Longest Common Subsequence | LCS Dynamic Programming Tutorial | Dynamic Programming | Intellipaat
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In this video on the Longest Common Subsequence, you will learn about what a subsequence is, what the longest common subsequence is, and how these two terminologies differ? This topic is among the most asked interview questions you may face while appearing for a CS/IT role. In this video, you will get to learn about various approaches to solving this problem, including recursion utilizing a top-down approach and dynamic programming employing a bottom-up approach.
Through this video, you will understand which approach to solve this problem is better through a hands-on, practical implementation of both ways. This video discusses the time complexity of each of the algorithms and explains the working of those algorithms in detail using animations and program codes so that you understand the concept better.
#LongestCommonSubsequence #LCSDynamicProgrammingTutorial #DynamicProgramming #DynamicProgramming #Intellipaat
🔵 Following topics are covered in this session:
00:00 - Introduction
01:17 - What is a Subsequences?
02:08 - What is the Longest Common Subsequence or LCS?
02:38 - Approaches to Find LCS
17:09 - Programming Implementation
18:05 - Application of LCS
The Longest Common Subsequence (LCS) is defined as the longest subsequence that is shared by all of the given sequences, assuming that the subsequence components do not have to occupy consecutive locations within the original sequences.
Recursion is the technique of repeating objects in a self-similar manner. If a program permits you to call a function inside another function, this is known as a recursive call of the function.
Dynamic Programming is mostly an improvement over simple recursion. We can use Dynamic Programming to optimize any recursive solution that involves repeated calls for the same inputs. The goal is to simply save the results of subproblems so that we don't have to recalculate them later.
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