Longest common subarray in the given two arrays
Last Updated :
09 Nov, 2023
Given two arrays A[] and B[] of N and M integers respectively, the task is to find the maximum length of an equal subarray or the longest common subarray between the two given array.
Examples:
Input: A[] = {1, 2, 8, 2, 1}, B[] = {8, 2, 1, 4, 7}
Output: 3
Explanation:
The subarray that is common to both arrays are {8, 2, 1} and the length of the subarray is 3.
Input: A[] = {1, 2, 3, 2, 1}, B[] = {8, 7, 6, 4, 7}
Output: 0
Explanation:
There is no such subarrays which are equal in the array A[] and B[].
Naive Approach: The idea is to generate all the subarrays of the two given array A[] and B[] and find the longest matching subarray. This solution is exponential in terms of time complexity.
C++
#include <iostream>
#include <vector>
using namespace std;
// Recursive function to find the longest common subarray (LCS)
int LCS(int i, int j, const vector<int>& A, const vector<int>& B, int count) {
// Base case: If either of the indices reaches the end of the array, return the count
if (i == A.size() || j == B.size())
return count;
// If the current elements are equal, recursively check the next elements
if (A[i] == B[j])
count = LCS(i + 1, j + 1, A, B, count + 1);
// Recursively check for the longest common subarray by considering two possibilities:
// 1. Exclude current element from array A and continue with array B
// 2. Exclude current element from array B and continue with array A
count = max(count, max(LCS(i + 1, j, A, B, 0), LCS(i, j + 1, A, B, 0)));
return count;
}
int main() {
// Example arrays
vector<int> A = {1, 2, 3, 2, 1};
vector<int> B = {3, 2, 1, 4, 7};
// Call the LCS function to find the maximum length of the common subarray
int maxLength = LCS(0, 0, A, B, 0);
// Print the result
cout << "Max length of common subarray: " << maxLength << endl;
return 0;
}
Java
import java.util.Arrays;
import java.util.Vector;
public class Main {
// Recursive function to find the longest common subarray (LCS)
static int LCS(int i, int j, Vector<Integer> A,
Vector<Integer> B, int count) {
// Base case: If either of the indices reaches the end of the array, return the count
if (i == A.size() || j == B.size())
return count;
// If the current elements are equal, recursively check the next elements
if (A.get(i).equals(B.get(j)))
count = LCS(i + 1, j + 1, A, B, count + 1);
// Recursively check for the longest common subarray by considering two possibilities:
// 1. Exclude the current element from array A and continue with array B
// 2. Exclude the current element from array B and continue with array A
count = Math.max(count, Math.max(LCS(i + 1, j, A, B, 0),
LCS(i, j + 1, A, B, 0)));
return count;
}
public static void main(String[] args) {
// Example arrays
Vector<Integer> A = new Vector<>(Arrays.asList(1, 2, 3, 2, 1));
Vector<Integer> B = new Vector<>(Arrays.asList(3, 2, 1, 4, 7));
// Call the LCS function to find the maximum length of the common subarray
int maxLength = LCS(0, 0, A, B, 0);
// Print the result
System.out.println("Max length of common subarray: " + maxLength);
}
}
// This code is contributed by akshitaguprjz3
Python3
def LCS(i, j, A, B, count):
# Base case: If either of the indices reaches the end of the array,
# return the count
if i == len(A) or j == len(B):
return count
# If the current elements are equal, recursively check the
# next elements
if A[i] == B[j]:
count = LCS(i + 1, j + 1, A, B, count + 1)
# Recursively check for the longest common subarray by considering two possibilities:
# 1. Exclude current element from array A and continue with array B
# 2. Exclude current element from array B and continue with array A
return max(count, max(LCS(i + 1, j, A, B, 0), LCS(i, j + 1, A, B, 0)))
# Example arrays
A = [1, 2, 3, 2, 1]
B = [3, 2, 1, 4, 7]
# Call the LCS function to find the maximum length of the common subarray
maxLength = LCS(0, 0, A, B, 0)
# Print the result
print("Max length of common subarray:", maxLength)
C#
using System;
class GFG {
// Recursive function to find the longest common
// subarray (LCS)
static int LCS(int i, int j, int[] A, int[] B,
int count)
{
// Base case: If either of the indices reaches the
// end of the array, return the count
if (i == A.Length || j == B.Length)
return count;
// If the current elements are equal, recursively
// check the next elements
if (A[i] == B[j])
count = LCS(i + 1, j + 1, A, B, count + 1);
// Recursively check for the longest common subarray
// by considering two possibilities:
// 1. Exclude the current element from array A and
// continue with array B
// 2. Exclude the current element from array B and
// continue with array A
count = Math.Max(count,
Math.Max(LCS(i + 1, j, A, B, 0),
LCS(i, j + 1, A, B, 0)));
return count;
}
static void Main()
{
// Example arrays
int[] A = { 1, 2, 3, 2, 1 };
int[] B = { 3, 2, 1, 4, 7 };
// Call the LCS function to find the maximum length
// of the common subarray
int maxLength = LCS(0, 0, A, B, 0);
// Print the result
Console.WriteLine("Max length of common subarray: "
+ maxLength);
}
}
JavaScript
// Recursive function to find the longest common subarray (LCS)
function LCS(i, j, A, B, count) {
// Base case: If either of the indices reaches the end of the array, return the count
if (i === A.length || j === B.length) {
return count;
}
// If the current elements are equal, recursively check the next elements
if (A[i] === B[j]) {
count = LCS(i + 1, j + 1, A, B, count + 1);
}
// Recursively check for the longest common subarray by considering two possibilities:
// 1. Exclude the current element from array A and continue with array B
// 2. Exclude the current element from array B and continue with array A
count = Math.max(count, Math.max(LCS(i + 1, j, A, B, 0), LCS(i, j + 1, A, B, 0)));
return count;
}
// Example arrays
const A = [1, 2, 3, 2, 1];
const B = [3, 2, 1, 4, 7];
// Call the LCS function to find the maximum length of the common subarray
const maxLength = LCS(0, 0, A, B, 0);
// Print the result
console.log("Max length of common subarray: " + maxLength);
OutputMax length of common subarray: 3
Time Complexity: O(2N+M), where N is the length of the array A[] and M is the length of the array B[].
Efficient Approach:
The efficient approach is to use Dynamic Programming(DP). This problem is the variation of the Longest Common Subsequence(LCS).
Let the input sequences are A[0..n-1] and B[0..m-1] of lengths m & n respectively. Following is the recursive implementation of the equal subarrays:
- Since common subarray of A[] and B[] must start at some index i and j such that A[i] is equals to B[j]. Let dp[i][j] be the longest common subarray of A[i...] and B[j...].
- Therefore, for any index i and j, if A[i] is equals to B[j], then dp[i][j] = dp[i+1][j+1] + 1.
- The maximum of all the elements in the array dp[][] will give the maximum length of equal subarrays.
For Example:
If the given array A[] = {1, 2, 8, 2, 1} and B[] = {8, 2, 1, 4, 7}.
If the characters match at index i and j for the array A[] and B[] respectively, then dp[i][j] will be updated as 1 + dp[i+1][j+1].
Below is the updated dp[][] table for the given array A[] and B[].

Below is the implementation of the above approach:
C++
// C++ program to DP approach
// to above solution
#include <bits/stdc++.h>
using namespace std;
// Function to find the maximum
// length of equal subarray
int FindMaxLength(int A[], int B[], int n, int m)
{
// Auxiliary dp[][] array
int dp[n + 1][m + 1];
for (int i = 0; i <= n; i++)
for (int j = 0; j <= m; j++)
dp[i][j] = 0;
// Updating the dp[][] table
// in Bottom Up approach
for (int i = n - 1; i >= 0; i--) {
for (int j = m - 1; j >= 0; j--) {
// If A[i] is equal to B[i]
// then dp[j][i] = dp[j + 1][i + 1] + 1
if (A[i] == B[j])
dp[i][j] = dp[i + 1][j + 1] + 1;
}
}
int maxm = 0;
// Find maximum of all the values
// in dp[][] array to get the
// maximum length
for (int i = 0; i < n; i++) {
for (int j = 0; j < m; j++) {
// Update the length
maxm = max(maxm, dp[i][j]);
}
}
// Return the maximum length
return maxm;
}
// Driver Code
int main()
{
int A[] = { 1, 2, 8, 2, 1 };
int B[] = { 8, 2, 1, 4, 7 };
int n = sizeof(A) / sizeof(A[0]);
int m = sizeof(B) / sizeof(B[0]);
// Function call to find
// maximum length of subarray
cout << (FindMaxLength(A, B, n, m));
}
// This code is contributed by chitranayal
Java
// Java program to DP approach
// to above solution
class GFG
{
// Function to find the maximum
// length of equal subarray
static int FindMaxLength(int A[], int B[], int n, int m)
{
// Auxiliary dp[][] array
int[][] dp = new int[n + 1][m + 1];
for (int i = 0; i <= n; i++)
for (int j = 0; j <= m; j++)
dp[i][j] = 0;
// Updating the dp[][] table
// in Bottom Up approach
for (int i = n - 1; i >= 0; i--)
{
for (int j = m - 1; j >= 0; j--)
{
// If A[i] is equal to B[i]
// then dp[j][i] = dp[j + 1][i + 1] + 1
if (A[i] == B[j])
dp[i][j] = dp[i + 1][j + 1] + 1;
}
}
int maxm = 0;
// Find maximum of all the values
// in dp[][] array to get the
// maximum length
for (int i = 0; i < n; i++)
{
for (int j = 0; j < m; j++)
{
// Update the length
maxm = Math.max(maxm, dp[i][j]);
}
}
// Return the maximum length
return maxm;
}
// Driver Code
public static void main(String[] args)
{
int A[] = { 1, 2, 8, 2, 1 };
int B[] = { 8, 2, 1, 4, 7 };
int n = A.length;
int m = B.length;
// Function call to find
// maximum length of subarray
System.out.print(FindMaxLength(A, B, n, m));
}
}
// This code is contributed by PrinciRaj1992
Python3
# Python program to DP approach
# to above solution
# Function to find the maximum
# length of equal subarray
def FindMaxLength(A, B):
n = len(A)
m = len(B)
# Auxiliary dp[][] array
dp = [[0 for i in range(n + 1)] for i in range(m + 1)]
# Updating the dp[][] table
# in Bottom Up approach
for i in range(n - 1, -1, -1):
for j in range(m - 1, -1, -1):
# If A[i] is equal to B[i]
# then dp[j][i] = dp[j + 1][i + 1] + 1
if A[i] == B[j]:
dp[i][j] = dp[i + 1][j + 1] + 1
maxm = 0
# Find maximum of all the values
# in dp[][] array to get the
# maximum length
for i in dp:
for j in i:
# Update the length
maxm = max(maxm, j)
# Return the maximum length
return maxm
# Driver Code
if __name__ == '__main__':
A = [1, 2, 8, 2, 1]
B = [8, 2, 1, 4, 7]
# Function call to find
# maximum length of subarray
print(FindMaxLength(A, B))
C#
// C# program to DP approach
// to above solution
using System;
class GFG
{
// Function to find the maximum
// length of equal subarray
static int FindMaxLength(int[] A, int[] B, int n, int m)
{
// Auxiliary [,]dp array
int[, ] dp = new int[n + 1, m + 1];
for (int i = 0; i <= n; i++)
for (int j = 0; j <= m; j++)
dp[i, j] = 0;
// Updating the [,]dp table
// in Bottom Up approach
for (int i = n - 1; i >= 0; i--)
{
for (int j = m - 1; j >= 0; j--)
{
// If A[i] is equal to B[i]
// then dp[j, i] = dp[j + 1, i + 1] + 1
if (A[i] == B[j])
dp[i, j] = dp[i + 1, j + 1] + 1;
}
}
int maxm = 0;
// Find maximum of all the values
// in [,]dp array to get the
// maximum length
for (int i = 0; i < n; i++) {
for (int j = 0; j < m; j++) {
// Update the length
maxm = Math.Max(maxm, dp[i, j]);
}
}
// Return the maximum length
return maxm;
}
// Driver Code
public static void Main(String[] args)
{
int[] A = { 1, 2, 8, 2, 1 };
int[] B = { 8, 2, 1, 4, 7 };
int n = A.Length;
int m = B.Length;
// Function call to find
// maximum length of subarray
Console.Write(FindMaxLength(A, B, n, m));
}
}
// This code is contributed by PrinciRaj1992
JavaScript
<script>
// Javascript program to DP approach
// to above solution
// Function to find the maximum
// length of equal subarray
function FindMaxLength(A,B,n,m)
{
// Auxiliary dp[][] array
let dp = new Array(n + 1);
for (let i = 0; i <= n; i++)
{
dp[i]=new Array(m+1);
for (let j = 0; j <= m; j++)
dp[i][j] = 0;
}
// Updating the dp[][] table
// in Bottom Up approach
for (let i = n - 1; i >= 0; i--)
{
for (let j = m - 1; j >= 0; j--)
{
// If A[i] is equal to B[i]
// then dp[i][j] = dp[i + 1][j + 1] + 1
if (A[i] == B[j])
dp[j][i] = dp[j + 1][i + 1] + 1;
}
}
let maxm = 0;
// Find maximum of all the values
// in dp[][] array to get the
// maximum length
for (let i = 0; i < n; i++)
{
for (let j = 0; j < m; j++)
{
// Update the length
maxm = Math.max(maxm, dp[i][j]);
}
}
// Return the maximum length
return maxm;
}
// Driver Code
let A=[1, 2, 8, 2, 1 ];
let B=[8, 2, 1, 4, 7];
let n = A.length;
let m = B.length;
// Function call to find
// maximum length of subarray
document.write(FindMaxLength(A, B, n, m));
// This code is contributed by avanitrachhadiya2155
</script>
Time Complexity: O(N*M), where N is the length of array A[] and M is the length of array B[].
Auxiliary Space: O(N*M)
Efficient approach: space optimization
In previous approach the dp[i][j] is depend upon the current and previous row of 2D matrix. So to optimize the space complexity we use only single vector dp for current row and a variable prev to get the previous computation.
Implementation Steps :
- Create a vector DP of size M+1 , that stores the computation of subproblems and initialize it with 0.
- Initialize a variable maxm with 0 used to store the maximum length.
- Now iterative over subproblems and update the DP vector in bottom up approach.
- Initialize variable prev and temp. prev is used to get the previous row value of Dp and temp is used to get the value of prev in another iteration.
- While iterating update the maxm with respect to the value stored in DP.
- At last return the maxm.
Implementation:
C++
#include <bits/stdc++.h>
using namespace std;
// Function to find the maximum
// length of equal subarray
int FindMaxLength(int A[], int B[], int n, int m)
{
// Auxiliary dp[] vector
vector<int> dp(m + 1, 0);
int maxm = 0;
// Updating the dp[] vector
// in Bottom Up approach
for (int i = n - 1; i >= 0; i--) {
int prev = 0;
for (int j = m - 1; j >= 0; j--) {
int temp = dp[j];
if (A[i] == B[j]) {
dp[j] = prev + 1;
maxm = max(maxm, dp[j]);
}
else {
dp[j] = 0;
}
prev = temp;
}
}
// Return the maximum length
return maxm;
}
// Driver Code
int main()
{
int A[] = { 1, 2, 8, 2, 1 };
int B[] = { 8, 2, 1, 4, 7 };
int n = sizeof(A) / sizeof(A[0]);
int m = sizeof(B) / sizeof(B[0]);
// Function call to find
// maximum length of subarray
cout << (FindMaxLength(A, B, n, m));
}
Java
import java.util.Arrays;
public class Main {
// Function to find the maximum length of equal subarray
static int findMaxLength(int[] A, int[] B, int n, int m) {
// Auxiliary dp[] array
int[] dp = new int[m + 1];
int maxm = 0;
// Updating the dp[] array in Bottom Up approach
for (int i = n - 1; i >= 0; i--) {
int prev = 0;
for (int j = m - 1; j >= 0; j--) {
int temp = dp[j];
if (A[i] == B[j]) {
dp[j] = prev + 1;
maxm = Math.max(maxm, dp[j]);
} else {
dp[j] = 0;
}
prev = temp;
}
}
// Return the maximum length
return maxm;
}
public static void main(String[] args) {
int[] A = { 1, 2, 8, 2, 1 };
int[] B = { 8, 2, 1, 4, 7 };
int n = A.length;
int m = B.length;
// Function call to find maximum length of subarray
System.out.println(findMaxLength(A, B, n, m));
}
}
Python3
# code
def find_max_length(A, B, n, m):
# Auxiliary dp[] list
dp = [0] * (m + 1)
maxm = 0
# Updating the dp[] list
# in Bottom Up approach
for i in range(n - 1, -1, -1):
prev = 0
for j in range(m - 1, -1, -1):
temp = dp[j]
if A[i] == B[j]:
dp[j] = prev + 1
maxm = max(maxm, dp[j])
else:
dp[j] = 0
prev = temp
# Return the maximum length
return maxm
# Driver Code
if __name__ == '__main__':
A = [1, 2, 8, 2, 1]
B = [8, 2, 1, 4, 7]
n = len(A)
m = len(B)
print(find_max_length(A, B, n, m))
C#
using System;
class Program
{
// Function to find the maximum
// length of equal subarray
static int FindMaxLength(int[] A, int[] B, int n, int m)
{
// Auxiliary dp[] array
int[] dp = new int[m + 1];
int maxm = 0;
// Updating the dp[] array
// in Bottom Up approach
for (int i = n - 1; i >= 0; i--)
{
int prev = 0;
for (int j = m - 1; j >= 0; j--)
{
int temp = dp[j];
if (A[i] == B[j])
{
dp[j] = prev + 1;
maxm = Math.Max(maxm, dp[j]);
}
else
{
dp[j] = 0;
}
prev = temp;
}
}
// Return the maximum length
return maxm;
}
// Driver Code
static void Main()
{
int[] A = { 1, 2, 8, 2, 1 };
int[] B = { 8, 2, 1, 4, 7 };
int n = A.Length;
int m = B.Length;
// Function call to find
// maximum length of subarray
Console.WriteLine(FindMaxLength(A, B, n, m));
}
}
JavaScript
// Function to find the maximum length of equal subarray
function FindMaxLength(A, B, n, m) {
// Auxiliary dp[] vector
let dp = new Array(m + 1).fill(0);
let maxm = 0;
// Updating the dp[] vector in Bottom Up approach
for (let i = n - 1; i >= 0; i--) {
let prev = 0;
for (let j = m - 1; j >= 0; j--) {
let temp = dp[j];
if (A[i] == B[j]) {
dp[j] = prev + 1;
maxm = Math.max(maxm, dp[j]);
} else {
dp[j] = 0;
}
prev = temp;
}
}
// Return the maximum length
return maxm;
}
let A = [1, 2, 8, 2, 1];
let B = [8, 2, 1, 4, 7];
let n = A.length;
let m = B.length;
// Function call to find maximum length of subarray
console.log(FindMaxLength(A, B, n, m));
Time Complexity: O(N*M)
Auxiliary Space: O(M) , only use one vector dp of size M
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Longest Common SubstringGiven two strings 's1' and 's2', find the length of the longest common substring. Example: Input: s1 = "GeeksforGeeks", s2 = "GeeksQuiz" Output : 5 Explanation:The longest common substring is "Geeks" and is of length 5.Input: s1 = "abcdxyz", s2 = "xyzabcd" Output : 4Explanation:The longest common su
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Longest Common Subsequence of two arrays out of which one array consists of distinct elements onlyGiven two arrays firstArr[], consisting of distinct elements only, and secondArr[], the task is to find the length of LCS between these 2 arrays. Examples: Input: firstArr[] = {3, 5, 1, 8}, secondArr[] = {3, 3, 5, 3, 8}Output: 3.Explanation: LCS between these two arrays is {3, 5, 8}. Input : firstAr
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Longest Repeating SubsequenceGiven a string s, the task is to find the length of the longest repeating subsequence, such that the two subsequences don't have the same string character at the same position, i.e. any ith character in the two subsequences shouldn't have the same index in the original string. Examples:Input: s= "ab
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Longest Common Anagram SubsequenceGiven two strings str1 and str2 of length n1 and n2 respectively. The problem is to find the length of the longest subsequence which is present in both the strings in the form of anagrams. Note: The strings contain only lowercase letters. Examples: Input : str1 = "abdacp", str2 = "ckamb" Output : 3
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Length of Longest Common Subsequence with given sum KGiven two arrays a[] and b[] and an integer K, the task is to find the length of the longest common subsequence such that sum of elements is equal to K. Examples: Input: a[] = { 9, 11, 2, 1, 6, 2, 7}, b[] = {1, 2, 6, 9, 2, 3, 11, 7}, K = 18Output: 3Explanation: Subsequence { 11, 7 } and { 9, 2, 7 }
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Longest Common Subsequence with no repeating characterGiven two strings s1 and s2, the task is to find the length of the longest common subsequence with no repeating character. Examples: Input: s1= "aabbcc", s2= "aabc"Output: 3Explanation: "aabc" is longest common subsequence but it has two repeating character 'a'.So the required longest common subsequ
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Find the Longest Common Subsequence (LCS) in given K permutationsGiven K permutations of numbers from 1 to N in a 2D array arr[][]. The task is to find the longest common subsequence of these K permutations. Examples: Input: N = 4, K = 3arr[][] = {{1, 4, 2, 3}, {4, 1, 2, 3}, {1, 2, 4, 3}}Output: 3Explanation: Longest common subsequence is {1, 2, 3} which has leng
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Find length of longest subsequence of one string which is substring of another stringGiven two strings X and Y. The task is to find the length of the longest subsequence of string X which is a substring in sequence Y.Examples: Input : X = "ABCD", Y = "BACDBDCD"Output : 3Explanation: "ACD" is longest subsequence of X which is substring of Y.Input : X = "A", Y = "A"Output : 1Perquisit
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Length of longest common prime subsequence from two given arraysGiven two arrays arr1[] and arr2[] of length N and M respectively, the task is to find the length of the longest common prime subsequence that can be obtained from the two given arrays. Examples: Input: arr1[] = {1, 2, 3, 4, 5, 6, 7, 8, 9}, arr2[] = {2, 5, 6, 3, 7, 9, 8} Output: 4 Explanation: The l
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A Space Optimized Solution of LCSGiven two strings, s1 and s2, the task is to find the length of the Longest Common Subsequence. If there is no common subsequence, return 0.Examples:Input: s1 = âABCDGHâ, s2 = âAEDFHRâOutput: 3Explanation: The longest subsequence present in both strings is "ADH".Input: s1 = âAGGTABâ, s2 = âGXTXAYBâO
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Longest common subarray in the given two arraysGiven two arrays A[] and B[] of N and M integers respectively, the task is to find the maximum length of an equal subarray or the longest common subarray between the two given array. Examples: Input: A[] = {1, 2, 8, 2, 1}, B[] = {8, 2, 1, 4, 7} Output: 3 Explanation: The subarray that is common to b
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Number of ways to insert a character to increase the LCS by oneGiven two strings A and B. The task is to count the number of ways to insert a character in string A to increase the length of the Longest Common Subsequence between string A and string B by 1. Examples: Input : A = "aa", B = "baaa" Output : 4 The longest common subsequence shared by string A and st
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Longest common subsequence with permutations allowedGiven two strings in lowercase, find the longest string whose permutations are subsequences of given two strings. The output longest string must be sorted. Examples: Input : str1 = "pink", str2 = "kite" Output : "ik" The string "ik" is the longest sorted string whose one permutation "ik" is subseque
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Longest subsequence such that adjacent elements have at least one common digitGiven an array arr[], the task is to find the length of the longest sub-sequence such that adjacent elements of the subsequence have at least one digit in common.Examples: Input: arr[] = [1, 12, 44, 29, 33, 96, 89] Output: 5 Explanation: The longest sub-sequence is [1 12 29 96 89]Input: arr[] = [12,
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Longest subsequence with different adjacent charactersGiven string str. The task is to find the longest subsequence of str such that all the characters adjacent to each other in the subsequence are different. Examples:Â Â Input: str = "ababa"Â Output: 5Â Explanation:Â "ababa" is the subsequence satisfying the condition Input: str = "xxxxy"Â Output: 2Â Explan
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Longest subsequence such that difference between adjacents is oneGiven an array arr[] of size n, the task is to find the longest subsequence such that the absolute difference between adjacent elements is 1.Examples: Input: arr[] = [10, 9, 4, 5, 4, 8, 6]Output: 3Explanation: The three possible subsequences of length 3 are [10, 9, 8], [4, 5, 4], and [4, 5, 6], wher
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Longest Uncommon SubsequenceGiven two strings, find the length of longest uncommon subsequence of the two strings. The longest uncommon subsequence is defined as the longest subsequence of one of these strings which is not a subsequence of other strings. Examples: Input : "abcd", "abc"Output : 4The longest subsequence is 4 bec
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LCS formed by consecutive segments of at least length KGiven two strings s1, s2 and K, find the length of the longest subsequence formed by consecutive segments of at least length K. Examples: Input : s1 = aggayxysdfa s2 = aggajxaaasdfa k = 4 Output : 8 Explanation: aggasdfa is the longest subsequence that can be formed by taking consecutive segments, m
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Longest Increasing Subsequence using Longest Common Subsequence AlgorithmGiven an array arr[] of N integers, the task is to find and print the Longest Increasing Subsequence.Examples: Input: arr[] = {12, 34, 1, 5, 40, 80} Output: 4 {12, 34, 40, 80} and {1, 5, 40, 80} are the longest increasing subsequences.Input: arr[] = {10, 22, 9, 33, 21, 50, 41, 60, 80} Output: 6 Prer
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