suitable for all types of problems, only those that involve finding the maximum or minimum element in a specific order
• It has limited functionality compared to more advanced data structures such as segment trees and Fenwick trees.