Subarray with given sum

In 3 simple steps you can find your personalised career roadmap in Software development for FREE Expand in New Tab Output: YES Explanation: Subarray [7,13,5] sum up to 25 . Input: 1,3,4,8,7,9 Sum = 13 Output: No Explanation: No such subarray is present having sum 13. Naive Approach The naive approach is to check for every subarray for the given sum. • Run a loop for i from [0…n-1] for the subarray starting from the i-th element. • And run a nested loop to check for every length of subarray starting from position i. • Every time check for the sum of the elements from i to j whether it’s equal to the required sum. Naive Approach – Subarray Sum C++ Implementation bool find_Subarray(int arr[], int N, int required) Python Implementation def find_subarray(arr, n, required): for i in range(n): sum = arr[i] j = i + 1 while j required or j == n: break sum = sum + arr[j] j += 1 return false Time complexity: O(N^2), Where N is the size of the array. Space complexity: O(1) Efficient Approach: Sliding window In a sliding window we take an empty window and move the upper bound of the window till our given constraint is satisfied and after that, if the constraints are violated we try to maintain the constraints by increasing or decreasing the size of the window. The efficient approach is similar as we have constrained the sum and we take the window empty initially and if the sum of the window is greater than the required sum then it’s clear that adding any element more will increase th...

Given an unsorted array Aof size Nthat contains onlynon-negative integers, find a continuous sub-array which adds to a given number Sand return the left and right index of that subarray. In case of multiple subarrays, return the subarray indexes which comes first on moving from left to right. Algorithm: The given code finds a continuous subarray in an input array arr of size n that adds up to a given number s. Here is a step-by-step algorithm to explain the code: • Create an empty ArrayList al to store the output subarray indices. • Initialize two pointers i and j to 1 and 0 respectively. • Initialize a variable sum to the first element of the input array arr[0]. • Use a for loop that iterates from i = 1 to i subarraySum(int[] arr, int n, int s) Disclaimer:This problem (Subarray with given Sum)is originally created by

Input: nums[] = 1. Using Sliding Window We can solve this problem by using a positive integers. The algorithm can be implemented as follows in C++, Java, and Python: Output: Sublist found (1, 3) The time complexity of the above solution is O(n) and doesn’t require any extra space, where n is the size of the input. 2. Using Hashing The above solution will fail for negative numbers. We can use Following is the implementation of the above algorithm in C++, Java, and Python:

Table of Contents • • • • • • • • • • The aim of this article is to help you compute the subarray whose sum is given and the subarray is contiguous. For your information, a contiguous array is one whose elements occupy positions that follow each other. In addition, the sequence of elements is also maintained. We will look at two approaches to solve this problem. The first approach is a brute-force one where all the subarrays are considered. In this case, the sum of their elements is computed and checked against the given expected sum. If the two are the same, we have found our solution. If not, then we continue adding the remaining items in the sequence till we reach the end. The second approach involves the use of dynamic programming. In this example, we will engage the use of top-down approach dynamic programming. Dynamic programming is a class or type of algorithm of simply what is referred in technical terms as algorithm paradigm. This is just one of the paradigm’s alongside others like the divide & conquer, brute-force, greedy algorithms. Dynamic programming solves a different set of problems. It is important to be able to identify problems that can be solved using dynamic programming and how to arrive at these solutions. Identifying problems that can be solved using dynamic programming. Problems that can be solved using dynamic programming exhibit two main properties 1) Optimal Substructure 2) Overlapping Subproblems When hearing about these two terms for the first t...

Problem Statement: Subarray with Given Sum Given an array and a sum k, generate the subarray whose elements sum to k. Examples: Example 1: Input: arr = , k = 10 Output: 2 1 3 4 Explanation: Of all the subarrays, 2, 1, 3 and 4 sums to 10 Solution: Disclaimer: Don’t jump directly to the solution, try it out yourself first. Solution 1: Intuition: There is no special intuition needed for the brute force solution. We just need to generate all possible subarrays and check the sum. Approach: • In order to generate all the subarrays, we need two nested loops. First loop decides the starting index of the subarray. • Second loop traverses from the starting index and generates all possible subarrays from that. • At each point we check whether we have reached the required sum. • If so, i and j are the starting and ending indexes of the subarray, we print using them. • Else, we move on to the next possible one. Code: #include using namespace std; void subArrWithSumKBruteForce(int arr[], int n, int k) Output: Subarray with given sum is: 1 9 3 7 Time Complexity: O(n^2 + n), because we need to generate all possible subarrays. This contributes O(n^2) and printing the subarray takes O(n). As a result, O(n^2 + n) ~ O(n^2). Space Complexity: O(1), we are not using any extra space. Java Code import java.util.*; public class Solution Output: Subarray with given sum is: 1 9 3 7 Time Complexity: O(n^2 + n), because we need to generate all possible subarrays. This contributes O(n^2) and printing...

Given an unsorted array Aof size Nthat contains onlynon-negative integers, find a continuous sub-array which adds to a given number Sand return the left and right index of that subarray. In case of multiple subarrays, return the subarray indexes which comes first on moving from left to right. Algorithm: The given code finds a continuous subarray in an input array arr of size n that adds up to a given number s. Here is a step-by-step algorithm to explain the code: • Create an empty ArrayList al to store the output subarray indices. • Initialize two pointers i and j to 1 and 0 respectively. • Initialize a variable sum to the first element of the input array arr[0]. • Use a for loop that iterates from i = 1 to i subarraySum(int[] arr, int n, int s) Disclaimer:This problem (Subarray with given Sum)is originally created by

Problem Statement: Subarray with Given Sum Given an array and a sum k, generate the subarray whose elements sum to k. Examples: Example 1: Input: arr = , k = 10 Output: 2 1 3 4 Explanation: Of all the subarrays, 2, 1, 3 and 4 sums to 10 Solution: Disclaimer: Don’t jump directly to the solution, try it out yourself first. Solution 1: Intuition: There is no special intuition needed for the brute force solution. We just need to generate all possible subarrays and check the sum. Approach: • In order to generate all the subarrays, we need two nested loops. First loop decides the starting index of the subarray. • Second loop traverses from the starting index and generates all possible subarrays from that. • At each point we check whether we have reached the required sum. • If so, i and j are the starting and ending indexes of the subarray, we print using them. • Else, we move on to the next possible one. Code: #include using namespace std; void subArrWithSumKBruteForce(int arr[], int n, int k) Output: Subarray with given sum is: 1 9 3 7 Time Complexity: O(n^2 + n), because we need to generate all possible subarrays. This contributes O(n^2) and printing the subarray takes O(n). As a result, O(n^2 + n) ~ O(n^2). Space Complexity: O(1), we are not using any extra space. Java Code import java.util.*; public class Solution Output: Subarray with given sum is: 1 9 3 7 Time Complexity: O(n^2 + n), because we need to generate all possible subarrays. This contributes O(n^2) and printing...

In 3 simple steps you can find your personalised career roadmap in Software development for FREE Expand in New Tab Output: YES Explanation: Subarray [7,13,5] sum up to 25 . Input: 1,3,4,8,7,9 Sum = 13 Output: No Explanation: No such subarray is present having sum 13. Naive Approach The naive approach is to check for every subarray for the given sum. • Run a loop for i from [0…n-1] for the subarray starting from the i-th element. • And run a nested loop to check for every length of subarray starting from position i. • Every time check for the sum of the elements from i to j whether it’s equal to the required sum. Naive Approach – Subarray Sum C++ Implementation bool find_Subarray(int arr[], int N, int required) Python Implementation def find_subarray(arr, n, required): for i in range(n): sum = arr[i] j = i + 1 while j required or j == n: break sum = sum + arr[j] j += 1 return false Time complexity: O(N^2), Where N is the size of the array. Space complexity: O(1) Efficient Approach: Sliding window In a sliding window we take an empty window and move the upper bound of the window till our given constraint is satisfied and after that, if the constraints are violated we try to maintain the constraints by increasing or decreasing the size of the window. The efficient approach is similar as we have constrained the sum and we take the window empty initially and if the sum of the window is greater than the required sum then it’s clear that adding any element more will increase th...

Input: nums[] = 1. Using Sliding Window We can solve this problem by using a positive integers. The algorithm can be implemented as follows in C++, Java, and Python: Output: Sublist found (1, 3) The time complexity of the above solution is O(n) and doesn’t require any extra space, where n is the size of the input. 2. Using Hashing The above solution will fail for negative numbers. We can use Following is the implementation of the above algorithm in C++, Java, and Python:

Table of Contents • • • • • • • • • • The aim of this article is to help you compute the subarray whose sum is given and the subarray is contiguous. For your information, a contiguous array is one whose elements occupy positions that follow each other. In addition, the sequence of elements is also maintained. We will look at two approaches to solve this problem. The first approach is a brute-force one where all the subarrays are considered. In this case, the sum of their elements is computed and checked against the given expected sum. If the two are the same, we have found our solution. If not, then we continue adding the remaining items in the sequence till we reach the end. The second approach involves the use of dynamic programming. In this example, we will engage the use of top-down approach dynamic programming. Dynamic programming is a class or type of algorithm of simply what is referred in technical terms as algorithm paradigm. This is just one of the paradigm’s alongside others like the divide & conquer, brute-force, greedy algorithms. Dynamic programming solves a different set of problems. It is important to be able to identify problems that can be solved using dynamic programming and how to arrive at these solutions. Identifying problems that can be solved using dynamic programming. Problems that can be solved using dynamic programming exhibit two main properties 1) Optimal Substructure 2) Overlapping Subproblems When hearing about these two terms for the first t...