Count of subsequences in an array with sum less than or equal to X
Last Updated :
12 Jul, 2025
Given an integer array arr[] of size N and an integer X, the task is to count the number of subsequences in that array such that its sum is less than or equal to X.
Note: 1 <= N <= 1000 and 1 <= X <= 1000, where N is the size of the array.
Examples:
Input : arr[] = {84, 87, 73}, X = 100
Output : 3
Explanation: The three subsequences with sum less than or equal to 100 are {84}, {87} and {73}.
Input : arr[] = {25, 13, 40}, X = 50
Output : 4
Explanation: The four subsequences with sum less than or equal to 50 are {25}, {13}, {40} and {25, 13}.
Naive Approach: Generate all the subsequences of the array and check if the sum is less than or equal to X.
Time complexity:O(2N)
Efficient Approach: Generate the count of subsequences using Dynamic Programming. In order to solve the problem, follow the steps below:
- For any index ind, if arr[ind] ? X then, the count of subsequences including as well as excluding the element at the current index:
countSubsequence(ind, X) = countSubsequence(ind + 1, X) (excluding) + countSubsequence(ind + 1, X - arr[ind]) (including)
- Else, count subsequences excluding the current index:
countSubsequence(ind, X) = countSubsequence(ind + 1, X) (excluding)
- Finally, subtract 1 from the final count returned by the function as it also counts an empty subsequence.
Below is the implementation of the above approach:
C++
// C++ Program to count number
// of subsequences in an array
// with sum less than or equal to X
#include <bits/stdc++.h>
using namespace std;
// Utility function to return the count
// of subsequence in an array with sum
// less than or equal to X
int countSubsequenceUtil(
int ind, int sum,
int* A, int N,
vector<vector<int> >& dp)
{
// Base condition
if (ind == N)
return 1;
// Return if the sub-problem
// is already calculated
if (dp[ind][sum] != -1)
return dp[ind][sum];
// Check if the current element is
// less than or equal to sum
if (A[ind] <= sum) {
// Count subsequences excluding
// the current element
dp[ind][sum]
= countSubsequenceUtil(
ind + 1,
sum, A, N, dp)
+
// Count subsequences including
// the current element
countSubsequenceUtil(
ind + 1,
sum - A[ind],
A, N, dp);
}
else {
// Exclude current element
dp[ind][sum]
= countSubsequenceUtil(
ind + 1,
sum, A,
N, dp);
}
// Return the result
return dp[ind][sum];
}
// Function to return the count of subsequence
// in an array with sum less than or equal to X
int countSubsequence(int* A, int N, int X)
{
// Initialize a DP array
vector<vector<int> > dp(
N,
vector<int>(X + 1, -1));
// Return the result
return countSubsequenceUtil(0, X, A,
N, dp)
- 1;
}
// Driver Code
int main()
{
int arr[] = { 25, 13, 40 }, X = 50;
int N = sizeof(arr) / sizeof(arr[0]);
cout << countSubsequence(arr, N, X);
return 0;
}
Java
// Java program to count number
// of subsequences in an array
// with sum less than or equal to X
class GFG{
// Utility function to return the count
// of subsequence in an array with sum
// less than or equal to X
static int countSubsequenceUtil(int ind, int sum,
int []A, int N,
int [][]dp)
{
// Base condition
if (ind == N)
return 1;
// Return if the sub-problem
// is already calculated
if (dp[ind][sum] != -1)
return dp[ind][sum];
// Check if the current element is
// less than or equal to sum
if (A[ind] <= sum)
{
// Count subsequences excluding
// the current element
dp[ind][sum] = countSubsequenceUtil(
ind + 1, sum,
A, N, dp) +
// Count subsequences
// including the current
// element
countSubsequenceUtil(
ind + 1,
sum - A[ind],
A, N, dp);
}
else
{
// Exclude current element
dp[ind][sum] = countSubsequenceUtil(
ind + 1, sum,
A, N, dp);
}
// Return the result
return dp[ind][sum];
}
// Function to return the count of subsequence
// in an array with sum less than or equal to X
static int countSubsequence(int[] A, int N, int X)
{
// Initialize a DP array
int [][]dp = new int[N][X + 1];
for(int i = 0; i < N; i++)
{
for(int j = 0; j < X + 1; j++)
{
dp[i][j] = -1;
}
}
// Return the result
return countSubsequenceUtil(0, X, A,
N, dp) - 1;
}
// Driver Code
public static void main(String[] args)
{
int arr[] = { 25, 13, 40 }, X = 50;
int N = arr.length;
System.out.print(countSubsequence(arr, N, X));
}
}
// This code is contributed by Rajput-Ji
Python3
# Python program for the above approach:
## Utility function to return the count
## of subsequence in an array with sum
## less than or equal to X
def countSubsequenceUtil(ind, s, A, N, dp):
## Base condition
if (ind == N):
return 1
## Return if the sub-problem
## is already calculated
if (dp[ind][s] != -1):
return dp[ind][s]
## Check if the current element is
## less than or equal to sum
if (A[ind] <= s):
## Count subsequences excluding
## the current element
## Also, Count subsequences including
## the current element
dp[ind][s] = countSubsequenceUtil(ind + 1, s, A, N, dp) + countSubsequenceUtil(ind + 1, s - A[ind], A, N, dp)
else:
## Exclude current element
dp[ind][s] = countSubsequenceUtil(ind + 1, s, A, N, dp)
## Return the result
return dp[ind][s]
## Function to return the count of subsequence
## in an array with sum less than or equal to X
def countSubsequence(A, N, X):
## Initialize a DP array
dp = [[-1 for _ in range(X + 1)] for i in range(N)]
## Return the result
return countSubsequenceUtil(0, X, A, N, dp) - 1
## Driver code
if __name__=='__main__':
arr = [25, 13, 40]
X = 50
N = len(arr)
print(countSubsequence(arr, N, X))
C#
// C# program to count number
// of subsequences in an array
// with sum less than or equal to X
using System;
class GFG{
// Utility function to return the count
// of subsequence in an array with sum
// less than or equal to X
static int countSubsequenceUtil(int ind, int sum,
int []A, int N,
int [,]dp)
{
// Base condition
if (ind == N)
return 1;
// Return if the sub-problem
// is already calculated
if (dp[ind, sum] != -1)
return dp[ind, sum];
// Check if the current element is
// less than or equal to sum
if (A[ind] <= sum)
{
// Count subsequences excluding
// the current element
dp[ind, sum] = countSubsequenceUtil(
ind + 1, sum,
A, N, dp) +
// Count subsequences
// including the current
// element
countSubsequenceUtil(
ind + 1,
sum - A[ind],
A, N, dp);
}
else
{
// Exclude current element
dp[ind, sum] = countSubsequenceUtil(
ind + 1, sum,
A, N, dp);
}
// Return the result
return dp[ind, sum];
}
// Function to return the count of subsequence
// in an array with sum less than or equal to X
static int countSubsequence(int[] A, int N, int X)
{
// Initialize a DP array
int [,]dp = new int[N, X + 1];
for(int i = 0; i < N; i++)
{
for(int j = 0; j < X + 1; j++)
{
dp[i, j] = -1;
}
}
// Return the result
return countSubsequenceUtil(0, X, A,
N, dp) - 1;
}
// Driver Code
public static void Main(String[] args)
{
int []arr = { 25, 13, 40 };
int X = 50;
int N = arr.Length;
Console.Write(countSubsequence(arr, N, X));
}
}
// This code is contributed by 29AjayKumar
JavaScript
<script>
// JavaScript program to count number
// of subsequences in an array
// with sum less than or equal to X
// Utility function to return the count
// of subsequence in an array with sum
// less than or equal to X
function countSubsequenceUtil(ind, sum, A, N, dp)
{
// Base condition
if (ind == N)
return 1;
// Return if the sub-problem
// is already calculated
if (dp[ind][sum] != -1)
return dp[ind][sum];
// Check if the current element is
// less than or equal to sum
if (A[ind] <= sum)
{
// Count subsequences excluding
// the current element
dp[ind][sum] = countSubsequenceUtil(
ind + 1, sum,
A, N, dp) +
// Count subsequences
// including the current
// element
countSubsequenceUtil(
ind + 1,
sum - A[ind],
A, N, dp);
}
else
{
// Exclude current element
dp[ind][sum] = countSubsequenceUtil(
ind + 1, sum,
A, N, dp);
}
// Return the result
return dp[ind][sum];
}
// Function to return the count of subsequence
// in an array with sum less than or equal to X
function countSubsequence(A, N, X)
{
// Initialize a DP array
let dp = new Array(N);
for(var i = 0; i < dp.length; i++)
{
dp[i] = new Array(2);
}
for(let i = 0; i < N; i++)
{
for(let j = 0; j < X + 1; j++)
{
dp[i][j] = -1;
}
}
// Return the result
return countSubsequenceUtil(0, X, A,
N, dp) - 1;
}
// Driver Code
let arr = [ 25, 13, 40 ], X = 50;
let N = arr.length;
document.write(countSubsequence(arr, N, X));
// This code is contributed by susmitakundugoaldanga
</script>
Time Complexity: O(N*X)
Auxiliary Space: O(N*X)
Efficient approach: Using DP Tabulation method ( Iterative approach )
The approach to solve this problem is same but DP tabulation(bottom-up) method is better then Dp + memoization(top-down) because memoization method needs extra stack space of recursion calls.
Steps to solve this problem :
- Create a DP to store the solution of the subproblems.
- Initialize the DP with base cases when index = n then dp[i][j] = 1.
- Now Iterate over subproblems to get the value of current problem form previous computation of subproblems stored in DP.
- Return the final solution stored in dp[0][x] - 1.
Implementation :
C++
// C++ program for above approach
#include <bits/stdc++.h>
using namespace std;
int countSubsequence(int* A, int N, int X)
{
// Initialize a DP array
int dp[N+1][X+1];
memset(dp, 0, sizeof(dp));
// Set Base Case
for(int i=0 ; i<=N ;i++){
for(int j=0 ;j<=X ; j++){
if(i==N){
dp[i][j] = 1;
}
}
}
// Fill the DP table
// iterate over subproblems and get the current
// solution for previous computations
for(int i=N-1; i>=0; i--) {
for(int j=1; j<=X; j++) {
// update current value
if(A[i] <= j) { // Fixed index here
dp[i][j] = dp[i+1][j] + dp[i+1][j-A[i]];
} else {
dp[i][j] = dp[i+1][j];
}
}
}
// Return the result
return dp[0][X] -1;
}
// Driver Code
int main()
{
int arr[] = { 25, 13, 40 }, X = 50;
int N = sizeof(arr) / sizeof(arr[0]);
// function call
cout << countSubsequence(arr, N, X);
return 0;
}
// This code is contributed by bhardwajji.
Java
import java.util.*;
public class Main {
public static void main(String[] args) {
int[] arr = {25, 13, 40};
int X = 50;
int N = arr.length;
System.out.println(countSubsequence(arr, N, X));
}
public static int countSubsequence(int[] A, int N, int X) {
// Initialize a DP array
int[][] dp = new int[N+1][X+1];
for (int[] row : dp) {
Arrays.fill(row, 0);
}
// Set Base Case
for (int i = 0; i <= N; i++) {
for (int j = 0; j <= X; j++) {
if (i == N) {
dp[i][j] = 1;
}
}
}
// Fill the DP table
// iterate over subproblems and get the current
// solution for previous computations
for (int i = N-1; i >= 0; i--) {
for (int j = 1; j <= X; j++) {
// update current value
if (A[i] <= j) {
dp[i][j] = dp[i+1][j] + dp[i+1][j-A[i]];
} else {
dp[i][j] = dp[i+1][j];
}
}
}
// Return the result
return dp[0][X] - 1;
}
}
Python3
def countSubsequence(A, N, X):
# Initialize a DP array
dp = [[0 for j in range(X+1)] for i in range(N+1)]
# Set Base Case
for i in range(N+1):
for j in range(X+1):
if i == N:
dp[i][j] = 1
# Fill the DP table
# iterate over subproblems and get the current
# solution for previous computations
for i in range(N-1, -1, -1):
for j in range(1, X+1):
# update current value
if A[i] <= j:
dp[i][j] = dp[i+1][j] + dp[i+1][j-A[i]]
else:
dp[i][j] = dp[i+1][j]
# Return the result
return dp[0][X] - 1
# Driver Code
arr = [25, 13, 40]
X = 50
N = len(arr)
# function call
print(countSubsequence(arr, N, X))
C#
using System;
class Program {
// Function to count subsequences of an array with sum X
static int CountSubsequence(int[] A, int N, int X)
{
// Initialize a DP array
int[, ] dp = new int[N + 1, X + 1];
for (int i = 0; i <= N; i++) {
for (int j = 0; j <= X; j++) {
dp[i, j] = 0;
}
}
// Set Base Case
for (int i = 0; i <= N; i++) {
for (int j = 0; j <= X; j++) {
if (i == N) {
dp[i, j] = 1;
}
}
}
// Fill the DP table
// iterate over subproblems and get the current
// solution for previous computations
for (int i = N - 1; i >= 0; i--) {
for (int j = 1; j <= X; j++) {
// update current value
if (A[i] <= j) // Fixed index here
{
dp[i, j] = dp[i + 1, j]
+ dp[i + 1, j - A[i]];
}
else {
dp[i, j] = dp[i + 1, j];
}
}
}
// Return the result
return dp[0, X] - 1;
}
static void Main(string[] args)
{
int[] arr = { 25, 13, 40 };
int X = 50;
int N = arr.Length;
// function call
Console.WriteLine(CountSubsequence(arr, N, X));
}
}
JavaScript
function countSubsequence(A, N, X) {
// Initialize a DP array
const dp = Array.from({ length: N + 1 }, () => Array(X + 1).fill(0));
// Set Base Case
for (let i = 0; i <= N; i++) {
for (let j = 0; j <= X; j++) {
if (i === N) {
dp[i][j] = 1;
}
}
}
// Fill the DP table
// iterate over subproblems and get the current
// solution for previous computations
for (let i = N - 1; i >= 0; i--) {
for (let j = 1; j <= X; j++) {
// update current value
if (A[i] <= j) {
dp[i][j] = dp[i + 1][j] + dp[i + 1][j - A[i]];
} else {
dp[i][j] = dp[i + 1][j];
}
}
}
// Return the result
return dp[0][X] - 1;
}
// Driver Code
const arr = [25, 13, 40];
const X = 50;
const N = arr.length;
// function call
console.log(countSubsequence(arr, N, X));
// This code is contributed by Samim Hossain Mondal.
Time Complexity: O(N*X)
Auxiliary Space: O(N*X)
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