Day 56/90 |⚡🚀 Merge K Sorted Lists Explained 🔥 Brute Force vs Min Heap vs Pairwise 🔥 | K-Way Merge

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🚀 Merge K Sorted Lists | Brute Force vs Min Heap vs Pairwise Merge (LeetCode Explained)

In this video, we break down the classic Merge K Sorted Lists LeetCode problem step-by-step and explore three different approaches to solve it efficiently.

You’ll learn:
✅ Problem intuition and real interview perspective
✅ Brute Force approach and why it’s not optimal
✅ Min Heap (Priority Queue) solution with full walkthrough
✅ Pairwise merging technique inspired by merge sort
✅ Time & Space complexity comparison
✅ When to use which approach in interviews

This problem is a must-know pattern for mastering K-way merge, heaps, and divide-and-conquer — concepts that frequently appear in coding interviews at top tech companies.

Whether you’re preparing for DSA interviews, learning heap patterns, or strengthening your problem-solving skills, this video will give you a clear and practical understanding.

📌 Topics Covered
• K-way Merge Pattern
• Priority Queue / Min Heap
• Divide and Conquer
• Linked List Manipulation
• Complexity Analysis

💡 If you found this helpful, don’t forget to like, share, and subscribe for more deep-dive DSA explanations!

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CHAPTERS
00:00 - Introduction & Recap
02:25 - LeetCode - 23 - Merge k Sorted Lists - Approach 1 - Theory
07:00 - LeetCode - 23 - Merge k Sorted Lists - Approach 1 - Code
12:21 - LeetCode - 23 - Merge k Sorted Lists - Approach 2 - Theory
18:18 - LeetCode - 23 - Merge k Sorted Lists - Approach 2 - Code
28:15 - LeetCode - 23 - Merge k Sorted Lists - Approach 3 - Theory
31:33 - LeetCode - 23 - Merge k Sorted Lists - Approach 3 - Code
41:33 - Conclusion

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Видео Day 56/90 |⚡🚀 Merge K Sorted Lists Explained 🔥 Brute Force vs Min Heap vs Pairwise 🔥 | K-Way Merge канала CTO Bhaiya