-
Preface
- FAQ
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Part I - Basics
- Basics Data Structure
- Basics Sorting
- Basics Algorithm
- Basics Misc
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Part II - Coding
- String
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Integer Array
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Remove Element
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Zero Sum Subarray
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Subarray Sum K
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Subarray Sum Closest
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Recover Rotated Sorted Array
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Product of Array Exclude Itself
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Partition Array
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First Missing Positive
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2 Sum
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3 Sum
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3 Sum Closest
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Remove Duplicates from Sorted Array
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Remove Duplicates from Sorted Array II
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Merge Sorted Array
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Merge Sorted Array II
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Median
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Partition Array by Odd and Even
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Kth Largest Element
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Remove Element
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Binary Search
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First Position of Target
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Search Insert Position
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Search for a Range
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First Bad Version
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Search a 2D Matrix
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Search a 2D Matrix II
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Find Peak Element
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Search in Rotated Sorted Array
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Search in Rotated Sorted Array II
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Find Minimum in Rotated Sorted Array
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Find Minimum in Rotated Sorted Array II
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Median of two Sorted Arrays
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Sqrt x
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Wood Cut
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First Position of Target
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Math and Bit Manipulation
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Single Number
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Single Number II
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Single Number III
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O1 Check Power of 2
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Convert Integer A to Integer B
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Factorial Trailing Zeroes
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Unique Binary Search Trees
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Update Bits
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Fast Power
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Hash Function
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Happy Number
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Count 1 in Binary
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Fibonacci
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A plus B Problem
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Print Numbers by Recursion
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Majority Number
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Majority Number II
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Majority Number III
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Digit Counts
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Ugly Number
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Plus One
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Palindrome Number
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Task Scheduler
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Single Number
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Linked List
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Remove Duplicates from Sorted List
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Remove Duplicates from Sorted List II
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Remove Duplicates from Unsorted List
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Partition List
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Add Two Numbers
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Two Lists Sum Advanced
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Remove Nth Node From End of List
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Linked List Cycle
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Linked List Cycle II
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Reverse Linked List
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Reverse Linked List II
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Merge Two Sorted Lists
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Merge k Sorted Lists
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Reorder List
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Copy List with Random Pointer
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Sort List
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Insertion Sort List
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Palindrome Linked List
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LRU Cache
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Rotate List
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Swap Nodes in Pairs
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Remove Linked List Elements
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Remove Duplicates from Sorted List
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Binary Tree
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Binary Tree Preorder Traversal
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Binary Tree Inorder Traversal
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Binary Tree Postorder Traversal
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Binary Tree Level Order Traversal
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Binary Tree Level Order Traversal II
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Maximum Depth of Binary Tree
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Balanced Binary Tree
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Binary Tree Maximum Path Sum
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Lowest Common Ancestor
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Invert Binary Tree
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Diameter of a Binary Tree
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Construct Binary Tree from Preorder and Inorder Traversal
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Construct Binary Tree from Inorder and Postorder Traversal
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Subtree
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Binary Tree Zigzag Level Order Traversal
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Binary Tree Serialization
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Binary Tree Preorder Traversal
- Binary Search Tree
- Exhaustive Search
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Dynamic Programming
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Triangle
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Backpack
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Backpack II
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Minimum Path Sum
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Unique Paths
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Unique Paths II
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Climbing Stairs
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Jump Game
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Word Break
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Longest Increasing Subsequence
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Palindrome Partitioning II
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Longest Common Subsequence
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Edit Distance
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Jump Game II
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Best Time to Buy and Sell Stock
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Best Time to Buy and Sell Stock II
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Best Time to Buy and Sell Stock III
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Best Time to Buy and Sell Stock IV
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Distinct Subsequences
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Interleaving String
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Maximum Subarray
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Maximum Subarray II
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Longest Increasing Continuous subsequence
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Longest Increasing Continuous subsequence II
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Maximal Square
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Triangle
- Graph
- Data Structure
- Big Data
- Problem Misc
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Part III - Contest
- Google APAC
- Microsoft
- Appendix I Interview and Resume
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Tags
Subtree
Question
- lintcode: (245) Subtree
You have two every large binary trees: T1,
with millions of nodes, and T2, with hundreds of nodes.
Create an algorithm to decide if T2 is a subtree of T1.
Example
T2 is a subtree of T1 in the following case:
1 3
/ \ /
T1 = 2 3 T2 = 4
/
4
T2 isn't a subtree of T1 in the following case:
1 3
/ \ \
T1 = 2 3 T2 = 4
/
4
Note
A tree T2 is a subtree of T1 if there exists a node n in T1 such that
the subtree of n is identical to T2.
That is, if you cut off the tree at node n,
the two trees would be identical.
copy
题解
判断 T2是否是 T1的子树,首先应该在 T1中找到 T2的根节点,找到根节点后两棵子树必须完全相同。所以整个思路分为两步走:找根节点,判断两棵树是否全等。咋看起来极其简单,但实际实现中还是比较精妙的,尤其是递归的先后顺序及条件与条件或的处理。
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 T1, T2: The roots of binary tree.
* @return: True if T2 is a subtree of T1, or false.
*/
public boolean isSubtree(TreeNode T1, TreeNode T2) {
if (T2 == null) return true;
if (T1 == null) return false;
return identical(T1, T2) || isSubtree(T1.left, T2) || isSubtree(T1.right, T2);
}
private boolean identical(TreeNode T1, TreeNode T2) {
if (T1 == null && T2 == null) return true;
if (T1 == null || T2 == null) return false;
if (T1.val != T2.val) return false;
return identical(T1.left, T2.left) && identical(T1.right, T2.right);
}
}
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源码分析
这道题的异常处理相对 trick 一点,需要理解 null 对子树的含义。另外需要先调用identical
再递归调用isSubtree
判断左右子树的情况。方法identical
中调用.val
前需要判断是否为 null, 而后递归调用判断左右子树是否 identical。
复杂度分析
identical 的调用,时间复杂度近似 , 查根节点的时间复杂度随机,平均为 , 故总的时间复杂度可近似为 .