# Construct Binary Tree from Preorder and Inorder Traversal
### Source
- leetcode: [Construct Binary Tree from Preorder and Inorder Traversal | LeetCode OJ](https://leetcode.com/problems/construct-binary-tree-from-preorder-and-inorder-traversal/)
- lintcode: [(73) Construct Binary Tree from Preorder and Inorder Traversal](http://www.lintcode.com/en/problem/construct-binary-tree-from-preorder-and-inorder-traversal/)
~~~
Given preorder and inorder traversal of a tree, construct the binary tree.
Example
Given in-order [1,2,3] and pre-order [2,1,3], return a tree:
2
/ \
1 3
Note
You may assume that duplicates do not exist in the tree.
~~~
### 题解
二叉树的重建,典型题。核心有两点:
1. preorder 先序遍历的第一个节点即为根节点。
1. 确定 inorder 数组中的根节点后其左子树和右子树也是 preorder 的左子树和右子树。
其中第二点是隐含条件,数组中没有重复元素,故可以根据先序遍历中第一个元素(根节点)得到根节点的值,然后在 inorder 中序遍历的数组中搜索得到根节点的索引值,即为左子树,右边为右子树。根据中序遍历中左子树的索引确定先序遍历数组中左子树的起止索引。递归直至处理完所有数组元素。
### Java
~~~
/**
* Definition of TreeNode:
* public class TreeNode {
* public int val;
* public TreeNode left, right;
* public TreeNode(int val) {
* this.val = val;
* this.left = this.right = null;
* }
* }
*/
public class Solution {
/**
*@param preorder : A list of integers that preorder traversal of a tree
*@param inorder : A list of integers that inorder traversal of a tree
*@return : Root of a tree
*/
public TreeNode buildTree(int[] preorder, int[] inorder) {
if (preorder == null || inorder == null) return null;
if (preorder.length == 0 || inorder.length == 0) return null;
if (preorder.length != inorder.length) return null;
TreeNode root = helper(preorder, 0, preorder.length - 1,
inorder, 0, inorder.length - 1);
return root;
}
private TreeNode helper(int[] preorder, int prestart, int preend,
int[] inorder, int instart, int inend) {
// corner cases
if (prestart > preend || instart > inend) return null;
// build root TreeNode
int root_val = preorder[prestart];
TreeNode root = new TreeNode(root_val);
// find index of root_val in inorder[]
int index = findIndex(inorder, instart, inend, root_val);
// build left subtree
root.left = helper(preorder, prestart + 1, prestart + index - instart,
inorder, instart, index - 1);
// build right subtree
root.right = helper(preorder, prestart + index - instart + 1, preend,
inorder, index + 1, inend);
return root;
}
private int findIndex(int[] nums, int start, int end, int target) {
for (int i = start; i <= end; i++) {
if (nums[i] == target) return i;
}
return -1;
}
}
~~~
### 源码分析
由于需要知道左右子树在数组中的索引,故需要引入辅助方法。找根节点这个大家都能很容易地想到,但是最关键的一步——找出左右子树的起止索引,这一点就不那么直接了,老实说想了很久忽略了这个突破点。
### 复杂度分析
`findIndex` 时间复杂度近似 O(n)O(n)O(n), `helper` 递归调用,每次调用都需要找中序遍历数组中的根节点,故总的时间复杂度为 O(n2)O(n^2)O(n2). 原地生成最终二叉树,空间复杂度为 O(1)O(1)O(1).
### Reference
- [Construct Binary Tree from Preorder and Inorder Traversal 参考程序 Java/C++/Python](http://www.jiuzhang.com/solutions/construct-binary-tree-from-preorder-and-inorder-traversal/)
- Preface
- Part I - Basics
- Basics Data Structure
- String
- Linked List
- Binary Tree
- Huffman Compression
- Queue
- Heap
- Stack
- Set
- Map
- Graph
- Basics Sorting
- Bubble Sort
- Selection Sort
- Insertion Sort
- Merge Sort
- Quick Sort
- Heap Sort
- Bucket Sort
- Counting Sort
- Radix Sort
- Basics Algorithm
- Divide and Conquer
- Binary Search
- Math
- Greatest Common Divisor
- Prime
- Knapsack
- Probability
- Shuffle
- Basics Misc
- Bit Manipulation
- Part II - Coding
- String
- strStr
- Two Strings Are Anagrams
- Compare Strings
- Anagrams
- Longest Common Substring
- Rotate String
- Reverse Words in a String
- Valid Palindrome
- Longest Palindromic Substring
- Space Replacement
- Wildcard Matching
- Length of Last Word
- Count and Say
- Integer Array
- Remove Element
- Zero Sum Subarray
- Subarray Sum K
- Subarray Sum Closest
- Recover Rotated Sorted Array
- Product of Array Exclude Itself
- Partition Array
- First Missing Positive
- 2 Sum
- 3 Sum
- 3 Sum Closest
- Remove Duplicates from Sorted Array
- Remove Duplicates from Sorted Array II
- Merge Sorted Array
- Merge Sorted Array II
- Median
- Partition Array by Odd and Even
- Kth Largest Element
- Binary Search
- Binary Search
- Search Insert Position
- Search for a Range
- First Bad Version
- Search a 2D Matrix
- Search a 2D Matrix II
- Find Peak Element
- Search in Rotated Sorted Array
- Search in Rotated Sorted Array II
- Find Minimum in Rotated Sorted Array
- Find Minimum in Rotated Sorted Array II
- Median of two Sorted Arrays
- Sqrt x
- Wood Cut
- Math and Bit Manipulation
- Single Number
- Single Number II
- Single Number III
- O1 Check Power of 2
- Convert Integer A to Integer B
- Factorial Trailing Zeroes
- Unique Binary Search Trees
- Update Bits
- Fast Power
- Hash Function
- Count 1 in Binary
- Fibonacci
- A plus B Problem
- Print Numbers by Recursion
- Majority Number
- Majority Number II
- Majority Number III
- Digit Counts
- Ugly Number
- Plus One
- Linked List
- Remove Duplicates from Sorted List
- Remove Duplicates from Sorted List II
- Remove Duplicates from Unsorted List
- Partition List
- Two Lists Sum
- Two Lists Sum Advanced
- Remove Nth Node From End of List
- Linked List Cycle
- Linked List Cycle II
- Reverse Linked List
- Reverse Linked List II
- Merge Two Sorted Lists
- Merge k Sorted Lists
- Reorder List
- Copy List with Random Pointer
- Sort List
- Insertion Sort List
- Check if a singly linked list is palindrome
- Delete Node in the Middle of Singly Linked List
- Rotate List
- Swap Nodes in Pairs
- Remove Linked List Elements
- Binary Tree
- Binary Tree Preorder Traversal
- Binary Tree Inorder Traversal
- Binary Tree Postorder Traversal
- Binary Tree Level Order Traversal
- Binary Tree Level Order Traversal II
- Maximum Depth of Binary Tree
- Balanced Binary Tree
- Binary Tree Maximum Path Sum
- Lowest Common Ancestor
- Invert Binary Tree
- Diameter of a Binary Tree
- Construct Binary Tree from Preorder and Inorder Traversal
- Construct Binary Tree from Inorder and Postorder Traversal
- Subtree
- Binary Tree Zigzag Level Order Traversal
- Binary Tree Serialization
- Binary Search Tree
- Insert Node in a Binary Search Tree
- Validate Binary Search Tree
- Search Range in Binary Search Tree
- Convert Sorted Array to Binary Search Tree
- Convert Sorted List to Binary Search Tree
- Binary Search Tree Iterator
- Exhaustive Search
- Subsets
- Unique Subsets
- Permutations
- Unique Permutations
- Next Permutation
- Previous Permuation
- Unique Binary Search Trees II
- Permutation Index
- Permutation Index II
- Permutation Sequence
- Palindrome Partitioning
- Combinations
- Combination Sum
- Combination Sum II
- Minimum Depth of Binary Tree
- Word Search
- Dynamic Programming
- Triangle
- Backpack
- Backpack II
- Minimum Path Sum
- Unique Paths
- Unique Paths II
- Climbing Stairs
- Jump Game
- Word Break
- Longest Increasing Subsequence
- Palindrome Partitioning II
- Longest Common Subsequence
- Edit Distance
- Jump Game II
- Best Time to Buy and Sell Stock
- Best Time to Buy and Sell Stock II
- Best Time to Buy and Sell Stock III
- Best Time to Buy and Sell Stock IV
- Distinct Subsequences
- Interleaving String
- Maximum Subarray
- Maximum Subarray II
- Longest Increasing Continuous subsequence
- Longest Increasing Continuous subsequence II
- Graph
- Find the Connected Component in the Undirected Graph
- Route Between Two Nodes in Graph
- Topological Sorting
- Word Ladder
- Bipartial Graph Part I
- Data Structure
- Implement Queue by Two Stacks
- Min Stack
- Sliding Window Maximum
- Longest Words
- Heapify
- Problem Misc
- Nuts and Bolts Problem
- String to Integer
- Insert Interval
- Merge Intervals
- Minimum Subarray
- Matrix Zigzag Traversal
- Valid Sudoku
- Add Binary
- Reverse Integer
- Gray Code
- Find the Missing Number
- Minimum Window Substring
- Continuous Subarray Sum
- Continuous Subarray Sum II
- Longest Consecutive Sequence
- Part III - Contest
- Google APAC
- APAC 2015 Round B
- Problem A. Password Attacker
- Microsoft
- Microsoft 2015 April
- Problem A. Magic Box
- Problem B. Professor Q's Software
- Problem C. Islands Travel
- Problem D. Recruitment
- Microsoft 2015 April 2
- Problem A. Lucky Substrings
- Problem B. Numeric Keypad
- Problem C. Spring Outing
- Microsoft 2015 September 2
- Problem A. Farthest Point
- Appendix I Interview and Resume
- Interview
- Resume