Find the Sub-array with sum closest to 0
Last Updated :
15 Sep, 2023
Given an array of both positive and negative numbers, the task is to find out the subarray whose sum is closest to 0.
There can be multiple such subarrays, we need to output just 1 of them.
Examples:
Input : arr[] = {-1, 3, 2, -5, 4}
Output : 1, 3
Subarray from index 1 to 3 has sum closest to 0 i.e.
3 + 2 + -5 = 0
Input : {2, -5, 4, -6, 3}
Output : 0, 2
2 + -5 + 4 = 1 closest to 0
Asked in : Microsoft
A Naive approach is to consider all subarrays one by one and update indexes of subarray with sum closest to 0.
Implementation:
C++
// C++ program to find subarray with
// sum closest to 0
#include <bits/stdc++.h>
using namespace std;
// Function to find the subarray
pair<int, int> findSubArray(int arr[], int n)
{
int start, end, min_sum = INT_MAX;
// Pick a starting point
for (int i = 0; i < n; i++) {
// Consider current starting point
// as a subarray and update minimum
// sum and subarray indexes
int curr_sum = arr[i];
if (min_sum > abs(curr_sum)) {
min_sum = abs(curr_sum);
start = i;
end = i;
}
// Try all subarrays starting with i
for (int j = i + 1; j < n; j++) {
curr_sum = curr_sum + arr[j];
// update minimum sum
// and subarray indexes
if (min_sum > abs(curr_sum)) {
min_sum = abs(curr_sum);
start = i;
end = j;
}
}
}
// Return starting and ending indexes
pair<int, int> p = make_pair(start, end);
return p;
}
// Drivers code
int main()
{
int arr[] = { 2, -5, 4, -6, -3 };
int n = sizeof(arr) / sizeof(arr[0]);
pair<int, int> point = findSubArray(arr, n);
cout << "Subarray starting from ";
cout << point.first << " to " << point.second;
return 0;
}
Java
// Java program to find subarray with
// sum closest to 0
class GFG
{
static class Pair
{
int first, second;
public Pair(int first, int second)
{
this.first = first;
this.second = second;
}
}
// Function to find the subarray
static Pair findSubArray(int arr[], int n)
{
int start = 0, end = 0, min_sum = Integer.MAX_VALUE;
// Pick a starting point
for (int i = 0; i < n; i++)
{
// Consider current starting point
// as a subarray and update minimum
// sum and subarray indexes
int curr_sum = arr[i];
if (min_sum > Math.abs(curr_sum))
{
min_sum = Math.abs(curr_sum);
start = i;
end = i;
}
// Try all subarrays starting with i
for (int j = i + 1; j < n; j++)
{
curr_sum = curr_sum + arr[j];
// update minimum sum
// and subarray indexes
if (min_sum > Math.abs(curr_sum))
{
min_sum = Math.abs(curr_sum);
start = i;
end = j;
}
}
}
// Return starting and ending indexes
Pair p = new Pair(start, end);
return p;
}
// Drivers code
public static void main(String[] args)
{
int arr[] = {2, -5, 4, -6, -3};
int n = arr.length;
Pair point = findSubArray(arr, n);
System.out.println("Subarray starting from "
+ point.first + " to " + point.second);
}
}
// This code has been contributed by 29AjayKumar
Python3
# Python 3 program to find subarray with
# sum closest to 0
import sys
# Function to find the subarray
def findSubArray(arr, n):
min_sum = sys.maxsize
# Pick a starting point
for i in range(n):
# Consider current starting point
# as a subarray and update minimum
# sum and subarray indexes
curr_sum = arr[i]
if (min_sum > abs(curr_sum)):
min_sum = abs(curr_sum)
start = i
end = i
# Try all subarrays starting with i
for j in range(i + 1, n, 1):
curr_sum = curr_sum + arr[j]
# update minimum sum
# and subarray indexes
if (min_sum > abs(curr_sum)):
min_sum = abs(curr_sum)
start = i
end = j
# Return starting and ending indexes
p = [start, end]
return p
# Driver Code
if __name__ == '__main__':
arr = [2, -5, 4, -6, -3]
n = len(arr)
point = findSubArray(arr, n)
print("Subarray starting from ", end = "")
print(point[0], "to", point[1])
# This code is contributed by
# Surendra_Gangwar
C#
// C# program to find subarray with
// sum closest to 0
using System;
class GFG
{
public class Pair
{
public int first, second;
public Pair(int first, int second)
{
this.first = first;
this.second = second;
}
}
// Function to find the subarray
static Pair findSubArray(int []arr, int n)
{
int start = 0, end = 0, min_sum = int.MaxValue;
// Pick a starting point
for (int i = 0; i < n; i++)
{
// Consider current starting point
// as a subarray and update minimum
// sum and subarray indexes
int curr_sum = arr[i];
if (min_sum > Math.Abs(curr_sum))
{
min_sum = Math.Abs(curr_sum);
start = i;
end = i;
}
// Try all subarrays starting with i
for (int j = i + 1; j < n; j++)
{
curr_sum = curr_sum + arr[j];
// update minimum sum
// and subarray indexes
if (min_sum > Math.Abs(curr_sum))
{
min_sum = Math.Abs(curr_sum);
start = i;
end = j;
}
}
}
// Return starting and ending indexes
Pair p = new Pair(start, end);
return p;
}
// Drivers code
public static void Main(String[] args)
{
int []arr = {2, -5, 4, -6, -3};
int n = arr.Length;
Pair point = findSubArray(arr, n);
Console.WriteLine("Subarray starting from "
+ point.first + " to " + point.second);
}
}
// This code is contributed by Princi Singh
JavaScript
<script>
// JavaScript program to find subarray with
// sum closest to 0
// Function to find the subarray
function findSubArray(arr, n) {
let start, end, min_sum = Number.MAX_SAFE_INTEGER;
// Pick a starting point
for (let i = 0; i < n; i++) {
// Consider current starting point
// as a subarray and update minimum
// sum and subarray indexes
let curr_sum = arr[i];
if (min_sum > Math.abs(curr_sum)) {
min_sum = Math.abs(curr_sum);
start = i;
end = i;
}
// Try all subarrays starting with i
for (let j = i + 1; j < n; j++) {
curr_sum = curr_sum + arr[j];
// update minimum sum
// and subarray indexes
if (min_sum > Math.abs(curr_sum)) {
min_sum = Math.abs(curr_sum);
start = i;
end = j;
}
}
}
// Return starting and ending indexes
let p = [start, end];
return p;
}
// Drivers code
let arr = [2, -5, 4, -6, -3];
let n = arr.length;
let point = findSubArray(arr, n);
document.write("Subarray starting from ");
document.write(point[0] + " to " + point[1]);
</script>
OutputSubarray starting from 0 to 2
Time Complexity: O(n2)
Space Complexity: O(1) as no extra space has been used.
An Efficient method is to perform following steps:-
- Maintain a Prefix sum array . Also maintain indexes in the prefix sum array.
- Sort the prefix sum array on the basis of sum.
- Find the two elements in a prefix sum array with minimum difference.
i.e. Find min(pre_sum[i] - pre_sum[i-1])
- Return indexes of pre_sum with minimum difference.
- Subarray with (lower_index+1, upper_index) will have the sum closest to 0.
- Taking lower_index+1 because on subtracting value at lower_index we get the sum closest to 0. That's why lower_index need not to be included.
Implementation:
C++
// C++ program to find subarray with sum
// closest to 0
#include <bits/stdc++.h>
using namespace std;
struct prefix {
int sum;
int index;
};
// Sort on the basis of sum
bool comparison(prefix a, prefix b)
{
return a.sum < b.sum;
}
// Returns subarray with sum closest to 0.
pair<int, int> findSubArray(int arr[], int n)
{
int start, end, min_diff = INT_MAX;
prefix pre_sum[n + 1];
// To consider the case of subarray starting
// from beginning of the array
pre_sum[0].sum = 0;
pre_sum[0].index = -1;
// Store prefix sum with index
for (int i = 1; i <= n; i++) {
pre_sum[i].sum = pre_sum[i-1].sum + arr[i-1];
pre_sum[i].index = i - 1;
}
// Sort on the basis of sum
sort(pre_sum, pre_sum + (n + 1), comparison);
// Find two consecutive elements with minimum difference
for (int i = 1; i <= n; i++) {
int diff = pre_sum[i].sum - pre_sum[i-1].sum;
// Update minimum difference
// and starting and ending indexes
if (min_diff > diff) {
min_diff = diff;
start = pre_sum[i-1].index;
end = pre_sum[i].index;
}
}
// Return starting and ending indexes
pair<int, int> p = make_pair(start + 1, end);
return p;
}
// Drivers code
int main()
{
int arr[] = { 2, 3, -4, -1, 6 };
int n = sizeof(arr) / sizeof(arr[0]);
pair<int, int> point = findSubArray(arr, n);
cout << "Subarray starting from ";
cout << point.first << " to " << point.second;
return 0;
}
Java
// Java program to find subarray with sum
// closest to 0
import java.util.*;
class Prefix
{
int sum, index;
}
class Pair
{
int first, second;
Pair(int a, int b)
{
first = a;
second = b;
}
}
class GFG{
// Returns subarray with sum closest to 0.
static Pair findSubArray(int arr[], int n)
{
int start = -1, end = -1,
min_diff = Integer.MAX_VALUE;
Prefix pre_sum[] = new Prefix[n + 1];
for(int i = 0; i < n + 1; i++)
pre_sum[i] = new Prefix();
// To consider the case of subarray starting
// from beginning of the array
pre_sum[0].sum = 0;
pre_sum[0].index = -1;
// Store prefix sum with index
for(int i = 1; i <= n; i++)
{
pre_sum[i].sum = pre_sum[i - 1].sum +
arr[i - 1];
pre_sum[i].index = i - 1;
}
// Sort on the basis of sum
Arrays.sort(pre_sum, ((a, b) -> a.sum - b.sum));
// Find two consecutive elements with minimum
// difference
for(int i = 1; i <= n; i++)
{
int diff = pre_sum[i].sum -
pre_sum[i - 1].sum;
// Update minimum difference
// and starting and ending indexes
if (min_diff > diff)
{
min_diff = diff;
start = pre_sum[i - 1].index;
end = pre_sum[i].index;
}
}
// Return starting and ending indexes
Pair p = new Pair(start + 1, end);
return p;
}
// Driver code
public static void main(String[] args)
{
int arr[] = { 2, 3, -4, -1, 6 };
int n = arr.length;
Pair point = findSubArray(arr, n);
System.out.print("Subarray starting from ");
System.out.println(point.first + " to " +
point.second);
}
}
// This code is contributed by jrishabh99
Python3
# Python3 program to find subarray
# with sum closest to 0
class prefix:
def __init__(self, sum, index):
self.sum = sum
self.index = index
# Returns subarray with sum closest to 0.
def findSubArray(arr, n):
start, end, min_diff = None, None, float('inf')
pre_sum = [None] * (n + 1)
# To consider the case of subarray
# starting from beginning of the array
pre_sum[0] = prefix(0, -1)
# Store prefix sum with index
for i in range(1, n + 1):
pre_sum[i] = prefix(pre_sum[i - 1].sum +
arr[i - 1], i - 1)
# Sort on the basis of sum
pre_sum.sort(key = lambda x: x.sum)
# Find two consecutive elements
# with minimum difference
for i in range(1, n + 1):
diff = pre_sum[i].sum - pre_sum[i - 1].sum
# Update minimum difference
# and starting and ending indexes
if min_diff > diff:
min_diff = diff
start = pre_sum[i - 1].index
end = pre_sum[i].index
# Return starting and ending indexes
return (start + 1, end)
# Driver code
if __name__ == "__main__":
arr = [2, 3, -4, -1, 6]
n = len(arr)
point = findSubArray(arr, n)
print("Subarray starting from",
point[0], "to", point[1])
# This code is contributed by Rituraj Jain
C#
// C# program to find subarray with sum
// closest to 0
using System;
class Prefix : IComparable<Prefix>
{
public int sum, index;
public int CompareTo(Prefix p)
{
return this.sum-p.sum;
}
}
class Pair
{
public int first, second;
public Pair(int a, int b)
{
first = a;
second = b;
}
}
public class GFG{
// Returns subarray with sum closest to 0.
static Pair findSubArray(int []arr, int n)
{
int start = -1, end = -1,
min_diff = int.MaxValue;
Prefix []pre_sum = new Prefix[n + 1];
for(int i = 0; i < n + 1; i++)
pre_sum[i] = new Prefix();
// To consider the case of subarray starting
// from beginning of the array
pre_sum[0].sum = 0;
pre_sum[0].index = -1;
// Store prefix sum with index
for(int i = 1; i <= n; i++)
{
pre_sum[i].sum = pre_sum[i - 1].sum +
arr[i - 1];
pre_sum[i].index = i - 1;
}
// Sort on the basis of sum
Array.Sort(pre_sum);
// Find two consecutive elements with minimum
// difference
for(int i = 1; i <= n; i++)
{
int diff = pre_sum[i].sum -
pre_sum[i - 1].sum;
// Update minimum difference
// and starting and ending indexes
if (min_diff > diff)
{
min_diff = diff;
start = pre_sum[i - 1].index;
end = pre_sum[i].index;
}
}
// Return starting and ending indexes
Pair p = new Pair(start + 1, end);
return p;
}
// Driver code
public static void Main(String[] args)
{
int []arr = { 2, 3, -4, -1, 6 };
int n = arr.Length;
Pair point = findSubArray(arr, n);
Console.Write("Subarray starting from ");
Console.WriteLine(point.first + " to " +
point.second);
}
}
// This code is contributed by 29AjayKumar
JavaScript
<script>
// Javascript program to find subarray with sum
// closest to 0
class Prefix
{
constructor()
{
this.sum = 0;
this.index = 0;
}
}
class Pair
{
constructor(a, b)
{
this.first = a;
this.second = b;
}
}
// Returns subarray with sum closest to 0.
function findSubArray(arr, n)
{
let start = -1, end = -1,
min_diff = Number.MAX_VALUE;
let pre_sum = new Array(n + 1);
for(let i = 0; i < n + 1; i++)
pre_sum[i] = new Prefix();
// To consider the case of subarray starting
// from beginning of the array
pre_sum[0].sum = 0;
pre_sum[0].index = -1;
// Store prefix sum with index
for(let i = 1; i <= n; i++)
{
pre_sum[i].sum = pre_sum[i - 1].sum +
arr[i - 1];
pre_sum[i].index = i - 1;
}
// Sort on the basis of sum
pre_sum.sort(function(a, b) {return a.sum - b.sum});
// Find two consecutive elements with minimum
// difference
for(let i = 1; i <= n; i++)
{
let diff = pre_sum[i].sum -
pre_sum[i - 1].sum;
// Update minimum difference
// and starting and ending indexes
if (min_diff > diff)
{
min_diff = diff;
start = pre_sum[i - 1].index;
end = pre_sum[i].index;
}
}
// Return starting and ending indexes
let p = new Pair(start + 1, end);
return p;
}
// Driver code
let arr = [2, 3, -4, -1, 6 ];
let n = arr.length;
let point = findSubArray(arr, n);
document.write("Subarray starting from ");
document.write(point.first + " to " +
point.second);
// This code is contributed by rag2127
</script>
OutputSubarray starting from 0 to 3
Time Complexity: O(n log n)
Space Complexity: O(n) as pre_sum array has been created. Here, n is size of input array.
Similar Reads
Basics & Prerequisites
Data Structures
Array Data StructureIn 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
3 min read
String in Data StructureA string is a sequence of characters. The following facts make string an interesting data structure.Small set of elements. Unlike normal array, strings typically have smaller set of items. For example, lowercase English alphabet has only 26 characters. ASCII has only 256 characters.Strings are immut
2 min read
Hashing in Data StructureHashing is a technique used in data structures that efficiently stores and retrieves data in a way that allows for quick access. Hashing involves mapping data to a specific index in a hash table (an array of items) using a hash function. It enables fast retrieval of information based on its key. The
2 min read
Linked List Data StructureA linked list is a fundamental data structure in computer science. It mainly allows efficient insertion and deletion operations compared to arrays. Like arrays, it is also used to implement other data structures like stack, queue and deque. Hereâs the comparison of Linked List vs Arrays Linked List:
2 min read
Stack Data StructureA Stack is a linear data structure that follows a particular order in which the operations are performed. The order may be LIFO(Last In First Out) or FILO(First In Last Out). LIFO implies that the element that is inserted last, comes out first and FILO implies that the element that is inserted first
2 min read
Queue Data StructureA Queue Data Structure is a fundamental concept in computer science used for storing and managing data in a specific order. It follows the principle of "First in, First out" (FIFO), where the first element added to the queue is the first one to be removed. It is used as a buffer in computer systems
2 min read
Tree Data StructureTree Data Structure is a non-linear data structure in which a collection of elements known as nodes are connected to each other via edges such that there exists exactly one path between any two nodes. Types of TreeBinary Tree : Every node has at most two childrenTernary Tree : Every node has at most
4 min read
Graph Data StructureGraph Data Structure is a collection of nodes connected by edges. It's used to represent relationships between different entities. If you are looking for topic-wise list of problems on different topics like DFS, BFS, Topological Sort, Shortest Path, etc., please refer to Graph Algorithms. Basics of
3 min read
Trie Data StructureThe Trie data structure is a tree-like structure used for storing a dynamic set of strings. It allows for efficient retrieval and storage of keys, making it highly effective in handling large datasets. Trie supports operations such as insertion, search, deletion of keys, and prefix searches. In this
15+ min read
Algorithms
Searching AlgorithmsSearching algorithms are essential tools in computer science used to locate specific items within a collection of data. In this tutorial, we are mainly going to focus upon searching in an array. When we search an item in an array, there are two most common algorithms used based on the type of input
2 min read
Sorting AlgorithmsA 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
Introduction to RecursionThe process in which a function calls itself directly or indirectly is called recursion and the corresponding function is called a recursive function. A recursive algorithm takes one step toward solution and then recursively call itself to further move. The algorithm stops once we reach the solution
14 min read
Greedy AlgorithmsGreedy algorithms are a class of algorithms that make locally optimal choices at each step with the hope of finding a global optimum solution. At every step of the algorithm, we make a choice that looks the best at the moment. To make the choice, we sometimes sort the array so that we can always get
3 min read
Graph AlgorithmsGraph is a non-linear data structure like tree data structure. The limitation of tree is, it can only represent hierarchical data. For situations where nodes or vertices are randomly connected with each other other, we use Graph. Example situations where we use graph data structure are, a social net
3 min read
Dynamic Programming or DPDynamic Programming is an algorithmic technique with the following properties.It is mainly an optimization over plain recursion. Wherever we see a recursive solution that has repeated calls for the same inputs, we can optimize it using Dynamic Programming. The idea is to simply store the results of
3 min read
Bitwise AlgorithmsBitwise algorithms in Data Structures and Algorithms (DSA) involve manipulating individual bits of binary representations of numbers to perform operations efficiently. These algorithms utilize bitwise operators like AND, OR, XOR, NOT, Left Shift, and Right Shift.BasicsIntroduction to Bitwise Algorit
4 min read
Advanced
Segment TreeSegment Tree is a data structure that allows efficient querying and updating of intervals or segments of an array. It is particularly useful for problems involving range queries, such as finding the sum, minimum, maximum, or any other operation over a specific range of elements in an array. The tree
3 min read
Pattern SearchingPattern searching algorithms are essential tools in computer science and data processing. These algorithms are designed to efficiently find a particular pattern within a larger set of data. Patten SearchingImportant Pattern Searching Algorithms:Naive String Matching : A Simple Algorithm that works i
2 min read
GeometryGeometry is a branch of mathematics that studies the properties, measurements, and relationships of points, lines, angles, surfaces, and solids. From basic lines and angles to complex structures, it helps us understand the world around us.Geometry for Students and BeginnersThis section covers key br
2 min read
Interview Preparation
Practice Problem