Longest Common Subsequence using Recursion and Dynamic Programming Test

Given two strings, the goal is to find the length of the longest common subsequence that they share. A subsequence is a sequence that can be derived from another sequence by deleting some or no elements without changing the order of the remaining.

Available in

  • English

1 skills measured

  • Programming (Algorithm)

Test Type

Coding Test

Duration

40 Mins

Level

Advanced

Questions

1

Use of Longest Common Subsequence using Recursion and Dynamic Programming Test

Recursion is one of the common ways to solve the LCS problem. The idea is to break down the problem into smaller subproblems and solve them recursively. Specifically, we can consider the last characters of the two strings. There are three possible cases: ->The last characters of both strings match: In this case, the LCS length can be obtained by adding 1 to the LCS of the two strings with their last characters removed. ->The last characters of both strings do not match: In this case, we have two subproblems, finding the LCS of the first string with the second string with their last characters removed, and finding the LCS of the second string with the first string with their last characters removed. The LCS length is then the maximum of these two subproblems. ->One of the strings is empty: In this case, the LCS length is 0.

Skills measured

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Programming skills covered in Longest Common Subsequence using Recursion and Dynamic Programming involve creating algorithms to find the longest common subsequence between two strings. Recursion involves breaking down the problem into smaller subproblems and solving them recursively, while Dynamic Programming involves storing the results of subproblems to avoid redundant calculations. These skills are important as they improve the efficiency of the algorithm by reducing the time complexity, making it more suitable for solving real-world problems where performance is crucial. Additionally, understanding these concepts can help in developing more complex algorithms and problem-solving abilities.

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