Print Longest Increasing Subsequence | LIS | Detailed Dry Run | Why It is Special |codestorywithMIK

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Published on May 16, 2025 12:29:26 AM ● Video Link: https://www.youtube.com/watch?v=rYuLUjRSTeo

Duration: 0:00

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Longest Increasing Subsequence | Recursion & Memo -    • Longest Increasing Subsequence | Recu...  
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Hi Everyone, this is the 111th video of our Playlist "Dynamic Programming : Popular Interview Problems".
Now we will be solving a famous and very good DP Problem - Print Longest Increasing Subsequence | LIS | Detailed Dry Run | Why It is Special | codestorywithMIK

I will explain it in full detail so that it becomes easy to understand. Each line will be explained and you will know the WHY behind everything.
We will deep dive so that the thought process will be cleared.

Problem Name : Print Longest Increasing Subsequence | LIS | Detailed Dry Run | Why It is Special | codestorywithMIK
Company Tags : Will update later
Code Github(C++ & JAVA) -


My DP Concepts Playlist :    • Roadmap for DP | How to Start DP ? | ...  
My Graph Concepts Playlist :    • Graph Concepts & Qns - 1 : Graph will...  
My Segment Tree Concepts Playlist :    • Segment Tree | Introduction | Basics ...  
My Recursion Concepts Playlist :    • Introduction | Recursion Concepts And...  
Trie Playlist -    • Word Search II (Google, Amazon, Meta,...  
Difference Array Technique: Concepts & Qns :    • Introduction | What | How | Differenc...  
My GitHub Repo for interview preparation : https://github.com/MAZHARMIK/Interview_DS_Algo
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Video Summary :
This bottom-up dynamic programming approach computes the Longest Increasing Subsequence (LIS) by iterating through all pairs of indices to build a dp array, where dp[i] stores the length of the LIS ending at index i. A prevIndex array is maintained to track the previous index in the LIS chain for reconstruction. After filling the arrays, we identify the index of the longest subsequence and trace back using prevIndex to recover the actual LIS. This approach has a time complexity of O(n^2) and is intuitive as it builds solutions for longer subsequences based on shorter ones.

✨ Timelines✨
00:00 - Introduction
0:19 - Motivation
0:41 - Problem Explanation
1:17 - Recap LIS
9:58 - Printing LIS + Complete Dry Run
26:05 - Story To Code
33:13 - Why LIS is a special Pattern

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