36. Priority queues and heaps - Part B
Heaps serve as a tree-based implementation of priority queues, enabling efficient operations such as insert and delete max in logarithmic time. The chapter explores various functionalities of heaps, including building techniques and sorting methods. By utilizing a bottom-up approach, heaps can be constructed in linear time, and the chapter also distinguishes between max heaps and min heaps, highlighting their applications in sorting and data prioritization.
Sections
Navigate through the learning materials and practice exercises.
What we have learnt
- Heaps can implement priority queues efficiently with operations like insert and delete max taking O(log n) time.
- Building a heap can be done in linear time (O(n)) using a bottom-up technique.
- Max heaps and min heaps differ in their operational conditions, impacting their use in sorting and other algorithms.
Key Concepts
- -- Heap
- A binary tree-based data structure that satisfies the heap property where the parent node is either greater (max heap) or smaller (min heap) than its child nodes.
- -- Insert Operation
- An operation that adds a new node to the heap and ensures the heap property is maintained, which takes O(log n) time.
- -- Delete Max/Min
- Operations that remove the maximum value (in max heaps) or the minimum value (in min heaps) from the heap and restore the heap property.
- -- Heapify
- A process of rearranging the elements in an array to create a heap structure.
- -- Sorting with Heaps
- A sorting algorithm that utilizes heaps to extract elements in a particular order, typically resulting in O(n log n) time complexity.
Additional Learning Materials
Supplementary resources to enhance your learning experience.