Longest Common Subsequence Using Dynamic Programming | LCS Tutorial | Dynamic Programming

Published: 01 January 1970
on channel: SCALER

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We at Scaler present a detailed tutorial on longest common subsequence using dynamic programming. In this video, Vamsi will walk you through this Dynamic Programming tutorial.
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What is dynamic programming?
Dynamic programming is both a mathematical optimisation method and a computer programming method. The method was developed by Richard Bellman in the 1950s and has found applications in numerous fields, from aerospace engineering to economics. In both contexts, it refers to simplifying a complicated problem by breaking it down into simpler sub-problems in a recursive manner. While some decision problems cannot be taken apart this way, decisions that span several points in time do often break apart recursively. Likewise, in computer science, if a problem can be solved optimally by breaking it into sub-problems and then recursively finding the optimal solutions to the sub-problems, then it is said to have an optimal substructure.

What is the Longest Common Subsequence?
The longest common subsequence (LCS) is defined as the longest subsequence that is common to all the given sequences, provided that the elements of the subsequence are not required to occupy consecutive positions within the original sequences.

The following explanation will help you learn how to use dynamic programming to find the longest common subsequence. It will also help you understand why you need to find the longest common subsequence with the help of relevant examples.

The following topics are covered in this Dynamic Programming tutorial:
0:00 - Introduction
0:05 - Longest common subsequence using dynamic programming
0:50 - Problem statement
6:10 - Recursion solution
26:28 - Recursive core
29:00 - Code solution

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