# Merge Sort and Binary Tree Postorder

Prerequisite Knowledge

Before reading this article, you should first learn:

Summary in One Sentence

The core concept of merge sort requires understanding in conjunction with Binary Tree Postorder Traversal: first use recursion to sort the left and right subarrays, then merge the two sorted arrays at the postorder position of the binary tree.

You can open this visualization panel, click the fullscreen button `merge(arr, lo, mid, hi)`

to intuitively see the recursive process and sorting effect of merge sort:

**Considering that this is a basic knowledge section, I will only discuss the overall concept of merge sort**. The specific code implementation and application of the algorithm will be arranged in the Detailed Explanation and Application of Merge Sort after the binary tree chapter. It is not recommended for beginners to look at it now.

Since the merge sort algorithm requires a good grasp of recursive thinking and uses the Two-Pointer Technique to merge two sorted arrays, it is advised for beginners to study according to the site's directory sequence. This way, understanding the code for merge sort will be easier.

## Core Concept of Merge Sort

Although the initial summary might seem abstract, with the foundation laid in the previous chapter Core Concept of Quick Sort, you should have some insight.

I will compare it with the quick sort approach, so you can clearly feel the difference between the two:

The idea of quick sort discussed earlier is to place an element in its correct position (sorted), then recursively sort the remaining elements on either side of this element, ultimately resulting in the entire array being sorted. The code framework is as follows: