- Preface
- FAQ
- Guidelines for Contributing
- Contributors
- Part I - Basics
- Basics Data Structure
- String
- Linked List
- Binary Tree
- Huffman Compression
- Queue
- Heap
- Stack
- Set
- Map
- Graph
- Basics Sorting
- 算法复习——排序
- 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
- Bitmap
- 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
- 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
- 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
- Permutation Index
- Permutation Index II
- Permutation Sequence
- Unique Binary Search Trees II
- 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
- Follow up
- 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
- 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
- APAC 2016 Round D
- Problem A. Dynamic Grid
- 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
- 術語表
Lowest Common Ancestor
Source
- lintcode: (88) Lowest Common Ancestor
Problem
Given the root and two nodes in a Binary Tree. Find the lowest common ancestor(LCA) of the two nodes.
The lowest common ancestor is the node with largest depth which is the ancestor of both nodes.
Example
For the following binary tree:
4
/ \
3 7
/ \
5 6
LCA(3, 5) = 4
LCA(5, 6) = 7
LCA(6, 7) = 7
题解 1 - 自底向上
初次接触这种题可能会没有什么思路,在没有思路的情况下我们就从简单例子开始分析!首先看看 3
和 5
,这两个节点分居根节点 4
的两侧,如果可以从子节点往父节点递推,那么他们将在根节点 4
处第一次重合;再来看看 5
和 6
,这两个都在根节点 4
的右侧,沿着父节点往上递推,他们将在节点 7
处第一次重合;最后来看看 6
和 7
,此时由于 7
是 6
的父节点,故 7
即为所求。从这三个基本例子我们可以总结出两种思路——自顶向下(从前往后递推) 和自底向上(从后往前递推)。
顺着上述实例的分析,我们首先看看自底向上的思路,自底向上的实现用一句话来总结就是——如果遍历到的当前节点是 A/B 中的任意一个,那么我们就向父节点汇报此节点,否则递归到节点为空时返回空值。具体来说会有如下几种情况:
- 当前节点不是两个节点中的任意一个,此时应判断左右子树的返回结果。
- 若左右子树均返回非空节点,那么当前节点一定是所求的根节点,将当前节点逐层向前汇报。// 两个节点分居树的两侧
- 若左右子树仅有一个子树返回非空节点,则将此非空节点向父节点汇报。// 节点仅存在于树的一侧
- 若左右子树均返回
NULL
, 则向父节点返回NULL
. // 节点不在这棵树中
- 当前节点即为两个节点中的一个,此时向父节点返回当前节点。
根据此递归模型容易看出应该使用先序/后序遍历来实现。
C++ Recursion From Bottom to Top
/**
* 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 A and B: two nodes in a Binary.
* @return: Return the least common ancestor(LCA) of the two nodes.
*/
TreeNode *lowestCommonAncestor(TreeNode *root, TreeNode *A, TreeNode *B) {
// return either A or B or NULL
if (NULL == root || root == A || root == B) return root;
TreeNode *left = lowestCommonAncestor(root->left, A, B);
TreeNode *right = lowestCommonAncestor(root->right, A, B);
// A and B are on both sides
if ((NULL != left) && (NULL != right)) return root;
// either left or right or NULL
return (NULL != left) ? left : right;
}
};
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 A and B: two nodes in a Binary.
* @return: Return the least common ancestor(LCA) of the two nodes.
*/
public TreeNode lowestCommonAncestor(TreeNode root, TreeNode A, TreeNode B) {
if (root == null) return null;
TreeNode lNode = lowestCommonAncestor(root.left, A, B);
TreeNode rNode = lowestCommonAncestor(root.right, A, B);
// root is the LCA of A and B
if (lNode != null && rNode != null) return root;
// root node is A/B(including the case below)
if (root == A || root == B) return root;
// return lNode/rNode if root is not LCA
return (lNode != null) ? lNode : rNode;
}
}
源码分析
结合例子和递归的整体思想去理解代码,在 root == A || root == B
后即层层上浮(自底向上),直至找到最终的最小公共祖先节点。
最后一行 return (NULL != left) ? left : right;
将非空的左右子树节点和空值都包含在内了,十分精炼!leetcode
细心的你也许会发现,其实题解的分析漏掉了一种情况,即树中可能只含有 A/B 中的一个节点!这种情况应该返回空值,但上述实现均返回非空节点。
关于重复节点:由于这里比较的是元素地址,因此可以认为树中不存在重复元素,否则不符合树的数据结构。
题解 - 自底向上(计数器)
为了解决上述方法可能导致误判的情况,我们可以对返回结果添加计数器来解决。 由于此计数器的值只能由子树向上递推,故应该用后序遍历。 在类中添加私有变量较为方便, C++中的写法较为复杂,后续再优化。
定义 pair<TreeNode *, int> result(node, counter)
表示遍历到某节点时的返回结果,返回的 node
表示 LCA 路径中的可能的最小节点,相应的计数器 counter
则表示目前和 A
或者 B
匹配的节点数,若计数器为 2,则表示已匹配过两次,该节点即为所求,若只匹配过一次,还需进一步向上递推。表述地可能比较模糊,还是看看代码吧。
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 A and B: two nodes in a Binary.
* @return: Return the least common ancestor(LCA) of the two nodes.
*/
TreeNode *lowestCommonAncestor(TreeNode *root, TreeNode *A, TreeNode *B) {
if ((NULL == A) || (NULL == B)) return NULL;
pair<TreeNode *, int> result = helper(root, A, B);
if (A != B) {
return (2 == result.second) ? result.first : NULL;
} else {
return (1 == result.second) ? result.first : NULL;
}
}
private:
pair<TreeNode *, int> helper(TreeNode *root, TreeNode *A, TreeNode *B) {
TreeNode * node = NULL;
if (NULL == root) return make_pair(node, 0);
pair<TreeNode *, int> left = helper(root->left, A, B);
pair<TreeNode *, int> right = helper(root->right, A, B);
// return either A or B
int count = max(left.second, right.second);
if (A == root || B == root) return make_pair(root, ++count);
// A and B are on both sides
if (NULL != left.first && NULL != right.first) return make_pair(root, 2);
// return either left or right or NULL
return (NULL != left.first) ? left : right;
}
};
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 {
private int count = 0;
/**
* @param root: The root of the binary search tree.
* @param A and B: two nodes in a Binary.
* @return: Return the least common ancestor(LCA) of the two nodes.
*/
public TreeNode lowestCommonAncestor(TreeNode root, TreeNode A, TreeNode B) {
TreeNode result = helper(root, A, B);
if (A == B) {
return result;
} else {
return (count == 2) ? result : null;
}
}
private TreeNode helper(TreeNode root, TreeNode A, TreeNode B) {
if (root == null) return null;
TreeNode lNode = helper(root.left, A, B);
TreeNode rNode = helper(root.right, A, B);
// root is the LCA of A and B
if (lNode != null && rNode != null) return root;
// root node is A/B(including the case below)
if (root == A || root == B) {
count++;
return root;
}
// return lNode/rNode if root is not LCA
return (lNode != null) ? lNode : rNode;
}
}
源码分析
在 A == B
时,计数器返回 1 的节点即为我们需要的节点,否则只取返回 2 的节点,如此便保证了该方法的正确性。对这种实现还有问题的在下面评论吧。
Reference
leetcode. Lowest Common Ancestor of a Binary Tree Part I | LeetCode - 清晰易懂的题解和实现。 ↩
- Lowest Common Ancestor of a Binary Tree Part II | LeetCode - 如果存在指向父节点的指针,我们能否有更好的解决方案?
- Lowest Common Ancestor of a Binary Search Tree (BST) | LeetCode - 二叉搜索树中求最小公共祖先。
- Lowest Common Ancestor | 九章算法 - 第一种和第二种方法可以在知道父节点时使用,但第二种 Divide and Conquer 才是本题需要的思想(第二种解法可以轻易改成不需要 parent 的指针的)。
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