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Preface
- FAQ
-
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
-
Tags
Search Range in Binary Search Tree
Question
- lintcode: (11) Search Range in Binary Search Tree
Problem Statement
Given two values k1 and k2 (where k1 < k2) and a root pointer to a Binary Search Tree. Find all the keys of tree in range k1 to k2. i.e. print all x such that k1<=x<=k2 and x is a key of given BST. Return all the keys in ascending order.
Example
If k1 = 10
and k2 = 22
, then your function should return [12, 20, 22]
.
20
/ \
8 22
/ \
4 12
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题解 - 中序遍历
中等偏易难度题,本题涉及到二叉查找树的按序输出,应马上联想到二叉树的中序遍历,对于二叉查找树而言,使用中序遍历即可得到有序元素。对每次访问的元素加以判断即可得最后结果,由于 OJ 上给的模板不适合递归处理,新建一个私有方法即可。
C++
/**
* Definition of TreeNode:
* class TreeNode {
* public:
* int val;
* TreeNode *left, *right;
* TreeNode(int val) {
* this->val = val;
* this->left = this->right = NULL;
* }
* }
*/
class Solution {
public:
/**
* @param root: The root of the binary search tree.
* @param k1 and k2: range k1 to k2.
* @return: Return all keys that k1<=key<=k2 in ascending order.
*/
vector<int> searchRange(TreeNode* root, int k1, int k2) {
vector<int> result;
inorder_dfs(result, root, k1, k2);
return result;
}
private:
void inorder_dfs(vector<int> &ret, TreeNode *root, int k1, int k2) {
if (NULL == root) {
return;
}
inorder_dfs(ret, root->left, k1, k2);
if ((root->val >= k1) && (root->val <= k2)) {
ret.push_back(root->val);
}
inorder_dfs(ret, root->right, k1, k2);
}
};
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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 root: The root of the binary search tree.
* @param k1 and k2: range k1 to k2.
* @return: Return all keys that k1<=key<=k2 in ascending order.
*/
public ArrayList<Integer> searchRange(TreeNode root, int k1, int k2) {
ArrayList<Integer> result = new ArrayList<Integer>();
helper(root, k1, k2, result);
return result;
}
private void helper(TreeNode root, int k1, int k2, ArrayList<Integer> result) {
if (root == null) return;
// in-order binary tree iteration
helper(root.left, k1, k2, result);
if (k1 <= root.val && root.val <= k2) {
result.add(root.val);
}
helper(root.right, k1, k2, result);
}
}
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源码分析
以上为题解思路的简易实现,可以优化的地方为「剪枝过程」的处理——不递归遍历不可能有解的节点。优化后的inorder_dfs
如下:
void inorder_dfs(vector<int> &ret, TreeNode *root, int k1, int k2) {
if (NULL == root) {
return;
}
if ((NULL != root->left) && (root->val > k1)) {
inorder_dfs(ret, root->left, k1, k2);
} // cut-off for left sub tree
if ((root->val >= k1) && (root->val <= k2)) {
ret.push_back(root->val);
} // add valid value
if ((NULL != root->right) && (root->val < k2)) {
inorder_dfs(ret, root->right, k1, k2);
} // cut-off for right sub tree
}
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Warning 「剪枝」的判断条件容易出错,应将当前节点的值与
k1
和k2
进行比较而不是其左子节点或右子节点的值。