Скачать или смотреть Shortest Common Supersequence | 3 Approaches | DP On Strings |DP Concepts & Qns 20|codestorywithMIK

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This is the 20th Video of our Playlist "DP Concepts & Qns".

This is the second video of the "DP On Strings" series in this playlist.
In this video we will try to solve a very good and famous DP problem : Shortest Common Supersequence
Longest Common Subsequence (LCS Video) -    • Longest Common Subsequence | Recursion | M...  

I will explain the intuition so easily that you will never forget and start seeing this as cakewalk EASYYY.
We will do live coding after explanation and see if we are able to pass all the test cases.
Also, please note that my Github solution link below contains both C++ as well as JAVA code.

Problem Name : Shortest Common Supersequence | 3 Approaches | DP On Strings | DP Concepts & Qns-20
Company Tags : Microsoft
My solutions on Github(C++ & JAVA) : https://github.com/MAZHARMIK/Intervie...
GfG Problem Link : https://www.geeksforgeeks.org/problem...


My DP Concepts Playlist :    • Roadmap for DP | How to Start DP ? | Topic...  
My Graph Concepts Playlist :    • Graph Concepts & Qns - 1 : Graph will no m...  
My Segment Tree Concepts Playlist :    • Segment Tree | Introduction | Basics | Bui...  
My Recursion Concepts Playlist :    • Introduction | Recursion Concepts And Ques...  
Trie Playlist -    • Word Search II | Leetcode 212 | Detailed |...  
Difference Array Technique: Concepts & Qns :    • Introduction | What | How | Difference Arr...  
My GitHub Repo for interview preparation : https://github.com/MAZHARMIK/Intervie...
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Approach Summary :
Approach-1 (Recursion + Memoization)
This approach uses recursion with memoization to avoid redundant calculations. The idea is to either include a matching character from both strings or take the minimum length by adding one character from either string. The memoization table helps store results to optimize repeated subproblems.

Approach-2 (Bottom-Up Dynamic Programming)
This approach builds a DP table iteratively. The base case considers the scenario when one string is empty, requiring all characters from the other. The table is filled by either taking a common character or choosing the minimum step to extend the supersequence. This avoids recursion overhead and ensures optimal computation.

Approach-3 (Using LCS Code)
This approach relies on computing the Longest Common Subsequence (LCS). The shortest common supersequence includes the LCS only once and appends the remaining characters from both strings. The formula is derived as LCS + (m - LCS) + (n - LCS), ensuring minimum length with maximum overlap.


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✨ Timelines✨
00:00 - Introduction
04:51 - Intuition & Thought Process (Recursion + Memo)
12:46 - Tree Diagram
23:25 - Story To Code
32:01 - Coding Approach-1 (Recursion + Memo)
35:39 - Approach-2 Bottom Up
42:01 - Coding Approach-2 (Bottom Up)
44:21 - Approach-3 (Using LCS)
51:34 - Coding Approach-3 (Using LCS)

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