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Heap Applications

Practical applications of heap data structures for solving real-world problems.

Top K elements​

  • K Largest Elements: Find k largest elements in array
  • K Smallest Elements: Find k smallest elements in array
  • K Most Frequent: Find k most frequent elements
  • K Closest Points: Find k closest points to origin

Median in data stream​

  • Running Median: Find median in stream of numbers
  • Two Heaps Approach: Using min-heap and max-heap
  • Balanced Heaps: Maintaining equal size heaps
  • Dynamic Median: Updating median as new elements arrive

Merge K sorted lists​

  • Problem: Merge k sorted linked lists into one
  • Heap Approach: Using min-heap for efficient merging
  • Time Complexity: O(n log k) where n is total elements
  • Space Complexity: O(k) for heap storage

Task scheduling​

  • Priority Scheduling: Schedule tasks based on priority
  • CPU Scheduling: Round-robin with priority queues
  • Resource Allocation: Managing shared resources
  • Deadline Scheduling: Tasks with specific deadlines

Shortest path (Dijkstra's)​

  • Dijkstra's Algorithm: Find shortest path in weighted graph
  • Priority Queue: Using heap for efficient vertex selection
  • Time Complexity: O((V + E) log V) with binary heap
  • Applications: Network routing, GPS navigation