Difference between Max and Min Heap
Min Heap | Max Heap | |
---|---|---|
1. | In a Min-Heap the key present at the root node must be less than or equal to among the keys present at all of its children. | In a Max-Heap the key present at the root node must be greater than or equal to among the keys present at all of its children. |
2. | In a Min-Heap the minimum key element present at the root. | In a Max-Heap the maximum key element present at the root. |
3. | A Min-Heap uses the ascending priority. | A Max-Heap uses the descending priority. |
4. | In the construction of a Min-Heap, the smallest element has priority. | In the construction of a Max-Heap, the largest element has priority. |
5. | In a Min-Heap, the smallest element is the first to be popped from the heap. | In a Max-Heap, the largest element is the first to be popped from the heap. |
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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