Operations of Heap Data Structure
The Heap Data Structure supports fundamental operations that enable efficient management of its elements. Below are some operations of Heap Data Structure:
1. Heapify:
- It is the process to rearrange the elements to maintain the property of heap data structure.
- It takes O(log N) to balance the tree.
2. Insertion:
- If we insert a new element into the heap since we are adding a new element into the heap so it will distort the properties of the heap so we need to perform the heapify operation so that it maintains the property of the heap.
- This operation also takes O(logN) time.
3. getMax (For max-heap) or getMin (For min-heap):
- It finds the maximum element or minimum element for max-heap and min-heap respectively and as we know minimum and maximum elements will always be the root node itself for min-heap and max-heap respectively.
- It takes O(1) time.
4. removeMin or removeMax:
- This operation returns and deletes the maximum element and minimum element from the max-heap and min-heap respectively. In short, it deletes the root element of the heap binary tree.
- It takes O(1) time.
Heap Data Structure for Competitive Programming
Competitive programming needs smart tools to solve problems quickly. One key tool is the Heap Data Structure, which helps organize data in a way that’s super fast. In this article, we’ll break down the Heap, looking at its types, basic moves, and how it’s a big deal in competitive programming. We’ll focus on something called Priority Queue, a special use of Heap, and show you how to use it to solve problems better. It does not matter if you are new to Heap or if you have a little knowledge already, learning it will greatly enhance your problem-solving skills in competitive programming.
Table of Content
- Introduction to Heap Data Structure
- Types of Heap Data Structure
- Operations of Heap Data Structure
- What is Priority Queue?
- Priority Queue in C++ for Competitve Programming
- Priority Queue in Java for Competitve Programming
- Priority Queue in Python for Competitve Programming
- Problem Identification of Priority Queue
- Priority Queue Use Cases in Competitive Programming
- Practice Problems of Heap Data Structure for Competitive Programming