Recognition signal
When should you think of Heaps?
You repeatedly need the smallest or largest active candidate while the candidate set changes.
17 worked problems
Heaps
Priority queues — always grab the smallest or largest fast. You repeatedly need the smallest or largest active candidate while the candidate set changes.
Core invariant
What must stay true?
The heap root is always the next globally valid choice among inserted candidates.
Practice in a deliberate order
Core
Recognize variations
Stretch
Combine ideas under pressure
What to learn in this cluster
- 01Top-k selection
- 02Merging sorted sources
- 03Scheduling, streaming, and shortest paths
All Heaps problems
17 of 17 walkthroughsNo matching problems. Try another title, tag, or difficulty.
- #506 Relative Ranksheap · sorting · arrayEasy
- #1046 Last Stone Weightheap · greedy · arrayEasy
- #215 Kth Largest Element in an Arrayheap · quickselect · arrayMedium
- #347 Top K Frequent Elementsheap · hash-table · bucket-sortMedium
- #355 Design Twitterdesign · heap · hash-tableMedium
- #373 Find K Pairs with Smallest Sumsarray · heap · best-first-searchMedium
- #378 Kth Smallest Element in a Sorted Matrixheap · binary-search · matrixMedium
- #451 Sort Characters By Frequencyheap · hash-map · sortingMedium
- #621 Task Schedulerheap · greedy · countingMedium
- #692 Top K Frequent Wordsheap · hash-map · sortingMedium
- #703 Kth Largest in Streamheap · design · streamMedium
- #767 Reorganize Stringheap · greedy · countingMedium
- #973 K Closest Points to Originheap · array · sortingMedium
- GFG Maximum Sum Combinationheap · arrayMedium
- GFG Implement Max Heapheap · designMedium
- #23 Merge K Sorted Listsheap · linked-list · divide-and-conquerHard
- #295 Find Median from Data Streamheap · design · two-heapsHard
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