Internal Implementation of Max-Heap Data Structure
A Min heap is typically represented as an array.
- The root element will be at Arr[0].
- For any ith node Arr[i].
- left child is stored at index 2i+1
- Right child is stored at index 2i+2
- Parent is stored at index floor((i-1)/2)
The Internal Implementation of the Max-Heap require 3 major steps:
- Insertion: To insert a new element into the heap, it is added to the end of the array and then “bubbled up” until it satisfies the heap property.
- Deletion: To delete the maximum element (the root of the heap), the last element in the array is swapped with the root, and the new root is “bubbled down” until it satisfies the heap property.
- Heapify: A heapify operation can be used to create a max heap from an unsorted array.
Introduction to Max-Heap – Data Structure and Algorithm Tutorials
A Max-Heap is defined as a type of Heap Data Structure in which each internal node is greater than or equal to its children.
The heap data structure is a type of binary tree that is commonly used in computer science for various purposes, including sorting, searching, and organizing data.
Purpose and Use Cases of Max-Heap:
- Priority Queue: One of the primary uses of the heap data structure is for implementing priority queues.
- Heap Sort: The heap data structure is also used in sorting algorithms.
- Memory Management: The heap data structure is also used in memory management. When a program needs to allocate memory dynamically, it uses the heap data structure to keep track of the available memory.
- Graph Algorithms: The heap data structure is used in various graph algorithms. For example, Dijkstra’s shortest path algorithm uses a heap data structure to keep track of the vertices with the shortest path from the source vertex.
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