Leetcode: Serialize and Deserialize BST

Serialization is the process of converting a data structure or object into a sequence of bits so that it can be stored in a file or memory buffer, or transmitted across a network connection link to be reconstructed later in the same or another computer environment.

Design an algorithm to serialize and deserialize a binary search tree. There is no restriction on how your serialization/deserialization algorithm should work. You just need to ensure that a binary search tree can be serialized to a string and this string can be deserialized to the original tree structure.

The encoded string should be as compact as possible.

Note: Do not use class member/global/static variables to store states. Your serialize and deserialize algorithms should be stateless.

So the first question is: what is the difference between this and #297?

This here is BST, however, in #297, it's BT. "The encoded string should be as compact as possible" here. The special property of binary search trees compared to general binary trees allows a more compact encoding. So while the solutions to problem #297 still work here as well, they're not as good as they should be.

 

For general BT, to reconstruct the tree, we need the information of both in-order and pre-order, or in-order and post-order. We've practised that already. 

However, as a BST, just the information of pre-order or post-order is sufficient to rebuild the tree.

 

The encoded string is also most compact, since we do not need to keep tract of information of 'Null nodes'.

 

Example:   4

     2       6

         1   3   5    7

The pre-order encoding is: "4213657". It is easy to tell "4" is root, "213" is left tree, "657" is right tree. We can use a Queue to implement this, very convenient.

 1 /**
 2  * Definition for a binary tree node.
 3  * public class TreeNode {
 4  *     int val;
 5  *     TreeNode left;
 6  *     TreeNode right;
 7  *     TreeNode(int x) { val = x; }
 8  * }
 9  */
10 public class Codec {
11 
12     // Encodes a tree to a single string.
13     public String serialize(TreeNode root) {
14         if (root == null) return "";
15         StringBuilder res = new StringBuilder();
16         Stack<TreeNode> stack = new Stack<TreeNode>();
17         TreeNode node = root;
18         while (!stack.isEmpty() || node!=null) {
19             if (node != null) {
20                 res.append(node.val).append(" ");
21                 stack.push(node);
22                 node = node.left;
23             }
24             else {
25                 node = stack.pop().right;
26             }
27         }
28         return res.toString().trim();
29     }
30 
31     // Decodes your encoded data to tree.
32     public TreeNode deserialize(String data) {
33         if (data==null || data.length()==0) return null;
34         String[] nodes = data.split(" ");
35         Queue<Integer> queue = new LinkedList<>();
36         for (String node : nodes) {
37             queue.offer(Integer.valueOf(node));
38         }
39         return buildTree(queue);
40     }
41     
42     public TreeNode buildTree(Queue<Integer> queue) {
43         if (queue.isEmpty()) return null;
44         TreeNode root = new TreeNode(queue.poll());
45         Queue<Integer> leftNodeVals = new LinkedList<>();
46         while (!queue.isEmpty() && queue.peek()<=root.val) {
47             leftNodeVals.offer(queue.poll());
48         }
49         root.left = buildTree(leftNodeVals);
50         root.right = buildTree(queue);
51         return root;
52     }
53 }
54 
55 // Your Codec object will be instantiated and called as such:
56 // Codec codec = new Codec();
57 // codec.deserialize(codec.serialize(root));

 

内容概要:本文系统介绍了算术优化算法(AOA)的基本原理、核心思想及Python实现方法,并通过图像分割的实际案例展示了其应用价值。AOA是一种基于种群的元启发式算法,其核心思想来源于四则运算,利用乘除运算进行全局勘探,加减运算进行局部开发,通过数学优化器加速函数(MOA)和数学优化概率(MOP)动态控制搜索过程,在全局探索与局部开发之间实现平衡。文章详细解析了算法的初始化、勘探与开发阶段的更新策略,并提供了完整的Python代码实现,结合Rastrigin函数进行测试验证。进一步地,以Flask框架搭建前后端分离系统,将AOA应用于图像分割任务,展示了其在实际工程中的可行性与高效性。最后,通过收敛速度、寻优精度等指标评估算法性能,并提出自适应参数调整、模型优化和并行计算等改进策略。; 适合人群:具备一定Python编程基础和优化算法基础知识的高校学生、科研人员及工程技术人员,尤其适合从事人工智能、图像处理、智能优化等领域的从业者;; 使用场景及目标:①理解元启发式算法的设计思想与实现机制;②掌握AOA在函数优化、图像分割等实际问题中的建模与求解方法;③学习如何将优化算法集成到Web系统中实现工程化应用;④为算法性能评估与改进提供实践参考; 阅读建议:建议读者结合代码逐行调试,深入理解算法流程中MOA与MOP的作用机制,尝试在不同测试函数上运行算法以观察性能差异,并可进一步扩展图像分割模块,引入更复杂的预处理或后处理技术以提升分割效果。
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