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CoursesSearch & Priority Patterns

Heap & Top-K

Lessons
9
Exercises
56
Minutes
55
  1. 1

    The Priority Queue

    What a heap actually guarantees — and why keeping only partial order makes it fast.

    Multiple choiceTap
    5 exercises
    4 min
  2. 2

    Kth Largest in a Stream

    Cap a min-heap at k elements — its top is always the kth largest, no matter how much streams past.

    Complete codeDebugMultiple choiceTrace
    6 exercises
    6 min
  3. 3

    Last Stone Weight

    Simulate with a max-heap: pop the two heaviest, push back the difference — never re-sort.

    DebugCode orderMultiple choiceTrace
    6 exercises
    5 min
  4. 4

    Kth Largest in an Array

    Keep only the k largest in a min-heap — pay log k per element, not log n.

    Complete codeMultiple choiceTrace
    6 exercises
    6 min
  5. 5

    Top K Frequent Elements

    Count first, then heap the pairs — and order the heap by COUNT, never by value.

    Complete codeMultiple choiceTrace
    6 exercises
    6 min
  6. 6

    K Closest Points to Origin

    Keep the k closest by evicting the farthest — a MAX-heap, the mirror image of top-K largest.

    Complete codeDebugMultiple choiceTrace
    6 exercises
    6 min
  7. 7

    Heap & Top-K CheckpointCheckpoint

    No new concepts — combine the island's heap moves: pick the right heap kind, run the size-k eviction, and catch the classic wrong-heap bug.

    DebugCode orderFill blankMultiple choiceTrace
    6 exercises
    6 min
  8. 8

    Heap & Top-K ReviewReview

    Mixed recap of the island: what a heap actually promises, size-k eviction, max-heap simulation, 'worst on top' comparators, and the cost of top-K.

    Complete codeDebugMultiple choiceTapTrace
    7 exercises
    6 min
  9. 9

    Boss: Median from a StreamBoss

    Two heaps split the stream down the middle — a max-heap holds the smaller half, a min-heap the larger — so both middle values wait at the tops.

    Complete codeDebugCode orderMultiple choiceTrace
    8 exercises
    10 min
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