Given two sequences,the task is to find the length of longest sub-sequence present in both of them. A sub-sequence is a sequence that appears in the same relative order, but not necessarily contiguous.For example- “abc”,”fit”,”abfi”,etc are sub-sequences of “abcfit”.A string of length n has 2^n different possible sub-sequences.

**Example-
**

String 1- “BATTING” Length-7

String 2- “BOWLING” Length-7

Longest Common Sub-sequence- “BING” Length-4

**Brute Force-**

Generate all sub-sequences of both given sequences and find the longest matching sub-sequence.It has an exponential time complexity.

**Efficient Algorithm-**

lcs(i,j)=

**if (X[i]==Y[j])**

1+lcs(i-1,j-1)

**else**

max(lcs(i-1,j),lcs(i,j-1))

where X[],Y[] are strings.

**Recursive Solution-**

//char X[0,1...m-1],Y[0,1,...n-1] contains the strings //lcs(m,n) is called initially. int lcs(int a,int b) { if (a == 0 || b == 0) return 0; if (X[a-1] == Y[b-1]) return 1 + lcs(a-1,b-1); else return max(lcs(a,b-1), lcs(a-1,b)); }

This problem has overlapping as well as optimal substructure property.Thus,dynamic programming can be applied.A temporary array L[ ][ ] is constructed to store the intermediate results.

**DP solution-**

int lcs(int m,int n) { int L[m+1][n+1]; int i,j; // L[i][j] contains length of LCS of X[0..i-1] and Y[0..j-1] for (i=0; i<=m; i++) { for (j=0; j<=n; j++) { if (i == 0 || j == 0) L[i][j] = 0; else if (X[i-1] == Y[j-1]) L[i][j] = L[i-1][j-1] + 1; else L[i][j] = max(L[i-1][j], L[i][j-1]); } } //L[m][n] contains length of LCS for X[0..n-1] and Y[0..m-1] return L[m][n]; }

This program has a time complexity of **O(m*n)** where m,n are lengths of the string which is much better than exponential time complexity.