In this tutorial, you will learn how the **longest common subsequence** is found. Additionally, you will discover working instances of the longest common subsequence in C, C++, Java, and Python.

A subsequence is a sequence that can be derived from another sequence by erasing a few elements without changing the order of the remaining elements. The longest common subsequence (LCS) of 2 sequences is a subsequence, with maximal length, which is common to both the sequences.

The longest common subsequence (LCS) is characterized as the longest subsequence that is normal to every one of the given sequences, given that the elements of the subsequence are not needed to occupy situations inside the original sequences.

On the off chance that S1 and S2 are the two given sequences then, Z is the common subsequence of S1 and S2 if Z is a subsequence of both S1 and S2. Besides, Z should be a strictly increasing sequence of the lists of both S1 and S2.

In a strictly increasing sequence, the lists of the elements chosen from the original sequences should be in ascending order in Z.

if

S1 = {B, C, D, A, A, C, D}

At that point, {A, D, B} can’t be a subsequence of S1 as the order for the elements isn’t something similar (ie. not strictly increasing sequence).

Let us understand LCS with an example.

If

S1 = {B, C, D, A, A, C, D} S2 = {A, C, D, B, A, C}

At that point, common subsequences are

{B, C}, {C, D, A, C}, {D, A, C}, {A, A, C}, {A, C}, {C, D}, …

Among these subsequences, {C, D, A, C} is the longest common subsequences. We will track down this longest common subsequence using dynamic programming.

Before proceeding further, if you do not already know about dynamic programming, please go through dynamic programming.

Contents

## Using Dynamic Programming to find the LCS

Let us take two sequences:

The accompanying steps are followed for tracking down the longest common subsequence.

- Create a table of measurement n+1*m+1 where n and m are the lengths of X and Y respectively. The first row and the first column are loaded up with zeros.

2. Fill every cell of the table using the accompanying logic.

3. In the event that the character corresponding to the current row and current column are matching, at that point fill the current cell by adding one to the diagonal element. Point an arrow to the diagonal cell.

4. Else take the maximum incentive from the previous column and previous row element for filling the current cell. Direct a bolt toward the cell with maximum worth. In the event that they are equivalent, highlight any of them.

5. Step 2 is repeated until the table is filled.

6. The value in the last row and the last column is the length of the longest common subsequence.

7. To track down the longest common subsequence, start from the last element and follow the direction of the arrow. The elements relating to () symbol structure the longest common subsequence.

Subsequently, the longest common subsequence is CD.

**How is a dynamic programming algorithm more effective than the recursive algorithm while taking care of an LCS issue?**

The technique for dynamic programming decreases the number of function calls. It stores the result of each function call so that it can be used in future calls without the requirement for excess calls.

In the above dynamic algorithm, the outcomes obtained from every comparison between elements of X and the elements of Y are put away in a table with the goal that they can be used in future computations.

In this way, the time adopted by a dynamic strategy is the time taken to fill the table (ie. O(mn)). Though, the recursion algorithm has the complexity of 2^{max(m, n)}.

## Longest Common Subsequence Algorithm

X and Y be two given sequences Initialize a table LCS of dimension X.length * Y.length X.label = X Y.label = Y LCS[0][] = 0 LCS[][0] = 0 Start from LCS[1][1] Compare X[i] and Y[j] If X[i] = Y[j] LCS[i][j] = 1 + LCS[i-1, j-1] Point an arrow to LCS[i][j] Else LCS[i][j] = max(LCS[i-1][j], LCS[i][j-1]) Point an arrow to max(LCS[i-1][j], LCS[i][j-1])

## Python, Java, and C/C++ Examples

**Python**

# The longest common subsequence in Python # Function to find lcs_algo def lcs_algo(S1, S2, m, n): L = [[0 for x in range(n+1)] for x in range(m+1)] # Building the mtrix in bottom-up way for i in range(m+1): for j in range(n+1): if i == 0 or j == 0: L[i][j] = 0 elif S1[i-1] == S2[j-1]: L[i][j] = L[i-1][j-1] + 1 else: L[i][j] = max(L[i-1][j], L[i][j-1]) index = L[m][n] lcs_algo = [""] * (index+1) lcs_algo[index] = "" i = m j = n while i > 0 and j > 0: if S1[i-1] == S2[j-1]: lcs_algo[index-1] = S1[i-1] i -= 1 j -= 1 index -= 1 elif L[i-1][j] > L[i][j-1]: i -= 1 else: j -= 1 # Printing the sub sequences print("S1 : " + S1 + "\nS2 : " + S2) print("LCS: " + "".join(lcs_algo)) S1 = "ACADB" S2 = "CBDA" m = len(S1) n = len(S2) lcs_algo(S1, S2, m, n)

**Java**

// The longest common subsequence in Java class LCS_ALGO { static void lcs(String S1, String S2, int m, int n) { int[][] LCS_table = new int[m + 1][n + 1]; // Building the mtrix in bottom-up way for (int i = 0; i <= m; i++) { for (int j = 0; j <= n; j++) { if (i == 0 || j == 0) LCS_table[i][j] = 0; else if (S1.charAt(i - 1) == S2.charAt(j - 1)) LCS_table[i][j] = LCS_table[i - 1][j - 1] + 1; else LCS_table[i][j] = Math.max(LCS_table[i - 1][j], LCS_table[i][j - 1]); } } int index = LCS_table[m][n]; int temp = index; char[] lcs = new char[index + 1]; lcs[index] = '\0'; int i = m, j = n; while (i > 0 && j > 0) { if (S1.charAt(i - 1) == S2.charAt(j - 1)) { lcs[index - 1] = S1.charAt(i - 1); i--; j--; index--; } else if (LCS_table[i - 1][j] > LCS_table[i][j - 1]) i--; else j--; } // Printing the sub sequences System.out.print("S1 : " + S1 + "\nS2 : " + S2 + "\nLCS: "); for (int k = 0; k <= temp; k++) System.out.print(lcs[k]); System.out.println(""); } public static void main(String[] args) { String S1 = "ACADB"; String S2 = "CBDA"; int m = S1.length(); int n = S2.length(); lcs(S1, S2, m, n); } }

**C**

// The longest common subsequence in C #include <stdio.h> #include <string.h> int i, j, m, n, LCS_table[20][20]; char S1[20] = "ACADB", S2[20] = "CBDA", b[20][20]; void lcsAlgo() { m = strlen(S1); n = strlen(S2); // Filling 0's in the matrix for (i = 0; i <= m; i++) LCS_table[i][0] = 0; for (i = 0; i <= n; i++) LCS_table[0][i] = 0; // Building the mtrix in bottom-up way for (i = 1; i <= m; i++) for (j = 1; j <= n; j++) { if (S1[i - 1] == S2[j - 1]) { LCS_table[i][j] = LCS_table[i - 1][j - 1] + 1; } else if (LCS_table[i - 1][j] >= LCS_table[i][j - 1]) { LCS_table[i][j] = LCS_table[i - 1][j]; } else { LCS_table[i][j] = LCS_table[i][j - 1]; } } int index = LCS_table[m][n]; char lcsAlgo[index + 1]; lcsAlgo[index] = '\0'; int i = m, j = n; while (i > 0 && j > 0) { if (S1[i - 1] == S2[j - 1]) { lcsAlgo[index - 1] = S1[i - 1]; i--; j--; index--; } else if (LCS_table[i - 1][j] > LCS_table[i][j - 1]) i--; else j--; } // Printing the sub sequences printf("S1 : %s \nS2 : %s \n", S1, S2); printf("LCS: %s", lcsAlgo); } int main() { lcsAlgo(); printf("\n"); }

**C++**

// The longest common subsequence in C++ #include <iostream> using namespace std; void lcsAlgo(char *S1, char *S2, int m, int n) { int LCS_table[m + 1][n + 1]; // Building the mtrix in bottom-up way for (int i = 0; i <= m; i++) { for (int j = 0; j <= n; j++) { if (i == 0 || j == 0) LCS_table[i][j] = 0; else if (S1[i - 1] == S2[j - 1]) LCS_table[i][j] = LCS_table[i - 1][j - 1] + 1; else LCS_table[i][j] = max(LCS_table[i - 1][j], LCS_table[i][j - 1]); } } int index = LCS_table[m][n]; char lcsAlgo[index + 1]; lcsAlgo[index] = '\0'; int i = m, j = n; while (i > 0 && j > 0) { if (S1[i - 1] == S2[j - 1]) { lcsAlgo[index - 1] = S1[i - 1]; i--; j--; index--; } else if (LCS_table[i - 1][j] > LCS_table[i][j - 1]) i--; else j--; } // Printing the sub sequences cout << "S1 : " << S1 << "\nS2 : " << S2 << "\nLCS: " << lcsAlgo << "\n"; } int main() { char S1[] = "ACADB"; char S2[] = "CBDA"; int m = strlen(S1); int n = strlen(S2); lcsAlgo(S1, S2, m, n); }

## Longest Common Subsequence Applications

- in compressing genome resequencing data
- to authenticate users within their mobile phone through in-air signatures

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