Count of subsets with sum equal to X | Set-2

Given an array arr[] of length N and an integer X, the task is to find the number of subsets with a sum equal to X.

Examples:

Input: arr[] = {1, 2, 3, 3}, X = 6 
Output: 3 
Explanation: All the possible subsets are {1, 2, 3}, {1, 2, 3} and {3, 3}.

Input: arr[] = {1, 1, 1, 1}, X = 1 
Output: 4 

Space Efficient Approach: This problem has already been discussed in the article here. This article focuses on a similar Dynamic Programming approach which uses only O(X) space. The standard DP relation of solving this problem as discussed in the above article is:

dp[i][C] = dp[i – 1][C – arr[i]] + dp[i – 1][C] 

where dp[i][C] stores the number of subsets of the subarray arr[0… i] such that their sum is equal to C. It can be noted that the dp[i]^th state only requires the array values of the dp[i – 1]^th state. Hence the above relation can be simplified into the following:

dp[C] = dp[C – arr[i]] + dp[C]

Here, a good point to note is that during the calculation of dp[C], the variable C must be iterated in decreasing order in order to avoid the duplicity of arr[i] in the subset-sum count.

Below is the implementation of the above approach:

C++

// C++ implementation of the above approach
#include <bits/stdc++.h>
using namespace std;

// Function to find the count of subsets
// having the given sum
int subsetSum(int arr[], int n, int sum)
{
    // Initializing the dp-table
    int dp[sum + 1] = {};

    // Case for sum of elements in empty set
    dp[0] = 1;

    // Loop to iterate over array elements
    for (int i = 0; i < n; i++) {
        for (int j = sum; j >= 0; j--) {

            // If j-arr[i] is a valid index
            if (j - arr[i] >= 0) {
                dp[j] = dp[j - arr[i]] + dp[j];
            }
        }
    }

    // Return answer
    return dp[sum];
}

// Driven Code
int main()
{
    int arr[] = { 1, 1, 1, 1 };
    int N = sizeof(arr) / sizeof(arr[0]);
    int sum = 1;

    cout << subsetSum(arr, N, sum) << endl;

    return 0;
}

Java

// Java implementation of the above approach
import java.util.*;
public class GFG
{

// Function to find the count of subsets
// having the given sum
static int subsetSum(int arr[], int n, int sum)
{
    // Initializing the dp-table
    int dp[] = new int[sum + 1];

    // Case for sum of elements in empty set
    dp[0] = 1;

    // Loop to iterate over array elements
    for (int i = 0; i < n; i++) {
        for (int j = sum; j >= 0; j--) {

            // If j-arr[i] is a valid index
            if (j - arr[i] >= 0) {
                dp[j] = dp[j - arr[i]] + dp[j];
            }
        }
    }

    // Return answer
    return dp[sum];
}

// Driver code
public static void main(String args[])
{
    int arr[] = { 1, 1, 1, 1 };
    int N = arr.length;
    int sum = 1;

    System.out.println(subsetSum(arr, N, sum));
}
}

// This code is contributed by Samim Hossain Mondal.

Python3

# Python implementation of the above approach

# Function to find the count of subsets
# having the given sum
def subsetSum(arr, n, sum):

    # Initializing the dp-table
    dp = [0] * (sum + 1)

    # Case for sum of elements in empty set
    dp[0] = 1;

    # Loop to iterate over array elements
    for i in range(n):
        for j in range(sum, 0, -1):

            # If j-arr[i] is a valid index
            if (j - arr[i] >= 0):
                dp[j] = dp[j - arr[i]] + dp[j];
    # Return answer
    return dp[sum];

# Driven Code
arr = [1, 1, 1, 1];
N = len(arr)
sum = 1;

print(subsetSum(arr, N, sum))

# This code is contributed by gfgking.

C#

// C# implementation of the above approach
using System;

public class GFG
{

// Function to find the count of subsets
// having the given sum
static int subsetSum(int []arr, int n, int sum)
{

    // Initializing the dp-table
    int []dp = new int[sum + 1];

    // Case for sum of elements in empty set
    dp[0] = 1;

    // Loop to iterate over array elements
    for (int i = 0; i < n; i++) {
        for (int j = sum; j >= 0; j--) {

            // If j-arr[i] is a valid index
            if (j - arr[i] >= 0) {
                dp[j] = dp[j - arr[i]] + dp[j];
            }
        }
    }

    // Return answer
    return dp[sum];
}

// Driver code
public static void Main(String []args)
{
    int []arr = { 1, 1, 1, 1 };
    int N = arr.Length;
    int sum = 1;

    Console.WriteLine(subsetSum(arr, N, sum));
}
}

// This code is contributed by shikhasingrajput

Javascript

<script>
// Javascript implementation of the above approach

// Function to find the count of subsets
// having the given sum
function subsetSum(arr, n, sum)
{

    // Initializing the dp-table
    let dp = new Array(sum + 1).fill(0)

    // Case for sum of elements in empty set
    dp[0] = 1;

    // Loop to iterate over array elements
    for (let i = 0; i < n; i++) {
        for (let j = sum; j >= 0; j--) {

            // If j-arr[i] is a valid index
            if (j - arr[i] >= 0) {
                dp[j] = dp[j - arr[i]] + dp[j];
            }
        }
    }

    // Return answer
    return dp[sum];
}

// Driven Code
let arr = [1, 1, 1, 1];
let N = arr.length;
let sum = 1;

document.write(subsetSum(arr, N, sum))

// This code is contributed by gfgking.
</script>

Output

4

Time Complexity: O(N * X)
Auxiliary Space: O(X)

Another Approach (Memoised dynamic programming):

The problem can be solved using memoised dynamic programming. We can create a 2D dp array where dp[i][j] represents the number of subsets of arr[0…i] with sum equal to j. The recursive relation for dp[i][j] can be defined as follows:

•    If i = 0, dp[0][j] = 1 if arr[0] == j else 0
•    If i > 0, dp[i][j] = dp[i-1][j] + dp[i-1][j-arr[i]], if j >= arr[i]
•    If i > 0, dp[i][j] = dp[i-1][j], if j < arr[i]

The base case for i = 0 can be easily computed as the subset containing only the first element of the array has a sum equal to arr[0] and no other sum. For all other i > 0, we need to consider two cases: either the ith element is included in a subset with sum j, or it is not included. If it is not included, we need to find the number of subsets of arr[0…(i-1)] with sum equal to j. If it is included, we need to find the number of subsets of arr[0…(i-1)] with sum equal to (j – arr[i]).

The final answer will be stored in dp[N-1][X], as we need to find the number of subsets of arr[0…N-1] with sum equal to X.

Algorithm:

1.     Define a recursive function countSubsets(arr, X, dp, i, sum) that takes the following parameters:
   
   • arr: The input array
   • X: The target sum
   • dp: The memoization table
   • i: The index of the current element being considered
   • sum: The current sum of elements in the subset
2.    If i is less than 0, return 1 if sum is equal to X, otherwise return 0
3.    If dp[i][sum] is not equal to -1, return dp[i][sum]
4.    Initialize ans to countSubsets(arr, X, dp, i – 1, sum)
5.    If sum plus the ith element of arr is less than or equal to X, add countSubsets(arr, X, dp, i – 1, sum + arr[i]) to ans
6.    Set dp[i][sum] to ans
7.    Return ans
8.    Initialize the DP table dp to all -1 values
9.    Call countSubsets(arr, X, dp, arr.size() – 1, 0) and store the result in ans
10.    Print ans as the number of subsets with a sum equal to X in arr

Below is the implementation of the approach:

C++

#include <bits/stdc++.h>
using namespace std;

// Recursive function to count subsets with a sum equal to X
int countSubsets(vector<int>& arr, int X, vector<vector<int>> &dp, int i, int sum) {
    // Base case: if we have reached the end of the array
    if (i < 0) {
        // If the sum is equal to X, return 1, else return 0
        return (sum == X ? 1 : 0);
    }

    // If the subproblem has already been solved, return the solution
    if (dp[i][sum] != -1) {
        return dp[i][sum];
    }

    // If we don't include the current element in the subset
    int ans = countSubsets(arr, X, dp, i - 1, sum);

    // If we include the current element in the subset
    if (sum + arr[i] <= X) {
        ans += countSubsets(arr, X, dp, i - 1, sum + arr[i]);
    }

    // Memoize the solution to the subproblem
    dp[i][sum] = ans;

    // Return the solution to the current subproblem
    return ans;
}

int main() {
    // Example usage
    vector<int> arr = { 1, 1, 1, 1 };
    int X = 1;

    // Initialize the DP table with -1
      vector<vector<int>> dp(arr.size()+1, vector<int>(X+1, -1));

    // Count the number of subsets with a sum equal to X
    int ans = countSubsets(arr, X, dp, arr.size() - 1, 0);

    // Print the result
    cout << ans << endl;

    return 0;
}

Java

// Java implementation
import java.util.*;

public class Main {
    // Recursive function to count subsets with a sum equal to X
    public static int countSubsets(List<Integer> arr, int X, int[][] dp, int i, int sum) {
        // Base case: if we have reached the end of the array
        if (i < 0) {
            // If the sum is equal to X, return 1, else return 0
            return (sum == X ? 1 : 0);
        }

        // If the subproblem has already been solved, return the solution
        if (dp[i][sum] != -1) {
            return dp[i][sum];
        }

        // If we don't include the current element in the subset
        int ans = countSubsets(arr, X, dp, i - 1, sum);

        // If we include the current element in the subset
        if (sum + arr.get(i) <= X) {
            ans += countSubsets(arr, X, dp, i - 1, sum + arr.get(i));
        }

        // Memoize the solution to the subproblem
        dp[i][sum] = ans;

        // Return the solution to the current subproblem
        return ans;
    }

    public static void main(String[] args) {
        // Example usage
        List<Integer> arr = Arrays.asList(1, 1, 1, 1);
        int X = 1;

        // Initialize the DP table with -1
        int[][] dp = new int[arr.size() + 1][X + 1];
        for (int i = 0; i <= arr.size(); i++) {
            Arrays.fill(dp[i], -1);
        }

        // Count the number of subsets with a sum equal to X
        int ans = countSubsets(arr, X, dp, arr.size() - 1, 0);

        // Print the result
        System.out.println(ans);
    }
}

// This code is contributed by Sakshi

Python3

# Recursive function to count subsets with a sum equal to X
def count_subsets(arr, X, dp, i, sum):
    # Base case: if we have reached the end of the array
    if i < 0:
        # If the sum is equal to X, return 1, else return 0
        return 1 if sum == X else 0

    # If the subproblem has already been solved, return the solution
    if dp[i][sum] != -1:
        return dp[i][sum]

    # If we don't include the current element in the subset
    ans = count_subsets(arr, X, dp, i - 1, sum)

    # If we include the current element in the subset
    if sum + arr[i] <= X:
        ans += count_subsets(arr, X, dp, i - 1, sum + arr[i])

    # Memoize the solution to the subproblem
    dp[i][sum] = ans

    # Return the solution to the current subproblem
    return ans

if __name__ == "__main__":
    # Example usage
    arr = [1, 1, 1, 1]
    X = 1

    # Initialize the DP table with -1
    dp = [[-1 for _ in range(X + 1)] for _ in range(len(arr) + 1)]

    # Count the number of subsets with a sum equal to X
    ans = count_subsets(arr, X, dp, len(arr) - 1, 0)

    # Print the result
    print(ans)

C#

using System;
using System.Collections.Generic;

class Program
{
    // Recursive function to count subsets with a sum equal to X
    static int CountSubsets(List<int> arr, int X, int[,] dp, int i, int sum)
    {
        // Base case: if we have reached the end of the array
        if (i < 0)
        {
            // If the sum is equal to X, return 1, else return 0
            return (sum == X ? 1 : 0);
        }

        // If the subproblem has already been solved, return the solution
        if (dp[i, sum] != -1)
        {
            return dp[i, sum];
        }

        // If we don't include the current element in the subset
        int ans = CountSubsets(arr, X, dp, i - 1, sum);

        // If we include the current element in the subset
        if (sum + arr[i] <= X)
        {
            ans += CountSubsets(arr, X, dp, i - 1, sum + arr[i]);
        }

        // Memoize the solution to the subproblem
        dp[i, sum] = ans;

        // Return the solution to the current subproblem
        return ans;
    }

    static void Main(string[] args)
    {
        // Example usage
        List<int> arr = new List<int> { 1, 1, 1, 1 };
        int X = 1;

        // Initialize the DP table with -1
        int[,] dp = new int[arr.Count + 1, X + 1];
        for (int i = 0; i <= arr.Count; i++)
        {
            for (int j = 0; j <= X; j++)
            {
                dp[i, j] = -1;
            }
        }

        // Count the number of subsets with a sum equal to X
        int ans = CountSubsets(arr, X, dp, arr.Count - 1, 0);

        // Print the result
        Console.WriteLine(ans);
    }
}

Javascript

// Recursive function to count subsets with a sum equal to X
const countSubsets = (arr, X, dp, i, sum) => {

    // Base case: if we have reached the end of the array
    if (i < 0) {
        return (sum === X ? 1 : 0);
    }

    // If the subproblem has already been solved, return the solution
    if (dp[i][sum] !== -1) {
        return dp[i][sum];
    }

    // If we don't include the current element in the subset
    let ans = countSubsets(arr, X, dp, i - 1, sum);

    // If we include the current element in the subset
    if (sum + arr[i] <= X) {
        ans += countSubsets(arr, X, dp, i - 1, sum + arr[i]);
    }

    // Memoize the solution to the subproblem
    dp[i][sum] = ans;

    // Return the solution to the current subproblem
    return ans;
};

// Driver code
const arr = [1, 1, 1, 1];
const X = 1;

const dp = new Array(arr.length + 1).fill().map(() => new Array(X + 1).fill(-1));

const ans = countSubsets(arr, X, dp, arr.length - 1, 0);

console.log(ans);

Output

4

Time Complexity: O(N * X), where N is the size of the array and X is the target sum. This is because each subproblem is solved only once, and there are N * X possible subproblems.

Auxiliary Space: O(N * X), as we are using a 2D array of size (N + 1) * (X + 1) to store the solutions to the subproblems.