Nine Chapter
  • Introduction
    • Summary
  • 1.Binary Search
    • Introduction
    • 458.Last position of target
    • 600.Smallest Rectangle Enclosing Black Pixels
    • 585.Maximum Number in Mountain Sequence
    • 183.Wood Cut
    • 62.Search in Rotated Sorted Array
    • 63.Search in Rotated Sorted Array II
    • 159.Find Minimum in Rotated Sorted Array
    • 160.Find Minimum in Rotated Sorted Array II
    • 75.Find Peak Element
    • 60.Search Insert Position
    • 28.Search a 2D Matrix
    • 240. Search a 2D Matrix II
    • 14.First Position of Target
    • 74.First Bad Version
    • 875. Koko Eating Bananas
    • 1011. Capacity To Ship Packages Within D Days (M)
    • 410. Split Array Largest Sum (H)
    • 475. Heaters (M)
    • 1044. Longest Duplicate Substring (H)
  • 2.Binary Tree
    • Summary
      • 二叉树八股文:递归改迭代
      • BST
      • Frame
    • 66.Binary Tree Preorder Traversal
    • 67.🌟Binary Tree Inorder Traversal
    • 145. Binary Tree Postorder Traversal (E)
    • 98.Validate Binary Search Tree(M)
    • 85.Insert Node in a Binary Search Tree
    • 104. Maximum Depth of Binary Tree(E)
    • 235. Lowest Common Ancestor of a Binary Search Tree (E)
    • 236.Lowest Common Ancestor of Binary Tree(M)
    • 578.Lowest Common Ancestor III
    • 1120.Subtree with Maximum Average
    • 596.Minimum Subtree
    • 480.Binary Tree Paths
    • 453.Flatten Binary Tree to Linked List
    • 110.Balanced Binary Tree
    • 376.Binary Tree Path Sum
    • 246.Binary Tree Path Sum II
    • 475.Binary Tree Maximum Path Sum II
    • 124.Binary Tree Maximum Path Sum (H)
    • Path Sum (*)
      • 112. Path Sum
      • 113. Path Sum II
      • 437. Path Sum III
    • 177.Convert Sorted Array to Binary Search Tree With Minimal Height
    • 7.Binary Tree Serialization
    • 72,73.Construct Binary Tree
    • Binary Search Tree Path
    • 245.Subtree
    • 469.Identical Binary Tree
    • 87.Remove Node in Binary Search Tree
    • 116.Populating Next Right Pointers in Each Node (M)
    • 114. Flatten Binary Tree to Linked List(M)
    • 654.Maximum Binary Tree (M)
    • 105. 🌟Construct Binary Tree from Preorder and Inorder Traversal (M)
    • 106. Construct Binary Tree from Inorder and Postorder Traversal (M)
    • 652. Find Duplicate Subtrees(M)
    • 230. Kth Smallest Element in a BST (M)
    • 538&1038. Convert BST to Greater Tree
    • 450. Delete Node in a BST (M)
    • 701. Insert into a Binary Search Tree (M)
    • 96. Unique Binary Search Trees
    • 95. Unique Binary Search Trees II (M)
    • 1373. Maximum Sum BST in Binary Tree (H)
    • 297. Serialize and Deserialize Binary Tree (H)
    • 222. Count Complete Tree Nodes (M)
    • 1120. Maximum Average Subtree
    • 341. Flatten Nested List Iterator
    • 333. Largest BST Subtree (M)
    • 543. Diameter of Binary Tree
    • Binary Tree Longest Consecutive Sequence(*)
      • 298.Binary Tree Longest Consecutive Sequence
      • 549. Binary Tree Longest Consecutive Sequence II (M)
  • 3.Breadth First Search
    • Introduction
      • BFS 算法解题套路框架
      • 双向 BFS 优化
    • 102.Binary Tree Level Order Traversal (M)
    • 103. Binary Tree Zigzag Level Order Traversal (M)
    • 107.Binary Tree Level Order Traversal II(M)
    • 618.Search Graph Nodes
    • 207.Course Schedule (M)
    • 210.Course Schedule II (M)
    • 611.Knight Shortest Path
    • 598.Zombie in Matrix
    • 133.Clone Graph (M)
    • 178.Graph Valid Tree
    • 7.Binary Tree Serialization
    • 574.Build Post Office
    • 573.Build Post Office II
    • 127.Topological Sorting
    • 127.Word Ladder
    • 126. Word Ladder II
    • (LeetCode)515.Find Largest Value in Each Tree Row
    • 111. Minimum Depth of Binary Tree (E)
    • 752. Open the Lock
    • 542. 01 Matrix (M)
    • 1306. Jump Game III (M)
  • 4.Depth First Search+BackTracking
    • Summary
      • FloodFill 算法
    • 136.Palindrome Partitioning
    • 39.Combination Sum
    • 40.Combination Sum II
    • 377. Combination Sum IV
    • 77.Combinations (M)
    • 78.Subsets (M)
    • 90.Subsets II (M)
    • 46.🌟Permutations
    • 47.Permutations II
    • 582.Word Break II
    • 490.The Maze (M)
    • 51.N-Queens (H)
    • 52. N-Queens II (H)
    • 698. Partition to K Equal Sum Subsets (M)
    • 22. Generate Parentheses (M)
    • 岛屿问题
      • 200.Number of Islands (M)
      • 1254. Number of Closed Islands (M)
      • 1020. Number of Enclaves (M)
      • 695. Max Area of Island (M)
      • 1905. Count Sub Islands (M)
      • 694. Number of Distinct Islands
    • 131. Palindrome Partitioning (M)
    • 967. Numbers With Same Consecutive Differences (M)
    • 79. Word Search (M)
    • 212. Word Search II (M)
    • 472. Concatenated Words (H)
    • Page 2
    • 291. Word Pattern II
    • 17. Letter Combinations of a Phone Number (M)
  • 5.LinkedList
    • Summary
      • 单链表的倒数第 k 个节点
      • Merge two/k sorted LinkedList
      • Middle of the Linked List
      • 判断链表是否包含环
      • 两个链表是否相交 Intersection of Two Linked Lists
      • 递归反转链表
      • 如何判断回文链表
    • 599.Insert into a Cyclic Sorted List
    • 21.Merge Two Sorted Lists (E)
    • 23.Merge k Sorted Lists (H)
    • 105.Copy List with Random Pointer
    • 141.Linked List Cycle (E)
    • 142.Linked List Cycle II (M)
    • 148.Sort List (M)
    • 86.Partition List (M)
    • 83.Remove Duplicates from Sorted List(E)
    • 82.Remove Duplicates from Sorted List II (M)
    • 206.Reverse Linked List (E)
    • 92.Reverse Linked List II (M)
    • 143.Reorder List (M)
    • 19.Remove Nth Node From End of List (E)
    • 170.Rotate List
    • 🤔25.Reverse Nodes in k-Group (H)
    • 452.Remove Linked List Elements
    • 167.Add Two Numbers
    • 221.Add Two Numbers II
    • 876. Middle of the Linked List (E)
    • 160. Intersection of Two Linked Lists (E)
    • 234. Palindrome Linked List (E)
    • 2130. Maximum Twin Sum of a Linked List (M)
  • 6.Array
    • Summary
      • 前缀和思路PrefixSum
      • 差分数组 Difference Array
      • 双指针Two Pointers
      • 滑动窗口算法算法
      • Sliding windows II
      • 二分搜索Binary Search
      • 排序算法
      • 快速选择算法
    • 604.Window Sum
    • 138.Subarray Sum
    • 41.Maximum Subarray
    • 42.Maximum Subarray II
    • 43.Maximum Subarray III
    • 620.Maximum Subarray IV
    • 621.Maximum Subarray V
    • 6.Merge Two Sorted Arrays
    • 88.Merge Sorted Array
    • 547.Intersection of Two Arrays
    • 548.Intersection of Two Arrays II
    • 139.Subarray Sum Closest
    • 65.Median of two Sorted Arrays
    • 636.132 Pattern
    • 402.Continuous Subarray Sum
    • 303. Range Sum Query - Immutable (E)
    • 304.Range Sum Query 2D - Immutable (M)
    • 560. Subarray Sum Equals K (M)
    • 370. Range Addition(M)
    • 1109. Corporate Flight Bookings(M)
    • 1094. Car Pooling (M)
    • 76. Minimum Window Substring(H)
    • 567. Permutation in String (M)
    • 438. Find All Anagrams in a String(M)
    • 3. Longest Substring Without Repeating Characters (M)
    • 380. Insert Delete GetRandom O(1) (M)
    • 710. Random Pick with Blacklist (H)
    • 528. Random Pick with Weight (M)
    • 26. Remove Duplicates from Sorted Array (E)
    • 27. Remove Element (E)
    • 283. Move Zeroes (E)
    • 659. Split Array into Consecutive Subsequences (M)
    • 4. Median of Two Sorted Arrays (H)
    • 48. Rotate Image (M)
    • 54. Spiral Matrix (M)
    • 59. Spiral Matrix II (M)
    • 918. Maximum Sum Circular Subarray
    • 128. Longest Consecutive Sequence (M)
    • 238. Product of Array Except Self (M)
    • 1438. Longest Continuous Subarray With Absolute Diff Less Than or Equal to Limit (M)
    • 1151. Minimum Swaps to Group All 1's Together (M)
    • 2134. Minimum Swaps to Group All 1's Together II
    • 2133. Check if Every Row and Column Contains All Numbers
    • 632. Smallest Range Covering Elements from K Lists (H)
    • 36. Valid Sudoku (M)
    • 383. Ransom Note
    • 228. Summary Ranges
  • 7.Two pointers
    • Summary
      • Two Sum
      • 2Sum 3Sum 4Sum 问题
    • 1.Two Sum I
    • 170.Two Sum III - Data structure design
    • 167.Two Sum II- Input array is sorted
    • 609.Two Sum - Less than or equal to target
    • 610.Two Sum - Difference equals to targe
    • 587.Two Sum - Unique pairs
    • 533.Two Sum - Closest to target
    • 443.Two Sum - Greater than target
    • 653. Two Sum IV - Input is a BST (M)
    • 57.3Sum
    • 59.3Sum Closest
    • 58.4Sum
    • 148.Sort Colors
    • 143.Sort Colors II
    • 31.Partition Array
    • 625.Partition Array II
    • 382.Triangle Count
      • 611. Valid Triangle Number
    • 521.Remove Duplicate Numbers in Array
    • 167. Two Sum II - Input Array Is Sorted (E)
    • 870. Advantage Shuffle (M)
    • 9. Palindrome Number (E)
    • 125. Valid Palindrome(E)
    • 5. Longest Palindromic Substring (M)
    • 42. Trapping Rain Water
    • 11. Container With Most Water (M)
    • 658. Find K Closest Elements (M)
    • 392. Is Subsequence
  • 8.Data Structure
    • Summary
      • 数据结构的存储方式
      • 单调栈
      • 单调队列
      • 二叉堆 Binary Heap
      • TreeMap
      • TreeSet
      • 🌟Trie
      • Trie Application
    • 155. Min Stack (E)
    • 716. Max Stack (E)
    • 1648. Sell Diminishing-Valued Colored Balls
    • 232. Implement Queue using Stacks (E)
    • 225. Implement Stack using Queues(E)
    • 84.Largest Rectangle in Histogram
    • 128.Hash Function
    • Max Tree
    • 544.Top k Largest Numbers
    • 545.Top k Largest Numbers II
    • 613.High Five
    • 606.Kth Largest Element II
    • 5.Kth Largest Element
    • 129.Rehashing
    • 4.Ugly Number II
    • 517.Ugly Number
    • 28. Implement strStr()
    • 594.strStr II
    • 146.LRU Cache
    • 460.LFU Cache
    • 486.Merge k Sorted Arrays
    • 130.Heapify
    • 215. Kth Largest Element in an Array (M)
    • 612.K Closest Points
    • 692. Top K Frequent Words
    • 347.Top K Frequent Elements
    • 601.Flatten 2D Vector
    • 540.Zigzag Iterator
    • 541.Zigzag Iterator II
    • 423.Valid Parentheses
    • 488.Happy Number
    • 547.Intersection of Two Arrays
    • 548.Intersection of Two Arrays II
    • 627.Longest Palindrome
    • 638.Strings Homomorphism
    • 138.Subarray Sum
    • 647.Substring Anagrams
    • 171.Anagrams
    • 739. Daily Temperatures(M)
    • 496. Next Greater Element I (E)
    • 503. Next Greater Element II(M)
    • 316. Remove Duplicate Letters(M) & 1081. Smallest Subsequence of Distinct Characters
    • 239. Sliding Window Maximum (H)
    • 355. Design Twitter (M)
    • 895. Maximum Frequency Stack (H)
    • 20. Valid Parentheses (E)
    • 921. Minimum Add to Make Parentheses Valid (M)
    • 1541. Minimum Insertions to Balance a Parentheses String (M)
    • 32. Longest Valid Parentheses (H)
    • Basic Calculator (*)
      • 224. Basic Calculator
      • 227. Basic Calculator II (M)
    • 844. Backspace String Compare
    • 295. Find Median from Data Stream
    • 208. Implement Trie (Prefix Tree)
    • 461.Kth Smallest Numbers in Unsorted Array
    • 1152.Analyze user website visit pattern
    • 811. Subdomain Visit Count (M)
    • 71. Simplify Path (M)
    • 362. Design Hit Counter
  • 9.Dynamic Programming
    • Summary
      • 最优子结构 Optimal Sustructure
      • 子序列解题模板
      • 空间压缩
      • 背包问题
        • Untitled
      • 股票买卖问题
      • KMP
    • 109.Triangle
    • 110.Minimum Path Sum
    • 114.Unique Paths
    • 115.Unique Paths II
    • 70.Climbing Stairs
    • 272.Climbing StairsII
    • 116.Jump Game
    • 117.Jump Game II
    • 322.Coin Change
    • 518. Coin Change 2 ()
    • Backpack I~VI
      • LintCode 563.Backpack V (M)
    • Best Time to Buy and Sell Stock(*)
      • 121. Best Time to Buy and Sell Stock
      • 122. Best Time to Buy and Sell Stock II (M)
      • 123. Best Time to Buy and Sell Stock III (H)
      • 188. Best Time to Buy and Sell Stock IV (H)
      • 309. Best Time to Buy and Sell Stock with Cooldown (M)
      • 714. Best Time to Buy and Sell Stock with Transaction Fee (M)
    • 394.Coins in a line
    • 395.Coins in a Line II
    • 509. Fibonacci Number (E)
    • 931. Minimum Falling Path Sum (M)
    • 494. Target Sum (M)
    • 72. Edit Distance (H)
    • 300.Longest Increasing Subsequence
    • 1143. Longest Common Subsequence (M)
    • 718. Maximum Length of Repeated Subarray
    • 583. Delete Operation for Two Strings (M)
    • 712. Minimum ASCII Delete Sum for Two Strings(M)
    • 53. Maximum Subarray (E)
    • 516. Longest Palindromic Subsequence (M)
    • 1312. Minimum Insertion Steps to Make a String Palindrome (H)
    • 416. Partition Equal Subset Sum (M)
    • 64. Minimum Path Sum(M)
    • 651. 4 Keys Keyboards (M)
    • House Robber (*)
      • 198. House Robber (M)
      • 213. House Robbber II
      • 337. House Robber III (M)
    • Word Break (*)
      • 139.Word Break (M)
    • 140. Word Break II (H)
    • 828. Count Unique Characters of All Substrings of a Given String (H)
    • 174. Dungeon Game (H)
    • 1567. Maximum Length of Subarray With Positive Product (M)
  • 10. Graph
    • Introduction
      • 有向图的环检测
      • 拓扑排序
      • 二分图判定
      • Union-Find
      • 最小生成树(Minimum Spanning Tree)算法
        • KRUSKAL 最小生成树算法
        • Prim 最小生成树算法
      • Dijkstra 最短路径算法
      • BFS vs DFS
    • 797. All Paths From Source to Target (M)
    • 785. Is Graph Bipartite? (M)
    • 886. Possible Bipartition (M)
    • 130. Surrounded Regions (M)
    • 990. Satisfiability of Equality Equations (M)
    • 721. Accounts Merge (M)
    • 323. Number of Connected Components in an Undirected Graph (M)
    • 261. Graph Valid Tree
    • 1135. Connecting Cities With Minimum Cost
    • 1584. Min Cost to Connect All Points (M)
    • 277. Find the Celebrity (M)
    • 743. Network Delay Time (M)
    • 1631. Path With Minimum Effort (M)
    • 1514. Path with Maximum Probability (M)
    • 589.Connecting Graph
    • 🌟787. Cheapest Flights Within K Stops (M)
    • 2050. Parallel Courses III (H)
    • 1293. Shortest Path in a Grid with Obstacles Elimination (H)
    • 864. Shortest Path to Get All Keys (H)
    • 269. Alien Dictionary (H)
    • 1192. Critical Connections in a Network (H)
    • 529. Minesweeper (M)
  • 11.Math
    • Page 1
Powered by GitBook
On this page

Was this helpful?

  1. 5.LinkedList
  2. Summary

递归反转链表

https://labuladong.github.io/algo/2/17/17/ LeetCode 206,92

Previous两个链表是否相交 Intersection of Two Linked ListsNext如何判断回文链表

Last updated 3 years ago

Was this helpful?

反转单链表的迭代实现不是一个困难的事情,但是递归实现就有点难度了,如果再加一点难度,让你仅仅反转单链表中的一部分,你是否能够递归实现呢?

本文就来由浅入深,step by step 地解决这个问题。如果你还不会递归地反转单链表也没关系,本文会从递归反转整个单链表开始拓展,只要你明白单链表的结构,相信你能够有所收获。

// 单链表节点的结构
public class ListNode {
    int val;
    ListNode next;
    ListNode(int x) { val = x; }
}

什么叫反转单链表的一部分呢,就是给你一个索引区间,让你把单链表中这部分元素反转,其他部分不变:

注意这里的索引是从 1 开始的。迭代的思路大概是:先用一个 for 循环找到第 m 个位置,然后再用一个 for 循环将 m 和 n 之间的元素反转。但是我们的递归解法不用一个 for 循环,纯递归实现反转。

迭代实现思路看起来虽然简单,但是细节问题很多的,反而不容易写对。相反,递归实现就很简洁优美,下面就由浅入深,先从反转整个单链表说起。

一、递归反转整个链表

这也是力扣第 206 题「反转链表」,递归反转单链表的算法可能很多读者都听说过,这里详细介绍一下,直接看代码实现:

ListNode reverse(ListNode head) {
    if (head == null || head.next == null) {
        return head;
    }
    ListNode last = reverse(head.next);
    head.next.next = head;
    head.next = null;
    return last;
}

看起来是不是感觉不知所云,完全不能理解这样为什么能够反转链表?这就对了,这个算法常常拿来显示递归的巧妙和优美,我们下面来详细解释一下这段代码。

对于递归算法,最重要的就是明确递归函数的定义。具体来说,我们的 reverse 函数定义是这样的:

输入一个节点 head,将「以 head 为起点」的链表反转,并返回反转之后的头结点。

明白了函数的定义,再来看这个问题。比如说我们想反转这个链表:

那么输入 reverse(head) 后,会在这里进行递归:

ListNode last = reverse(head.next);

不要跳进递归(你的脑袋能压几个栈呀?),而是要根据刚才的函数定义,来弄清楚这段代码会产生什么结果:

这个 reverse(head.next) 执行完成后,整个链表就成了这样:

并且根据函数定义,reverse 函数会返回反转之后的头结点,我们用变量 last 接收了。

现在再来看下面的代码:

head.next.next = head;

接下来:

head.next = null;
return last;

神不神奇,这样整个链表就反转过来了!递归代码就是这么简洁优雅,不过其中有两个地方需要注意:

1、递归函数要有 base case,也就是这句:

if (head.next == null) return head;

意思是如果链表只有一个节点的时候反转也是它自己,直接返回即可。

2、当链表递归反转之后,新的头结点是 last,而之前的 head 变成了最后一个节点,别忘了链表的末尾要指向 null:

head.next = null;

理解了这两点后,我们就可以进一步深入了,接下来的问题其实都是在这个算法上的扩展。

二、反转链表前 N 个节点

这次我们实现一个这样的函数:

// 将链表的前 n 个节点反转(n <= 链表长度)
ListNode reverseN(ListNode head, int n)

比如说对于下图链表,执行 reverseN(head, 3):

解决思路和反转整个链表差不多,只要稍加修改即可:

ListNode successor = null; // 后驱节点

// 反转以 head 为起点的 n 个节点,返回新的头结点
ListNode reverseN(ListNode head, int n) {
    if (n == 1) {
        // 记录第 n + 1 个节点
        successor = head.next;
        return head;
    }
    // 以 head.next 为起点,需要反转前 n - 1 个节点
    ListNode last = reverseN(head.next, n - 1);

    head.next.next = head;
    // 让反转之后的 head 节点和后面的节点连起来
    head.next = successor;
    return last;
}

(Another version)优化后的代码:

// 反转以 head 为起点的 n 个节点,返回新的头结点
ListNode reverseN(ListNode head, int n) {
    if (n == 1) {
        // 返回最后头节点
        return head;
    }
    // 以 head.next 为起点,需要反转前 n - 1 个节点
    ListNode last = reverseN(head.next, n - 1);
    
    /*
    这里意思是,head的next指针没有啥用,可以用来记录第n+1个节点,随着递归一直往上传到原来头节点的next。(可以自己画图,比较好理解一点)
    */
    ListNode headNext = head.next;
    head.next = headNext.next;
    headNext.next = head;

    return last;
}

具体的区别:

1、base case 变为 n == 1,反转一个元素,就是它本身,同时要记录后驱节点。

2、刚才我们直接把 head.next 设置为 null,因为整个链表反转后原来的 head 变成了整个链表的最后一个节点。但现在 head 节点在递归反转之后不一定是最后一个节点了,所以要记录后驱 successor(第 n + 1 个节点),反转之后将 head 连接上。

OK,如果这个函数你也能看懂,就离实现「反转一部分链表」不远了。

三、反转链表的一部分

现在解决我们最开始提出的问题,给一个索引区间 [m,n](索引从 1 开始),仅仅反转区间中的链表元素。

ListNode reverseBetween(ListNode head, int m, int n)

首先,如果 m == 1,就相当于反转链表开头的 n 个元素嘛,也就是我们刚才实现的功能:

ListNode reverseBetween(ListNode head, int m, int n) {
    // base case
    if (m == 1) {
        // 相当于反转前 n 个元素
        return reverseN(head, n);
    }
    // ...
}

如果 m != 1 怎么办?如果我们把 head 的索引视为 1,那么我们是想从第 m 个元素开始反转对吧;如果把 head.next 的索引视为 1 呢?那么相对于 head.next,反转的区间应该是从第 m - 1 个元素开始的;那么对于 head.next.next 呢……

区别于迭代思想,这就是递归思想,所以我们可以完成代码:

ListNode reverseBetween(ListNode head, int m, int n) {
    // base case
    if (m == 1) {
        return reverseN(head, n);
    }
    // 前进到反转的起点触发 base case
    head.next = reverseBetween(head.next, m - 1, n - 1);
    return head;
}

至此,我们的最终大 BOSS 就被解决了。

四、最后总结

递归的思想相对迭代思想,稍微有点难以理解,处理的技巧是:不要跳进递归,而是利用明确的定义来实现算法逻辑。

处理看起来比较困难的问题,可以尝试化整为零,把一些简单的解法进行修改,解决困难的问题。

值得一提的是,递归操作链表并不高效。和迭代解法相比,虽然时间复杂度都是 O(N),但是迭代解法的空间复杂度是 O(1),而递归解法需要堆栈,空间复杂度是 O(N)。所以递归操作链表可以作为对递归算法的练习或者拿去和小伙伴装逼,但是考虑效率的话还是使用迭代算法更好。