11 Triplet Sum in Array

Triplet Sum in Array | Practice | GeeksforGeeks

Question

Given an array arr of size n and an integer, X. Find if there's a triplet in the array which sums up to the given integer X.

Example 1:

Input:
n = 6, X = 13
arr[] = [1 4 45 6 10 8]
Output:
1
Explanation:
The triplet {1, 4, 8} in 
the array sums up to 13.

Example 2:

Input:
n = 5, X = 10
arr[] = [1 2 4 3 6]
Output:
1
Explanation:
The triplet {1, 3, 6} in 
the array sums up to 10.

Your Task: You don't need to read input or print anything. Your task is to complete the function find3Numbers() which takes the array arr[], the size of the array (n) and the sum (X) as inputs and returns True if there exists a triplet in the array arr[] which sums up to X and False otherwise.

Complexities

Expected Time Complexity: $O(n^2)$ Expected Auxiliary Space: $O(1)$

Constraints

• $1 ≤ n ≤ 10^3$

• $1 ≤ A[i] ≤ 10^5$

Naive Approach

Logic

1. Use three loops to find the triple

2. Do not repeat elements

   1. jth loop runs from [i + 1, n) and so on.

Code

bool find3Numbers(int arr[], int n, int X)
{
    for(int i = 0; i < n; i++)
        for(int j = i + 1; j < n; j++)
            for(int k = j + 1; k < n; k++)
                if(arr[i] + arr[j] + arr[k] == X)
                    return true;
                    
    return false;
}

Complexity

Time Comlexity: $O(n^3)$

Space Complexity: $O(1)$

Using `map`

Logic

1. This problem is a simple two-sum extension.

2. a + b + c = x

3. b + c = x - a

4. The quesion reduces to finding the two-sum from the array for X - a

5. The only catch is that elements up to the index of a should not be used.

Code

bool twoSum(const int arr[], const int n, const int X, int offset) {
    unordered_set<int> visited;
    for(int i = offset; i < n; i++)
        if(visited.count(X - arr[i]))
            return true;
        else 
            visited.insert(arr[i]);
            
    return false;
}

bool find3Numbers(int arr[], int n, int X) {
    for(int i = 0; i < n; i++)
        if(twoSum(arr, n, X - arr[i], i + 1))
            return true;

    return false;
}

Complexity

Time Complexity: $O(n^2)$

Space Complexity: $O(n)$