How I Solved a Challenging Coding Problem

 Introduction:

As a programmer, encountering challenging coding problems is a part of the job. In this article, I will share my experience of how I solved a particularly challenging coding problem.

Background:

The coding problem was to create a program that could efficiently compute the longest common subsequence (LCS) between two given strings. An LCS is the longest string that is a subsequence of both input strings. For example, the LCS of "ABCDEF" and "ABDFG" is "ABD".

Solution:

At first, I thought this problem was relatively easy and tried to solve it using a brute-force approach. I generated all possible subsequences of both strings and compared them one by one to find the longest common subsequence. However, this approach was inefficient and could not handle large input strings.

Next, I started researching more efficient algorithms to solve this problem. I came across the dynamic programming approach, which involves breaking down the problem into smaller subproblems and solving them one by one.

I implemented the dynamic programming approach using a two-dimensional array to store the length of the LCS for each pair of prefixes of the input strings. By using the previously calculated values, I was able to efficiently compute the length of the LCS for the entire input strings.

Finally, I used the same array to backtrack and generate the LCS from the computed length. The resulting program was efficient and could handle large input strings without any issues.

Conclusion:

Solving this challenging coding problem taught me the importance of researching and implementing efficient algorithms. The dynamic programming approach was a game-changer in solving this problem, and I learned how to apply it to other similar problems. As a programmer, I now approach coding problems with more confidence and a broader range of problem-solving techniques.