Number of M-length sorted arrays that can be formed using first N natural numbers
Last Updated :
23 Jul, 2025
Given two numbers N and M, the task is to find the number of sorted arrays that can be formed of size M using first N natural numbers, if each number can be taken any number of times.
Examples:
Input: N = 4, M = 2
Output: 10
Explanation: All such possible arrays are {1, 1}, {1, 2}, {1, 2}, {1, 4}, {2, 2}, {2, 3}, {2, 4}, {3, 3}, {3, 4}, {4, 4}.
Input: N = 2, M = 4
Output: 5
Explanation: All such possible arrays are {1, 1, 1, 1}, {1, 1, 1, 2}, {1, 1, 2, 2}, {1, 2, 2, 2}, {2, 2, 2, 2}.
Naive Approach: There are two choices for each number that it can be taken or can be left. Also, a number can be taken multiple times.
- Elements that are taken multiple times should be consecutive in the array as the array should be sorted.
- If an element is left and has moved to another element then that element can not be taken again.
Recursive Approach:

The left branch is indicating that the element is taken and the right branch indicating that the element is left and the pointer moved to the next element.
Below is the implementation of the above approach:
C++
// C++ program for the above approach
#include <bits/stdc++.h>
using namespace std;
// Function to find the number of
// M-length sorted arrays possible
// using numbers from the range [1, N]
int countSortedArrays(int start, int m,
int size, int n)
{
// If size becomes equal to m,
// that means an array is found
if (size == m)
return 1;
if (start > n)
return 0;
int notTaken = 0, taken = 0;
// Include current element, increase
// size by 1 and remain on the same
// element as it can be included again
taken = countSortedArrays(start, m,
size + 1, n);
// Exclude current element
notTaken = countSortedArrays(start + 1,
m, size, n);
// Return the sum obtained
// in both the cases
return taken + notTaken;
}
// Driver Code
int main()
{
// Given Input
int n = 2, m = 3;
// Function Call
cout << countSortedArrays(1, m, 0, n);
return 0;
}
Java
// Java program for the above approach
import java.util.*;
import java.lang.*;
class GFG{
// Function to find the number of
// M-length sorted arrays possible
// using numbers from the range [1, N]
static int countSortedArrays(int start, int m,
int size, int n)
{
// If size becomes equal to m,
// that means an array is found
if (size == m)
return 1;
if (start > n)
return 0;
int notTaken = 0, taken = 0;
// Include current element, increase
// size by 1 and remain on the same
// element as it can be included again
taken = countSortedArrays(start, m,
size + 1, n);
// Exclude current element
notTaken = countSortedArrays(start + 1,
m, size, n);
// Return the sum obtained
// in both the cases
return taken + notTaken;
}
// Driver Code
public static void main(String[] args)
{
// Given Input
int n = 2, m = 3;
// Function Call
System.out.println(countSortedArrays(1, m, 0, n));
}
}
// This code is contributed by sanjoy_62
Python3
# Python3 program for the above approach
# Function to find the number of
# M-length sorted arrays possible
# using numbers from the range [1, N]
def countSortedArrays(start, m, size, n):
# If size becomes equal to m,
# that means an array is found
if (size == m):
return 1
if (start > n):
return 0
notTaken, taken = 0, 0
# Include current element, increase
# size by 1 and remain on the same
# element as it can be included again
taken = countSortedArrays(start, m,
size + 1, n)
# Exclude current element
notTaken = countSortedArrays(start + 1,
m, size, n)
# Return the sum obtained
# in both the cases
return taken + notTaken
# Driver Code
if __name__ == '__main__':
# Given Input
n, m = 2, 3
# Function Call
print (countSortedArrays(1, m, 0, n))
# This code is contributed by mohit kumar 29
C#
// C# program for the above approach
using System;
class GFG{
// Function to find the number of
// M-length sorted arrays possible
// using numbers from the range [1, N]
static int countSortedArrays(int start, int m,
int size, int n)
{
// If size becomes equal to m,
// that means an array is found
if (size == m)
return 1;
if (start > n)
return 0;
int notTaken = 0, taken = 0;
// Include current element, increase
// size by 1 and remain on the same
// element as it can be included again
taken = countSortedArrays(start, m,
size + 1, n);
// Exclude current element
notTaken = countSortedArrays(start + 1,
m, size, n);
// Return the sum obtained
// in both the cases
return taken + notTaken;
}
// Driver Code
public static void Main()
{
// Given Input
int n = 2, m = 3;
// Function Call
Console.WriteLine(countSortedArrays(1, m, 0, n));
}
}
// This code is contributed by susmitakundugoaldanga
JavaScript
<script>
// JavaScript program for the above approach
// Function to find the number of
// M-length sorted arrays possible
// using numbers from the range [1, N]
function countSortedArrays(start, m, size, n)
{
// If size becomes equal to m,
// that means an array is found
if (size === m)
return 1;
if (start > n)
return 0;
var notTaken = 0,
taken = 0;
// Include current element, increase
// size by 1 and remain on the same
// element as it can be included again
taken = countSortedArrays(start, m, size + 1, n);
// Exclude current element
notTaken = countSortedArrays(start + 1, m,
size, n);
// Return the sum obtained
// in both the cases
return taken + notTaken;
}
// Driver Code
// Given Input
var n = 2,
m = 3;
// Function Call
document.write(countSortedArrays(1, m, 0, n));
// This code is contributed by rdtank
</script>
Time Complexity: O(2N)
Auxiliary Space: O(1)
Recursive Approach with optimization:
- Traverse through each element and try to find all possible arrays starting from that element.
- In the previous approach for the right branch, the element is left first and in the next step, shifted to the next element.
- In this approach, instead of leaving the element first and then moving to the next element, directly go to the next element, so there will be fewer function calls.

Below is the implementation of the above approach:
C++
// C++ program for the above approach
#include <bits/stdc++.h>
using namespace std;
// Function to find the number of
// M-length sorted arrays possible
// using numbers from the range [1, N]
void countSortedArrays(int st, int n,
int m, int& ans, int size)
{
// If size becomes equal to m
// one sorted array is found
if (size == m) {
ans += 1;
return;
}
// Traverse over the range [st, N]
for (int i = st; i <= n; i++) {
// Find all sorted arrays
// starting from i
countSortedArrays(i, n, m,
ans, size + 1);
}
}
// Driver Code
int main()
{
// Given Input
int n = 2, m = 3;
// Store the required result
int ans = 0;
// Function Call
countSortedArrays(1, n, m, ans, 0);
// Print the result
cout << ans;
return 0;
}
Java
// Java program for the above approach
import java.io.*;
class GFG{
// Function to find the number of
// M-length sorted arrays possible
// using numbers from the range [1, N]
static int countSortedArrays(int st, int n,
int m, int ans,
int size)
{
// If size becomes equal to m
// one sorted array is found
if (size == m)
{
ans += 1;
System.out.println(ans);
return ans;
}
// Traverse over the range [st, N]
for(int i = st; i <= n; i++)
{
// Find all sorted arrays
// starting from i
ans = countSortedArrays(i, n, m,
ans, size + 1);
}
return ans;
}
// Driver Code
public static void main(String[] args)
{
// Given Input
int n = 2, m = 3;
// Store the required result
int ans = 0;
// Function Call
ans = countSortedArrays(1, n, m, ans, 0);
// Print the result
System.out.println(ans);
}
}
// This code is contributed by Dharanendra L V.
Python3
# Python program for the above approach
# Function to find the number of
# M-length sorted arrays possible
# using numbers from the range [1, N]
def countSortedArrays( st, n, m, ans, size):
# If size becomes equal to m
# one sorted array is found
if (size == m):
ans += 1
return ans
# Traverse over the range [st, N]
for i in range(st,n+1):
# Find all sorted arrays
# starting from i
ans = countSortedArrays(i, n, m, ans, size + 1)
return ans
# Given Input
n = 2
m = 3
# Store the required result
ans = 0
# Function Call
ans = countSortedArrays(1, n, m, ans, 0)
# Print the result
print(ans)
# This code is contributed by unknown2108.
C#
// C# program for the above approach
using System;
class GFG{
// Function to find the number of
// M-length sorted arrays possible
// using numbers from the range [1, N]
static int countSortedArrays(int st, int n,
int m, int ans,
int size)
{
// If size becomes equal to m
// one sorted array is found
if (size == m)
{
ans += 1;
return ans;
}
// Traverse over the range [st, N]
for(int i = st; i <= n; i++)
{
// Find all sorted arrays
// starting from i
ans = countSortedArrays(i, n, m,
ans, size + 1);
}
return ans;
}
// Driver Code
public static void Main(String[] args)
{
// Given Input
int n = 2, m = 3;
// Store the required result
int ans = 0;
// Function Call
ans = countSortedArrays(1, n, m, ans, 0);
// Print the result
Console.Write(ans);
}
}
// This code is contributed by shivanisinghss2110
JavaScript
<script>
// JavaScript program for the above approach
// Function to find the number of
// M-length sorted arrays possible
// using numbers from the range [1, N]
function countSortedArrays( st, n, m, ans, size)
{
// If size becomes equal to m
// one sorted array is found
if (size == m)
{
ans += 1;
return ans;
}
// Traverse over the range [st, N]
for(var i = st; i <= n; i++)
{
// Find all sorted arrays
// starting from i
ans = countSortedArrays(i, n, m, ans, size + 1);
}
return ans;
}
// Given Input
var n = 2, m = 3;
// Store the required result
var ans = 0;
// Function Call
ans = countSortedArrays(1, n, m, ans, 0);
// Print the result
document.write(ans);
// This code is contributed by SoumikMondal
</script>
Time Complexity: O(2N)
Auxiliary Space: O(1)
Dynamic Programming Approach: It can be observed that this problem has overlapping subproblems and optimal substructure property, i.e, it satisfies both properties of dynamic programming. So, the idea is to use a 2D table to memorize the results during the function calls.
Below is the implementation of the above approach:
C++
// C++ program for the above approach
#include <bits/stdc++.h>
using namespace std;
// Function to find the number of
// M-length sorted arrays possible
// using numbers from the range [1, N]
int countSortedArrays(vector<vector<int> >& dp,
int m, int n)
{
// Base cases
if (m == 0) {
return 1;
}
if (n <= 0)
return 0;
// If the result is already computed,
// return the result of the state
if (dp[m][n] != -1)
return dp[m][n];
int taken = 0, notTaken = 0;
// Include current element, decrease
// required size by 1 and remain on the
// same element, as it can be taken again
taken = countSortedArrays(dp, m - 1, n);
// If element is not included
notTaken = countSortedArrays(dp, m, n - 1);
// Store the result and return it
return dp[m][n] = taken + notTaken;
}
// Driver Code
int main()
{
// Given Input
int n = 2, m = 3;
// Create an 2D array for memoization
vector<vector<int> > dp(m + 1,
vector<int>(n + 1, -1));
// Function Call
cout << countSortedArrays(dp, m, n);
return 0;
}
Java
import java.util.*;
import java.io.*;
// Java program for the above approach
class GFG{
// Function to find the number of
// M-length sorted arrays possible
// using numbers from the range [1, N]
static int countSortedArrays(ArrayList<ArrayList<Integer>> dp, int m, int n)
{
// Base cases
if (m == 0) {
return 1;
}
if (n <= 0){
return 0;
}
// If the result is already computed,
// return the result of the state
if (dp.get(m).get(n) != -1){
return dp.get(m).get(n);
}
int taken = 0, notTaken = 0;
// Include current element, decrease
// required size by 1 and remain on the
// same element, as it can be taken again
taken = countSortedArrays(dp, m - 1, n);
// If element is not included
notTaken = countSortedArrays(dp, m, n - 1);
// Store the result and return it
dp.get(m).set(n, taken + notTaken);
return taken + notTaken;
}
public static void main(String args[])
{
// Given Input
int n = 2, m = 3;
// Create an 2D array for memoization
ArrayList<ArrayList<Integer>> dp = new ArrayList<ArrayList<Integer>>();
for(int i = 0 ; i <= m ; i++){
dp.add(new ArrayList<Integer>());
for(int j = 0 ; j <=n ; j++){
dp.get(i).add(-1);
}
}
// Function Call
System.out.println(countSortedArrays(dp, m, n));
}
}
// This code is contributed by subhamgoyal2014.
Python3
# Python3 program for the above approach
# Function to find the number of
# M-length sorted arrays possible
# using numbers from the range [1, N]
def countSortedArrays(dp, m, n):
# Base cases
if(m == 0):
return 1
if(n <= 0):
return 0
# If the result is already computed,
# return the result of the state
if(dp[m][n] != -1):
return dp[m][n]
taken, notTaken = 0, 0
# Include current element, decrease
# required size by 1 and remain on the
# same element, as it can be taken again
taken = countSortedArrays(dp, m - 1, n)
# If element is not included
notTaken = countSortedArrays(dp, m, n - 1)
# Store the result and return it
dp[m][n] = taken + notTaken
return dp[m][n]
# Driver Code
if __name__ == '__main__':
# Given Input
n, m = 2, 3
# Create an 2D array for memoization
dp=[[-1 for i in range(n+1)] for j in range(m+1)]
# Function Call
print (countSortedArrays(dp, m, n))
# This code is contributed by Pushpesh Raj
C#
// C# program to implement above approach
using System;
using System.Collections;
using System.Collections.Generic;
class GFG
{
// Function to find the number of
// M-length sorted arrays possible
// using numbers from the range [1, N]
static int countSortedArrays(List<List<int>> dp, int m, int n)
{
// Base cases
if (m == 0) {
return 1;
}
if (n <= 0){
return 0;
}
// If the result is already computed,
// return the result of the state
if (dp[m][n] != -1){
return dp[m][n];
}
int taken = 0, notTaken = 0;
// Include current element, decrease
// required size by 1 and remain on the
// same element, as it can be taken again
taken = countSortedArrays(dp, m - 1, n);
// If element is not included
notTaken = countSortedArrays(dp, m, n - 1);
// Store the result and return it
dp[m][n] = taken + notTaken;
return taken + notTaken;
}
// Driver code
public static void Main(string[] args){
// Given Input
int n = 2, m = 3;
// Create an 2D array for memoization
List<List<int>> dp = new List<List<int>>();
for(int i = 0 ; i <= m ; i++){
dp.Add(new List<int>());
for(int j = 0 ; j <= n ; j++){
dp[i].Add(-1);
}
}
// Function Call
Console.WriteLine(countSortedArrays(dp, m, n));
}
}
// This code is contributed by entertain2022.
JavaScript
// Function to find the number of
// M-length sorted arrays possible
// using numbers from the range [1, N]
function countSortedArrays( dp,m, n)
{
// Base cases
if (m == 0) {
return 1;
}
if (n <= 0)
return 0;
// If the result is already computed,
// return the result of the state
if (dp[m][n] != -1)
return dp[m][n];
let taken = 0, notTaken = 0;
// Include current element, decrease
// required size by 1 and remain on the
// same element, as it can be taken again
taken = countSortedArrays(dp, m - 1, n);
// If element is not included
notTaken = countSortedArrays(dp, m, n - 1);
// Store the result and return it
return dp[m][n] = taken + notTaken;
}
// Driver Code
// Given Input
let n = 2, m = 3;
// Create an 2D array for memoization
var dp = new Array(m+1);
for(let i = 0; i <= m; i++)
dp[i] = new Array(n+1);
for(let i = 0; i <= m; i++)
for(let j = 0; j <= n; j++)
dp[i][j] = -1;
// Function Call
console.log(countSortedArrays(dp, m, n));
// This code is contributed by garg28harsh.
Time Complexity: O(N*M)
Auxiliary Space: O(N*M)
Space Optimized Iterative Dynamic Programming Approach:
- As all elements are available as many times as needed, so there is no need to save values for previous rows, the values from the same row can be used.
- So a 1-D array can be used to save previous results.
- Create an array, dp of size M, where dp[i] stores the maximum number of sorted arrays of size i that can be formed from numbers in the range [1, N].
Below is the implementation of the above approach:
C++
// C++ program for the above approach
#include <bits/stdc++.h>
using namespace std;
// Function to find the number of
// M-length sorted arrays possible
// using numbers from the range [1, N]
int countSortedArrays(int n, int m)
{
// Create an array of size M+1
vector<int> dp(m + 1, 0);
// Base cases
dp[0] = 1;
// Fill the dp table
for (int i = 1; i <= n; i++) {
for (int j = 1; j <= m; j++) {
// dp[j] will be equal to maximum
// number of sorted array of size j
// when elements are taken from 1 to i
dp[j] = dp[j - 1] + dp[j];
}
// Here dp[m] will be equal to the
// maximum number of sorted arrays when
// element are taken from 1 to i
}
// Return the result
return dp[m];
}
// Driver Code
int main()
{
// Given Input
int n = 2, m = 3;
// Function Call
cout << countSortedArrays(n, m);
return 0;
}
Java
// Java program for the above approach
public class Main
{
// Function to find the number of
// M-length sorted arrays possible
// using numbers from the range [1, N]
static int countSortedArrays(int n, int m)
{
// Create an array of size M+1
int[] dp = new int[(m + 1)];
// Base cases
dp[0] = 1;
// Fill the dp table
for (int i = 1; i <= n; i++) {
for (int j = 1; j <= m; j++) {
// dp[j] will be equal to maximum
// number of sorted array of size j
// when elements are taken from 1 to i
dp[j] = dp[j - 1] + dp[j];
}
// Here dp[m] will be equal to the
// maximum number of sorted arrays when
// element are taken from 1 to i
}
// Return the result
return dp[m];
}
// Driver code
public static void main(String[] args)
{
// Given Input
int n = 2, m = 3;
// Function Call
System.out.print(countSortedArrays(n, m));
}
}
// This code is contributed by suresh07.
Python3
# Python program for the above approach
# Function to find the number of
# M-length sorted arrays possible
# using numbers from the range [1, N]
def countSortedArrays(n, m):
# Create an array of size M+1
dp = [0 for _ in range(m + 1)]
# Base cases
dp[0] = 1
# Fill the dp table
for i in range(1, n + 1):
for j in range(1, m + 1):
# dp[j] will be equal to maximum
# number of sorted array of size j
# when elements are taken from 1 to i
dp[j] = dp[j - 1] + dp[j]
# Here dp[m] will be equal to the
# maximum number of sorted arrays when
# element are taken from 1 to i
# Return the result
return dp[m]
# Driver code
# Given Input
n = 2
m = 3
# Function Call
print (countSortedArrays(n, m))
# This code is contributed by rdtank.
C#
// C# program for the above approach
using System;
class GFG {
// Function to find the number of
// M-length sorted arrays possible
// using numbers from the range [1, N]
static int countSortedArrays(int n, int m)
{
// Create an array of size M+1
int[] dp = new int[(m + 1)];
// Base cases
dp[0] = 1;
// Fill the dp table
for (int i = 1; i <= n; i++) {
for (int j = 1; j <= m; j++) {
// dp[j] will be equal to maximum
// number of sorted array of size j
// when elements are taken from 1 to i
dp[j] = dp[j - 1] + dp[j];
}
// Here dp[m] will be equal to the
// maximum number of sorted arrays when
// element are taken from 1 to i
}
// Return the result
return dp[m];
}
// Driver Code
public static void Main()
{
// Given Input
int n = 2, m = 3;
// Function Call
Console.WriteLine(countSortedArrays(n, m));
}
}
// This code is contributed by ukasp.
JavaScript
<script>
// JavaScript program for the above approach
// Function to find the number of
// M-length sorted arrays possible
// using numbers from the range [1, N]
function countSortedArrays(n, m)
{
// Create an array of size M+1
let dp = new Array(m + 1);
dp.fill(0);
// Base cases
dp[0] = 1;
// Fill the dp table
for (let i = 1; i <= n; i++) {
for (let j = 1; j <= m; j++) {
// dp[j] will be equal to maximum
// number of sorted array of size j
// when elements are taken from 1 to i
dp[j] = dp[j - 1] + dp[j];
}
// Here dp[m] will be equal to the
// maximum number of sorted arrays when
// element are taken from 1 to i
}
// Return the result
return dp[m];
}
// Given Input
let n = 2, m = 3;
// Function Call
document.write(countSortedArrays(n, m));
</script>
Time Complexity: O(N*M)
Auxiliary Space: O(M)
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