-
Preface
- FAQ
-
Part I - Basics
- Basics Data Structure
- Basics Sorting
- Basics Algorithm
- Basics Misc
-
Part II - Coding
- String
-
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
-
Remove Element
-
Binary Search
-
First Position of Target
-
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
-
First Position of Target
-
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
-
Happy Number
-
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
-
Palindrome Number
-
Task Scheduler
-
Single Number
-
Linked List
-
Remove Duplicates from Sorted List
-
Remove Duplicates from Sorted List II
-
Remove Duplicates from Unsorted List
-
Partition List
-
Add Two Numbers
-
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
-
Palindrome Linked List
-
LRU Cache
-
Rotate List
-
Swap Nodes in Pairs
-
Remove Linked List Elements
-
Remove Duplicates from Sorted List
-
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 Tree Preorder Traversal
- Binary Search Tree
- Exhaustive 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
-
Maximal Square
-
Triangle
- Graph
- Data Structure
- Big Data
- Problem Misc
-
Part III - Contest
- Google APAC
- Microsoft
- Appendix I Interview and Resume
-
Tags
Unique Binary Search Trees II
Question
- leetcode: Unique Binary Search Trees II | LeetCode OJ
- lintcode: (164) Unique Binary Search Trees II
Given n, generate all structurally unique BST's
(binary search trees) that store values 1...n.
Example
Given n = 3, your program should return all 5 unique BST's shown below.
1 3 3 2 1
\ / / / \ \
3 2 1 1 3 2
/ / \ \
2 1 2 3
copy
题解
题 Unique Binary Search Trees 的升级版,这道题要求的不是二叉搜索树的数目,而是要构建这样的树。分析方法仍然是可以借鉴的,核心思想为利用『二叉搜索树』的定义,如果以 i 为根节点,那么其左子树由[1, i - 1]构成,右子树由[i + 1, n] 构成。要构建包含1到n的二叉搜索树,只需遍历1到n中的数作为根节点,以i
为界将数列分为左右两部分,小于i
的数作为左子树,大于i
的数作为右子树,使用两重循环将左右子树所有可能的组合链接到以i
为根节点的节点上。
容易看出,以上求解的思路非常适合用递归来处理,接下来便是设计递归的终止步、输入参数和返回结果了。由以上分析可以看出递归严重依赖数的区间和i
,那要不要将i
也作为输入参数的一部分呢?首先可以肯定的是必须使用『数的区间』这两个输入参数,又因为i
是随着『数的区间』这两个参数的,故不应该将其加入到输入参数中。分析方便,不妨设『数的区间』两个输入参数分别为start
和end
.
接下来谈谈终止步的确定,由于根据i
拆分左右子树的过程中,递归调用的方法中入口参数会缩小,且存在start <= i <= end
, 故终止步为start > end
. 那要不要对start == end
返回呢?保险起见可以先写上,后面根据情况再做删改。总结以上思路,简单的伪代码如下:
helper(start, end) {
result;
if (start > end) {
result.push_back(NULL);
return;
} else if (start == end) {
result.push_back(TreeNode(i));
return;
}
// dfs
for (int i = start; i <= end; ++i) {
leftTree = helper(start, i - 1);
rightTree = helper(i + 1, end);
// link left and right sub tree to the root i
for (j in leftTree ){
for (k in rightTree) {
root = TreeNode(i);
root->left = leftTree[j];
root->right = rightTree[k];
result.push_back(root);
}
}
}
return result;
}
copy
大致的框架如上所示,我们来个简单的数据验证下,以[1, 2, 3]为例,调用堆栈图如下所示:
- helper(1,3)
- [leftTree]: helper(1, 0) ==> return NULL
- ---loop i = 2---
- [rightTree]: helper(2, 3)
- [leftTree]: helper(2,1) ==> return NULL
- [rightTree]: helper(3,3) ==> return node(3)
- [for loop]: ==> return (2->3)
- ---loop i = 3---
- [leftTree]: helper(2,2) ==> return node(2)
- [rightTree]: helper(4,3) ==> return NULL
- [for loop]: ==> return (3->2)
- ...
简单验证后可以发现这种方法的核心为递归地构造左右子树并将其链接到相应的根节点中。对于start
和end
相等的情况的,其实不必单独考虑,因为start == end
时其左右子树均返回空,故在for
循环中返回根节点。当然单独考虑可减少递归栈的层数,但实际测下来后发现运行时间反而变长了不少 :(
Python
"""
Definition of TreeNode:
class TreeNode:
def __init__(self, val):
this.val = val
this.left, this.right = None, None
"""
class Solution:
# @paramn n: An integer
# @return: A list of root
def generateTrees(self, n):
return self.helper(1, n)
def helper(self, start, end):
result = []
if start > end:
result.append(None)
return result
for i in xrange(start, end + 1):
# generate left and right sub tree
leftTree = self.helper(start, i - 1)
rightTree = self.helper(i + 1, end)
# link left and right sub tree to root(i)
for j in xrange(len(leftTree)):
for k in xrange(len(rightTree)):
root = TreeNode(i)
root.left = leftTree[j]
root.right = rightTree[k]
result.append(root)
return result
copy
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:
/**
* @paramn n: An integer
* @return: A list of root
*/
vector<TreeNode *> generateTrees(int n) {
return helper(1, n);
}
private:
vector<TreeNode *> helper(int start, int end) {
vector<TreeNode *> result;
if (start > end) {
result.push_back(NULL);
return result;
}
for (int i = start; i <= end; ++i) {
// generate left and right sub tree
vector<TreeNode *> leftTree = helper(start, i - 1);
vector<TreeNode *> rightTree = helper(i + 1, end);
// link left and right sub tree to root(i)
for (int j = 0; j < leftTree.size(); ++j) {
for (int k = 0; k < rightTree.size(); ++k) {
TreeNode *root = new TreeNode(i);
root->left = leftTree[j];
root->right = rightTree[k];
result.push_back(root);
}
}
}
return result;
}
};
copy
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 {
/**
* @paramn n: An integer
* @return: A list of root
*/
public List<TreeNode> generateTrees(int n) {
return helper(1, n);
}
private List<TreeNode> helper(int start, int end) {
List<TreeNode> result = new ArrayList<TreeNode>();
if (start > end) {
result.add(null);
return result;
}
for (int i = start; i <= end; i++) {
// generate left and right sub tree
List<TreeNode> leftTree = helper(start, i - 1);
List<TreeNode> rightTree = helper(i + 1, end);
// link left and right sub tree to root(i)
for (TreeNode lnode: leftTree) {
for (TreeNode rnode: rightTree) {
TreeNode root = new TreeNode(i);
root.left = lnode;
root.right = rnode;
result.add(root);
}
}
}
return result;
}
}
copy
源码分析
- 异常处理,返回None/NULL/null.
- 遍历start->end, 递归得到左子树和右子树。
- 两重
for
循环将左右子树的所有可能组合添加至最终返回结果。
注意 DFS 辅助方法helper
中左右子树及返回根节点的顺序。
复杂度分析
递归调用,一个合理的数组区间将生成新的左右子树,时间复杂度为指数级别,使用的临时空间最后都被加入到最终结果,空间复杂度(堆)近似为 , 栈上的空间较大。