Keep only the largest k values by maintaining a small min-heap.

Algorithm

@steps

  1. Store the heap in an array.
  2. Compare parent and child indexes instead of building explicit tree nodes.
  3. Swap only when the heap order is violated.
  4. Print the deterministic final heap state for replay comparison. @end @complexity
  • Time: O(n log k)
  • Space: O(k) @end
bounded heap For top-k largest values, a min-heap of size k keeps the current cutoff at the root.

C# DSA Implementation

basic.cs
using System;
using System.Collections.Generic;
using System.Linq;
class Program {
    static string ListString(IEnumerable<int> values) => "[" + string.Join(", ", values) + "]";
    static void HeapInsert(List<int> heap, int value) {
        heap.Add(value);
        int child = heap.Count - 1;
        while (child > 0) {
            int parent = (child - 1) / 2;
            if (heap[parent] <= heap[child]) break;
            (heap[parent], heap[child]) = (heap[child], heap[parent]);
            child = parent;
        }
    }
    static int HeapPop(List<int> heap) {
        int smallest = heap[0];
        heap[0] = heap[^1];
        heap.RemoveAt(heap.Count - 1);
        int parent = 0;
        while (true) {
            int left = parent * 2 + 1, right = left + 1;
            if (left >= heap.Count) break;
            int child = left;
            if (right < heap.Count && heap[right] < heap[left]) child = right;
            if (heap[parent] <= heap[child]) break;
            (heap[parent], heap[child]) = (heap[child], heap[parent]);
            parent = child;
        }
        return smallest;
    }
    static void Main() { var heap = new List<int>(); foreach (var value in new[] {5, 1, 9, 3, 7, 2}) { HeapInsert(heap, value); if (heap.Count > 3) HeapPop(heap); } heap.Sort((a, b) => b.CompareTo(a)); Console.WriteLine(ListString(heap)); }
}

@end @output [9, 7, 5] @end