[leetcode] 230. Kth Smallest Element in a BST 找出二叉搜索树中的第k小的元素
题目大意
https://leetcode.com/problems/kth-smallest-element-in-a-bst/description/
230. Kth Smallest Element in a BST
Given a binary search tree, write a function kthSmallest to find the kth smallest element in it.
Note:
You may assume k is always valid, 1 ≤ k ≤ BST's total elements.
Example 1:
Input: root = [3,1,4,null,2], k = 13/ \1 4\2 Output: 1Example 2:
Input: root = [5,3,6,2,4,null,null,1], k = 35/ \3 6/ \2 4/1 Output: 3Follow up:
What if the BST is modified (insert/delete operations) often and you need to find the kth smallest frequently? How would you optimize the kthSmallest routine?
给定一棵二叉搜索树(BST),编写一个函数kthSmallest找出其中第k小的元素。
注意:
你可以假设k总是有效的, 1 ≤ k ≤ BST的元素总数。
进一步思考:
如果BST的修改(插入/删除)操作十分频繁,并且需要频繁地找出第k小的元素,应该怎样优化kthSmallest函数?
解题思路
BST具有如下性质:
- 左子树中所有元素的值均小于根节点的值
- 右子树中所有元素的值均大于根节点的值
因此采用中序遍历(左 -> 根 -> 右)即可以递增顺序访问BST中的节点,从而得到第k小的元素,时间复杂度O(k)
Python代码:
# Definition for a binary tree node. class TreeNode(object):def __init__(self, x):self.val = xself.left = Noneself.right = Noneclass Solution(object): def kthSmallest(self, root, k): # 52 ms""":type root: TreeNode:type k: int:rtype: int"""stack = []node = rootwhile node:stack.append(node)node = node.leftx = 1while stack and x <= k:node = stack.pop()x += 1right = node.rightwhile right:stack.append(right)right = right.leftreturn node.val
递归方式:
class Solution(object):def kthSmallest(self, root, k):""":type root: TreeNode:type k: int:rtype: int"""cnt = []self.helper(root, cnt, k)return cnt[k - 1]def helper(self, node, cnt, k):if not node:return Noneself.helper(node.left, cnt, k)cnt.append(node.val)if len(cnt) == k: # 56 ms <= 96msreturn Noneself.helper(node.right, cnt, k)
进一步思考:
如果BST节点TreeNode的属性可以扩展,则再添加一个属性leftCnt,记录左子树的节点个数
上述算法时间复杂度为O(BST的高度)
参考
http://bookshadow.com/weblog/2015/07/02/leetcode-kth-smallest-element-bst/
https://leetcode.com/problems/kth-smallest-element-in-a-bst/discuss/63660/3-ways-implemented-in-JAVA-(Python):-Binary-Search-in-order-iterative-and-recursive
转载于:https://www.cnblogs.com/bymo/p/9667337.html
总结
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