Longest Common Subsequence - Solution & Explanation

This approach uses dynamic programming to solve the problem by creating a 2D table to store the lengths of the longest common subsequences for different substrings. Each cell dp[i][j] in the table represents the longest common subsequence length of substrings text1[0..i-1] and text2[0..j-1]. The table is filled using the following rules:

• If the characters text1[i-1] and text2[j-1] are equal, then dp[i][j] = dp[i-1][j-1] + 1.
• If they are not equal, then dp[i][j] = max(dp[i-1][j], dp[i][j-1]).

The final answer is dp[text1.length][text2.length].

This C solution defines a function longestCommonSubsequence that calculates the longest common subsequence length using a 2D array dp. The program iteratively updates the table based on character matches and topological decision making (max operation). The main function demonstrates this by testing with two example strings and printing the result.

This optimization reduces space complexity by only storing data for the current and the previous row. The idea remains the same - calculating the LCS length incrementally using a dynamic programming strategy. However, instead of a full 2D table, only two 1D arrays are used, effectively reducing space usage from O(n*m) to O(min(n, m)). This is achieved by noting that each row of the DP table depends only on the previous row. So, we use two arrays that swap every iteration.

The optimized C solution uses two arrays previous and current to store the LCS length for the current and last iteration. After computing each row, the current becomes the previous, thereby saving space. The solution is efficient both in terms of time and space compared to the full 2D table.