Count maximum non-overlapping subarrays with given sum
Last Updated :
24 May, 2021
Given an array arr[] consisting of N integers and an integer target, the task is to find the maximum number of non-empty non-overlapping subarrays such that the sum of array elements in each subarray is equal to the target.
Examples:
Input: arr[] = {2, -1, 4, 3, 6, 4, 5, 1}, target = 6
Output: 3
Explanation:
Subarrays {-1, 4, 3}, {6} and {5, 1} have sum equal to target(= 6).
Input: arr[] = {2, 2, 2, 2, 2}, target = 4
Output: 2
Approach: To obtain the smallest non-overlapping subarrays with the sum target, the target is to use the Prefix Sum technique. Follow the steps below to solve the problem:
- Store all the sums calculated so far in a Map mp with key as the sum of the prefix till that index and value as the ending index of the subarray with that sum.
- If the prefix-sum till index i, say sum, is equal to target, check if sum - target exists in the Map or not.
- If sum - target exists in Map and mp[sum - target] = idx, it means that the subarray from [idx + 1, i] has sum equal to target.
- Now for non-overlapping subarrays, maintain an additional variable availIdx(initially set to -1), and take the subarray from [idx + 1, i] only when mp[sum - target] ? availIdx.
- Whenever such a subarray is found, increment the answer and change the value of availIdx to the current index.
- Also, for non-overlapping subarrays, it is always beneficial to greedily take subarrays as small as possible. So, for every prefix-sum found, update its index in the Map, even if it already exists.
- Print the value of count after completing the above steps.
Below is the implementation of the above approach:
C++
// C++ program for the above approach
#include <bits/stdc++.h>
using namespace std;
// Function to count maximum number
// of non-overlapping subarrays with
// sum equals to the target
int maximumSubarrays(int arr[], int N,
int target)
{
// Stores the final count
int ans = 0;
// Next subarray should start
// from index >= availIdx
int availIdx = -1;
// Tracks the prefix sum
int cur_sum = 0;
// Map to store the prefix sum
// for respective indices
unordered_map<int, int> mp;
mp[0] = -1;
for (int i = 0; i < N; i++) {
cur_sum += arr[i];
// Check if cur_sum - target is
// present in the array or not
if (mp.find(cur_sum - target)
!= mp.end()
&& mp[cur_sum - target]
>= availIdx) {
ans++;
availIdx = i;
}
// Update the index of
// current prefix sum
mp[cur_sum] = i;
}
// Return the count of subarrays
return ans;
}
// Driver Code
int main()
{
// Given array arr[]
int arr[] = { 2, -1, 4, 3,
6, 4, 5, 1 };
int N = sizeof(arr) / sizeof(arr[0]);
// Given sum target
int target = 6;
// Function Call
cout << maximumSubarrays(arr, N,
target);
return 0;
}
Java
// Java program for the above approach
import java.util.*;
class GFG{
// Function to count maximum number
// of non-overlapping subarrays with
// sum equals to the target
static int maximumSubarrays(int arr[], int N,
int target)
{
// Stores the final count
int ans = 0;
// Next subarray should start
// from index >= availIdx
int availIdx = -1;
// Tracks the prefix sum
int cur_sum = 0;
// Map to store the prefix sum
// for respective indices
HashMap<Integer,
Integer> mp = new HashMap<Integer,
Integer>();
mp.put(0, 1);
for(int i = 0; i < N; i++)
{
cur_sum += arr[i];
// Check if cur_sum - target is
// present in the array or not
if (mp.containsKey(cur_sum - target) &&
mp.get(cur_sum - target) >= availIdx)
{
ans++;
availIdx = i;
}
// Update the index of
// current prefix sum
mp.put(cur_sum, i);
}
// Return the count of subarrays
return ans;
}
// Driver Code
public static void main(String[] args)
{
// Given array arr[]
int arr[] = { 2, -1, 4, 3,
6, 4, 5, 1 };
int N = arr.length;
// Given sum target
int target = 6;
// Function call
System.out.print(maximumSubarrays(arr, N,
target));
}
}
// This code is contributed by Amit Katiyar
Python3
# Python3 program for the above approach
# Function to count maximum number
# of non-overlapping subarrays with
# sum equals to the target
def maximumSubarrays(arr, N, target):
# Stores the final count
ans = 0
# Next subarray should start
# from index >= availIdx
availIdx = -1
# Tracks the prefix sum
cur_sum = 0
# Map to store the prefix sum
# for respective indices
mp = {}
mp[0] = -1
for i in range(N):
cur_sum += arr[i]
# Check if cur_sum - target is
# present in the array or not
if ((cur_sum - target) in mp and
mp[cur_sum - target] >= availIdx):
ans += 1
availIdx = i
# Update the index of
# current prefix sum
mp[cur_sum] = i
# Return the count of subarrays
return ans
# Driver Code
if __name__ == '__main__':
# Given array arr[]
arr = [ 2, -1, 4, 3,
6, 4, 5, 1 ]
N = len(arr)
# Given sum target
target = 6
# Function call
print(maximumSubarrays(arr, N, target))
# This code is contributed by mohit kumar 29
C#
// C# program for the above approach
using System;
using System.Collections.Generic;
class GFG{
// Function to count maximum number
// of non-overlapping subarrays with
// sum equals to the target
static int maximumSubarrays(int []arr, int N,
int target)
{
// Stores the readonly count
int ans = 0;
// Next subarray should start
// from index >= availIdx
int availIdx = -1;
// Tracks the prefix sum
int cur_sum = 0;
// Map to store the prefix sum
// for respective indices
Dictionary<int,
int> mp = new Dictionary<int,
int>();
mp.Add(0, 1);
for(int i = 0; i < N; i++)
{
cur_sum += arr[i];
// Check if cur_sum - target is
// present in the array or not
if (mp.ContainsKey(cur_sum - target) &&
mp[cur_sum - target] >= availIdx)
{
ans++;
availIdx = i;
}
// Update the index of
// current prefix sum
if(mp.ContainsKey(cur_sum))
mp[cur_sum] = i;
else
mp.Add(cur_sum, i);
}
// Return the count of subarrays
return ans;
}
// Driver Code
public static void Main(String[] args)
{
// Given array []arr
int []arr = {2, -1, 4, 3,
6, 4, 5, 1};
int N = arr.Length;
// Given sum target
int target = 6;
// Function call
Console.Write(maximumSubarrays(arr, N,
target));
}
}
// This code is contributed by Princi Singh
JavaScript
<script>
// JavaScript program for the above approach
// Function to count maximum number
// of non-overlapping subarrays with
// sum equals to the target
function maximumSubarrays(arr, N, target)
{
// Stores the final count
var ans = 0;
// Next subarray should start
// from index >= availIdx
var availIdx = -1;
// Tracks the prefix sum
var cur_sum = 0;
// Map to store the prefix sum
// for respective indices
var mp = new Map();
mp.set(0, 1);
for (var i = 0; i < N; i++) {
cur_sum += arr[i];
// Check if cur_sum - target is
// present in the array or not
if (mp.has(cur_sum - target)
&& mp.get(cur_sum - target)
>= availIdx) {
ans++;
availIdx = i;
}
// Update the index of
// current prefix sum
mp.set(cur_sum , i);
}
// Return the count of subarrays
return ans;
}
// Driver Code
// Given array arr[]
var arr = [2, -1, 4, 3,
6, 4, 5, 1];
var N = arr.length;
// Given sum target
var target = 6;
// Function Call
document.write( maximumSubarrays(arr, N,
target));
</script>
Time Complexity: O(N)
Auxiliary Space: O(N)
Similar Reads
DSA Tutorial - Learn Data Structures and Algorithms DSA (Data Structures and Algorithms) is the study of organizing data efficiently using data structures like arrays, stacks, and trees, paired with step-by-step procedures (or algorithms) to solve problems effectively. Data structures manage how data is stored and accessed, while algorithms focus on
7 min read
Quick Sort QuickSort is a sorting algorithm based on the Divide and Conquer that picks an element as a pivot and partitions the given array around the picked pivot by placing the pivot in its correct position in the sorted array. It works on the principle of divide and conquer, breaking down the problem into s
12 min read
Merge Sort - Data Structure and Algorithms Tutorials Merge sort is a popular sorting algorithm known for its efficiency and stability. It follows the divide-and-conquer approach. It works by recursively dividing the input array into two halves, recursively sorting the two halves and finally merging them back together to obtain the sorted array. Merge
14 min read
Data Structures Tutorial Data structures are the fundamental building blocks of computer programming. They define how data is organized, stored, and manipulated within a program. Understanding data structures is very important for developing efficient and effective algorithms. What is Data Structure?A data structure is a st
2 min read
Bubble Sort Algorithm Bubble Sort is the simplest sorting algorithm that works by repeatedly swapping the adjacent elements if they are in the wrong order. This algorithm is not suitable for large data sets as its average and worst-case time complexity are quite high.We sort the array using multiple passes. After the fir
8 min read
Breadth First Search or BFS for a Graph Given a undirected graph represented by an adjacency list adj, where each adj[i] represents the list of vertices connected to vertex i. Perform a Breadth First Search (BFS) traversal starting from vertex 0, visiting vertices from left to right according to the adjacency list, and return a list conta
15+ min read
Binary Search Algorithm - Iterative and Recursive Implementation Binary Search Algorithm is a searching algorithm used in a sorted array by repeatedly dividing the search interval in half. The idea of binary search is to use the information that the array is sorted and reduce the time complexity to O(log N). Binary Search AlgorithmConditions to apply Binary Searc
15 min read
Insertion Sort Algorithm Insertion sort is a simple sorting algorithm that works by iteratively inserting each element of an unsorted list into its correct position in a sorted portion of the list. It is like sorting playing cards in your hands. You split the cards into two groups: the sorted cards and the unsorted cards. T
9 min read
Array Data Structure Guide In this article, we introduce array, implementation in different popular languages, its basic operations and commonly seen problems / interview questions. An array stores items (in case of C/C++ and Java Primitive Arrays) or their references (in case of Python, JS, Java Non-Primitive) at contiguous
4 min read
Sorting Algorithms A Sorting Algorithm is used to rearrange a given array or list of elements in an order. For example, a given array [10, 20, 5, 2] becomes [2, 5, 10, 20] after sorting in increasing order and becomes [20, 10, 5, 2] after sorting in decreasing order. There exist different sorting algorithms for differ
3 min read