Heap Topics

Master essential heap problems and patterns frequently asked in interviews.

•   01 Kth Largest / Smallest Element Find Kth largest or smallest using min/max heap
•   02 Merge K Sorted Arrays / Lists Use min-heap to efficiently merge multiple sorted lists
•   03 Heap Sort Classic sorting algorithm using max-heap
•   04 Top K Frequent Elements Use heap to track most frequent elements efficiently
•   05 Sliding Window Median Maintain two heaps to find median in a sliding window

WONDERING, HOW DOES IT COVER PATTERNS?
These problems cover the most important heap patterns for interviews:

Kth Largest / Smallest Element → Use min/max heap to maintain top k elements efficiently.
Merge K Sorted Arrays / Lists → Min-heap to merge multiple streams in O(n log k).
Heap Sort → Build a heap and repeatedly extract max/min for sorting.
Top K Frequent Elements → Use heap to track k most frequent elements.
Sliding Window Median → Two-heap approach to maintain median dynamically in a window.

Together, they ensure you understand min/max heaps, top k elements, merge patterns, heap-based sorting, and dynamic median tracking in heap problems.