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| Assignment Type | Turnaround Time | Price (USD) |
|---|---|---|
| Introduction to Mini-Heaps and Dynamic Arrays | 24 hours | $30 |
| Implementation of Heapify Operation | 48 hours | $50 |
| Heap Insertion and Deletion Operations | 2 days | $60 |
| Heap Sorting Algorithms | 3 days | $80 |
| Priority Queue Implementation | 4 days | $90 |
| Memory Management in Dynamic Arrays | 5 days | $100 |
| Optimization Techniques for Mini-Heap Implementation | 7 days | $120 |
- Mini-heap Implementation Assignment Help Using Dynamic Array
- Implementing Heapify Function for Dynamic Array Heaps
- Why Opt for Our Help with Mini-Heap Implementation Assignment Using Dynamic Array?
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Mini-heap Implementation Assignment Help Using Dynamic Array
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Implementing Heapify Function for Dynamic Array Heaps
def heapify(arr, n, i):
largest = i # Initialize largest as root
left = 2 * i + 1 # left = 2*i + 1
right = 2 * i + 2 # right = 2*i + 2
# Check if left child exists and is greater than root
if left < n and arr[largest] < arr[left]:
largest = left
# Check if right child exists and is greater than largest so far
if right < n and arr[largest] < arr[right]:
largest = right
# Change root, if needed
if largest != i:
arr[i], arr[largest] = arr[largest], arr[i] # swap
# Heapify the root.
heapify(arr, n, largest)
# Example usage:
arr = [12, 11, 13, 5, 6, 7]
n = len(arr)
for i in range(n // 2 - 1, -1, -1):
heapify(arr, n, i)
Heapify function is crucial for maintaining the heap property in a binary heap, ensuring efficient insertion, deletion, and sorting operations. This function ensures that the subtree rooted at index i satisfies the heap property (either max-heap or min-heap). It recursively adjusts elements to maintain the heap structure.
Students studying Mini-Heap Implementation benefit from understanding heapify because it forms the core of operations like heap insertion, deletion, and sorting algorithms (such as heap sort).
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