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			268 lines
		
	
	
		
			6.5 KiB
		
	
	
	
		
			Markdown
		
	
	
		
			Executable File
		
	
	
	
	
# 二叉树遍历
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从物理结构的角度来看,树是一种基于链表的数据结构,因此其遍历方式是通过指针逐个访问节点。然而,树是一种非线性数据结构,这使得遍历树比遍历链表更加复杂,需要借助搜索算法来实现。
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二叉树常见的遍历方式包括层序遍历、前序遍历、中序遍历和后序遍历等。
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## 层序遍历
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「层序遍历 Level-Order Traversal」从顶部到底部逐层遍历二叉树,并在每一层按照从左到右的顺序访问节点。
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层序遍历本质上属于「广度优先搜索 Breadth-First Traversal」,它体现了一种“一圈一圈向外扩展”的逐层搜索方式。
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广度优先遍历通常借助「队列」来实现。队列遵循“先进先出”的规则,而广度优先遍历则遵循“逐层推进”的规则,两者背后的思想是一致的。
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=== "Java"
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    ```java title="binary_tree_bfs.java"
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    [class]{binary_tree_bfs}-[func]{levelOrder}
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    ```
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=== "C++"
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    ```cpp title="binary_tree_bfs.cpp"
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    [class]{}-[func]{levelOrder}
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    ```
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=== "Python"
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    ```python title="binary_tree_bfs.py"
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    [class]{}-[func]{level_order}
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    ```
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=== "Go"
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    ```go title="binary_tree_bfs.go"
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    [class]{}-[func]{levelOrder}
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    ```
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=== "JS"
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    ```javascript title="binary_tree_bfs.js"
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    [class]{}-[func]{levelOrder}
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    ```
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=== "TS"
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    ```typescript title="binary_tree_bfs.ts"
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    [class]{}-[func]{levelOrder}
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    ```
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=== "C"
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    ```c title="binary_tree_bfs.c"
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    [class]{}-[func]{levelOrder}
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    ```
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=== "C#"
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    ```csharp title="binary_tree_bfs.cs"
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    [class]{binary_tree_bfs}-[func]{levelOrder}
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    ```
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=== "Swift"
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    ```swift title="binary_tree_bfs.swift"
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    [class]{}-[func]{levelOrder}
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    ```
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=== "Zig"
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    ```zig title="binary_tree_bfs.zig"
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    [class]{}-[func]{levelOrder}
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    ```
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=== "Dart"
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    ```dart title="binary_tree_bfs.dart"
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    [class]{}-[func]{levelOrder}
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    ```
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=== "Rust"
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    ```rust title="binary_tree_bfs.rs"
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    [class]{}-[func]{level_order}
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    ```
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**时间复杂度**:所有节点被访问一次,使用 $O(n)$ 时间,其中 $n$ 为节点数量。
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**空间复杂度**:在最差情况下,即满二叉树时,遍历到最底层之前,队列中最多同时存在 $\frac{n + 1}{2}$ 个节点,占用 $O(n)$ 空间。
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## 前序、中序、后序遍历
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相应地,前序、中序和后序遍历都属于「深度优先遍历 Depth-First Traversal」,它体现了一种“先走到尽头,再回溯继续”的遍历方式。
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如下图所示,左侧是深度优先遍历的示意图,右上方是对应的递归代码。深度优先遍历就像是绕着整个二叉树的外围“走”一圈,在这个过程中,在每个节点都会遇到三个位置,分别对应前序遍历、中序遍历和后序遍历。
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以下给出了实现代码,请配合上图理解深度优先遍历的递归过程。
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=== "Java"
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    ```java title="binary_tree_dfs.java"
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    [class]{binary_tree_dfs}-[func]{preOrder}
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    [class]{binary_tree_dfs}-[func]{inOrder}
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    [class]{binary_tree_dfs}-[func]{postOrder}
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    ```
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=== "C++"
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    ```cpp title="binary_tree_dfs.cpp"
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    [class]{}-[func]{preOrder}
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    [class]{}-[func]{inOrder}
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    [class]{}-[func]{postOrder}
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    ```
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=== "Python"
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    ```python title="binary_tree_dfs.py"
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    [class]{}-[func]{pre_order}
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    [class]{}-[func]{in_order}
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    [class]{}-[func]{post_order}
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    ```
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=== "Go"
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    ```go title="binary_tree_dfs.go"
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    [class]{}-[func]{preOrder}
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    [class]{}-[func]{inOrder}
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    [class]{}-[func]{postOrder}
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    ```
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=== "JS"
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    ```javascript title="binary_tree_dfs.js"
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    [class]{}-[func]{preOrder}
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    [class]{}-[func]{inOrder}
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    [class]{}-[func]{postOrder}
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    ```
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=== "TS"
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    ```typescript title="binary_tree_dfs.ts"
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    [class]{}-[func]{preOrder}
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    [class]{}-[func]{inOrder}
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    [class]{}-[func]{postOrder}
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    ```
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=== "C"
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    ```c title="binary_tree_dfs.c"
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    [class]{}-[func]{preOrder}
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    [class]{}-[func]{inOrder}
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    [class]{}-[func]{postOrder}
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    ```
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=== "C#"
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    ```csharp title="binary_tree_dfs.cs"
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    [class]{binary_tree_dfs}-[func]{preOrder}
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    [class]{binary_tree_dfs}-[func]{inOrder}
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    [class]{binary_tree_dfs}-[func]{postOrder}
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    ```
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=== "Swift"
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    ```swift title="binary_tree_dfs.swift"
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    [class]{}-[func]{preOrder}
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    [class]{}-[func]{inOrder}
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    [class]{}-[func]{postOrder}
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    ```
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=== "Zig"
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    ```zig title="binary_tree_dfs.zig"
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    [class]{}-[func]{preOrder}
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    [class]{}-[func]{inOrder}
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    [class]{}-[func]{postOrder}
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    ```
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=== "Dart"
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    ```dart title="binary_tree_dfs.dart"
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    [class]{}-[func]{preOrder}
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    [class]{}-[func]{inOrder}
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    [class]{}-[func]{postOrder}
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    ```
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=== "Rust"
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    ```rust title="binary_tree_dfs.rs"
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    [class]{}-[func]{pre_order}
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    [class]{}-[func]{in_order}
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    [class]{}-[func]{post_order}
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    ```
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**时间复杂度**:所有节点被访问一次,使用 $O(n)$ 时间,其中 $n$ 为节点数量。
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**空间复杂度**:在最差情况下,即树退化为链表时,递归深度达到 $n$ ,系统占用 $O(n)$ 栈帧空间。
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!!! note
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    我们也可以不使用递归,仅基于迭代实现前、中、后序遍历,有兴趣的同学可以自行研究。
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下图展示了前序遍历二叉树的递归过程,其可分为“递”和“归”两个逆向的部分:
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1. “递”表示开启新方法,程序在此过程中访问下一个节点。
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2. “归”表示函数返回,代表当前节点已经访问完毕。
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=== "<1>"
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=== "<2>"
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=== "<3>"
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=== "<4>"
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=== "<5>"
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=== "<6>"
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=== "<7>"
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=== "<8>"
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=== "<9>"
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=== "<10>"
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=== "<11>"
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