Merge Sort and Binary Tree Postorder
Prerequisite Knowledge
Before reading this article, you should first learn:
Summary in One Sentence
The core idea of merge sort can be understood by combining Binary Tree Postorder Traversal: first, use recursion to sort the left and right halves of the subarray, then merge the two sorted arrays at the binary tree's postorder position.
You can open this visualization panel, click the full-screen button merge(arr, lo, mid, hi)
to visually observe the recursive process and sorting effect of merge sort:
Considering this is a basic knowledge section, I will only discuss the overall idea of merge sort. The detailed code implementation and its application will be covered in Detailed Explanation and Application of Merge Sort after the binary tree chapter. It is not recommended for beginners to view it now.
Since mastering the recursive thinking required for merge sort, along with using Two-pointer Technique to merge two sorted arrays, is essential, beginners are advised to follow the sequence of topics on this site. This will make understanding the code for merge sort easier.
Core Idea of Merge Sort
Although the opening summary may seem abstract, with the foundation laid in the previous chapter Core Idea of Quick Sort, you should have some intuition.
I will compare it with the thought process of quick sort, allowing you to clearly perceive their differences:
The idea of quick sort discussed earlier involves placing an element in its correct position (sorted), then using recursion to sort the remaining elements on both sides of this element, ultimately resulting in a sorted array. The code framework is as follows: