Practice Inductive Definitions, Recursive Functions and Efficient Evaluation - 43.1.1 | 43. Longest common subsequence - Part A | Data Structures and Algorithms in Python
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43.1.1 - Inductive Definitions, Recursive Functions and Efficient Evaluation

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

Define inductive definition in your own words.

πŸ’‘ Hint: Think about breaking something complex into simpler parts.

Question 2

Easy

What is a recursive function?

πŸ’‘ Hint: Recall the factorial function as an example.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

What is the primary goal of the Longest Common Subsequence problem?

  • To find the shortest subsequence
  • To determine the longest subsequence common to both words
  • To find completely different substrings

πŸ’‘ Hint: Think about what 'longest' means in terms of similarity.

Question 2

True or False: Dynamic programming can reduce the computation time for recursive functions.

  • True
  • False

πŸ’‘ Hint: Consider how storing values helps in calculation.

Solve and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Given the strings 'AGGTAB' and 'GXTXAYB', determine the length of their Longest Common Subsequence using dynamic programming.

πŸ’‘ Hint: Fill the DP table while tracking matches and mismatches.

Question 2

Explain how the inductive structure of a problem can lead to a recursive solution and give an example.

πŸ’‘ Hint: Reflect on how defining one case leads to the next smaller case.

Challenge and get performance evaluation