4. Longest Common Subsequence
This chapter explores the concept of the longest common subsequence (LCS) and its significance in fields like bioinformatics and text comparison. It details the algorithmic approach to finding LCS, comparing it to the longest common subword problem, and discusses the computational efficiency using dynamic programming. The application of LCS in real-world scenarios, such as genetic sequencing and text file comparison, highlights its relevance.
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What we have learnt
- The longest common subsequence allows for matches while allowing some letters to be dropped, resulting in potentially longer sequences.
- The LCS problem can be applied in various fields such as biology for genetic comparison and computer science for text differences.
- Dynamic programming and memoization are essential techniques used to efficiently solve the LCS problem.
Key Concepts
- -- Longest Common Subsequence (LCS)
- A problem that identifies the longest subsequence common to two sequences, allowing for some discrepancies or dropped letters.
- -- Dynamic Programming
- An algorithmic technique for solving optimization problems by breaking them down into simpler subproblems and storing their solutions.
- -- Memoization
- An optimization technique used to speed up algorithms by storing previously computed results in order to avoid redundant calculations.
- -- Subsequence
- A sequence derived from another sequence where some elements may be omitted without rearranging the remaining elements.
Additional Learning Materials
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