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) - https://www.youtube.com/watch?v=aJNu_DLyOxY
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) :
GfG Problem Link : https://www.geeksforgeeks.org/problems/shortest-common-supersequence0322/1
My DP Concepts Playlist : https://youtu.be/7eLMOE1jnls
My Graph Concepts Playlist : https://youtu.be/5JGiZnr6B5w
My Recursion Concepts Playlist : https://www.youtube.com/watch?v=pfb1Zduesi8&list=PLpIkg8OmuX-IBcXsfITH5ql0Lqci1MYPM
My GitHub Repo for interview preparation : https://github.com/MAZHARMIK/Interview_DS_Algo
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Approach Summary :
**Approach-1 (Recursion + Memoization):**
- This approach utilizes recursion with memoization to find the length of the shortest common supersequence of two strings.
- Time Complexity (T.C): O(m * n), where m and n are the lengths of the input strings.
- Space Complexity (S.C): O(m * n) for the memoization table.
- It stores intermediate results in a memoization table to avoid redundant calculations during recursion.
**Approach-2 (Bottom Up):**
- This approach uses a bottom-up dynamic programming approach to find the length of the shortest common supersequence.
- It iterates through the strings and fills a 2D table to store the lengths of the shortest common supersequence.
- Time Complexity (T.C): O(m * n), where m and n are the lengths of the input strings.
- Space Complexity (S.C): O(m * n) for the dynamic programming table.
**Approach-3 (Using LCS Code):**
- This approach repurposes the Longest Common Subsequence (LCS) algorithm to find the length of the shortest common supersequence.
- It computes the LCS of the input strings and then calculates the length of the shortest common supersequence based on the LCS.
- Time Complexity (T.C): O(m * n), where m and n are the lengths of the input strings.
- Space Complexity (S.C): O(m * n) for the dynamic programming table used to compute LCS.
- After finding the LCS, it calculates the number of letters that are not part of LCS in each string and adds them to the LCS length to obtain the length of the shortest common supersequence.
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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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