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
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  • Data Structure
  • HashTable 工作原理:
  • Hashtable与HashMap的简单比较

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  1. 8.Data Structure

Summary

Previous392. Is SubsequenceNext数据结构的存储方式

Last updated 3 years ago

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Data Structure

1.Linear Data Structure

(1) Queue : O(1) offer/ O(1) poll /O(1) peek ==>BFS

(2) Stack : O(1) push / O(1) pop /O(1) peek ==>Non-recursive DFS

(3) Hash : O(1) Insert / O(1) Find / O(1) Delete ==> Hash Table / Hash Map / Hash Set 的区别是什么?

There are several differences between HashMap and Hashtable in Java:

  1. is, whereasis not. This makesbetter for non-threaded applications, as unsynchronized Objects typically perform better than synchronized ones.

  2. does not allownullkeys or values.allows onenullkey and any number ofnullvalues.

  3. One of HashMap's subclasses is,(LRU Cache) so in the event that you'd want predictable iteration order (which is insertion order by default), you could easily swap out thefor a. This wouldn't be as easy if you were using.

Since synchronization is not an issue for you, I'd recommend. If synchronization becomes an issue, you may also look atorCollections.synchronizedMap(map).

Note: What is java thread synchronized-- When we start two or more threads within a program, there may be a situation when multiple threads try to access the same resource and finally they can produce unforeseen result due to concurrency issues. For example, if multiple threads try to write within a same file then they may corrupt the data because one of the threads can override data or while one thread is opening the same file at the same time another thread might be closing the same file.

So there is a need to synchronize the action of multiple threads and make sure that only one thread can access the resource at a given point in time. This is implemented using a concept called monitors. Each object in Java is associated with a monitor, which a thread can lock or unlock. Only one thread at a time may hold a lock on a monitor.

Java programming language provides a very handy way of creating threads and synchronizing their task by using synchronized blocks. You keep shared resources within this block. Following is the general form of the synchronized statement

2. Tree Data Structure

(1) Heap : O(log N) Add / O(log N) Remove / O(1) Min or Max

基本操作:130.

(2) Priority Queue

如果不提供Comparator的话,优先队列中元素默认按自然顺序排列,也就是数字默认是小的在队列头,字符串则按字典序排列。

如果想实现按照自己的意愿进行优先级排列的队列的话,需要实现Comparator接口。

实际上是一个堆(不指定Comparator时默认为最小堆) 队列既可以根据元素的自然顺序来排序,也可以根据 Comparator来设置排序规则。 队列的头是按指定排序方式的最小元素。如果多个元素都是最小值,则头是其中一个元素。 新建对象的时候可以指定一个初始容量,其容量会自动增加。

注意1:该队列是用数组实现,但是数组大小可以动态增加,容量无限。

注意2:队列的实现不是同步的。不是线程安全的。如果多个线程中的任意线程从结构上修改了列表, 则这些线程不应同时访问 PriorityQueue实例。保证线程安全可以使用PriorityBlockingQueue 类。

注意3:不允许使用 null 元素。

注意4:插入方法(offer()、poll()、remove() 、add() 方法)时间复杂度为O(log(n)) ; remove(Object) 和 contains(Object) 时间复杂度为O(n); 检索方法(peek、element 和 size)时间复杂度为常量。

注意5:方法iterator()中提供的迭代器并不保证以有序的方式遍历优先级队列中的元素。(原因可参考PriorityQueue的内部实现) 如果需要按顺序遍历,可用Arrays.sort(pq.toArray())。

注意6:可以在构造函数中指定如何排序。如: PriorityQueue() 使用默认的初始容量(11)创建一个 PriorityQueue,并根据其自然顺序来排序其元素(使用 Comparable)。 PriorityQueue(int initialCapacity) 使用指定的初始容量创建一个 PriorityQueue,并根据其自然顺序来排序其元素(使用 Comparable)。 PriorityQueue(int initialCapacity, Comparator<? super E> comparator) 使用指定的初始容量创建一个 PriorityQueue,并根据指定的比较器comparator来排序其元素。

注意7:此类及其迭代器实现了 Collection 和 Iterator 接口的所有可选 方法。

PriorityQueue对元素采用的是堆排序,头是按指定排序方式的最小元素。堆排序只能保证根是最大(最小),整个堆并不是有序的。 方法iterator()中提供的迭代器可能只是对整个数组的依次遍历。也就只能保证数组的第一个元素是最小的。

示例:自己指定比较器进行排序(Descending)

Queue<Integer> pq = new PriorityQueue<Integer>(11, new Comparator<Integer>() {
                    public int compare(Integer i1, Integer i2) {
                        return i2 -i1;
                    }
                });
                
//Another one:
Queue<Integer> pq = new PriorityQueue<Integer>((a,b) -> {return a-b;});

HashTable 工作原理:

Hashtable中有一个内部类Entry,用来保存单元数据,我们用来构建哈希表的每一个数据是Entry的一个实例。假设我们保存下面一组数据,第一列作为key,第二列作为value。

Hashtable内部用一个Entry数组table,来保存所有的数据。

当我们插入一个新的Entry对象时,即用Hashtable的put(key, value)方法。 在put方法里: 计算key的hash值 计算index值,作为数组table的下标,即table[index] 哈希表中根据key的索引值index,创建了多个bucket,所有index值一样的Entry对象,构造成一个链接表存放在同一个bucket里。既然是一个链接表,根据数据结构知识,自然我们的Entry对象需要有一个指向下一个对象的指针,即Entry对象需要有这些属性:key,value,next。

put方法的整个流程为:

1.判断value是否为空,为空则抛出异常;

2.计算key的hash值,并根据hash值获得key在table数组中的位置index,如果table[index]元素不为空,则进行迭代,如果遇到相同的key,则直接替换,并返回旧value;

3.否则,我们可以将其插入到table[index]位置。

通过一个实际的例子来演示一下这个过程:

假设我们现在Hashtable的容量为5,已经存在了(5,5),(13,13),(16,16),(17,17),(21,21)这5个键值对,目前他们在Hashtable中的位置如下:

现在,我们插入一个新的键值对,put(16,22),假设key=16的索引为1.但现在索引1的位置有两个Entry了,所以程序会对链表进行迭代。迭代的过程中,发现其中有一个Entry的key和我们要插入的键值对的key相同,所以现在会做的工作就是将newValue=22替换oldValue=16,然后返回oldValue=16.

然后我们现在再插入一个,put(33,33),key=33的索引为3,并且在链表中也不存在key=33的Entry,所以将该节点插入链表的第一个位置。

Hashtable与HashMap的简单比较

1.HashTable基于Dictionary类,而HashMap是基于AbstractMap。Dictionary是任何可将键映射到相应值的类的抽象父类,而AbstractMap是基于Map接口的实现,它以最大限度地减少实现此接口所需的工作。

2.HashMap的key和value都允许为null,而Hashtable的key和value都不允许为null。HashMap遇到key为null的时候,调用putForNullKey方法进行处理,而对value没有处理;Hashtable遇到null,直接返回NullPointerException。

3.Hashtable方法是同步,而HashMap则不是。我们可以看一下源码,Hashtable中的几乎所有的public的方法都是synchronized的,而有些方法也是在内部通过synchronized代码块来实现。所以有人一般都建议如果是涉及到多线程同步时采用HashTable,没有涉及就采用HashMap,但是在Collections类中存在一个静态方法:synchronizedMap(),该方法创建了一个线程安全的Map对象,并把它作为一个封装的对象来返回。

Hashtable
synchronized
HashMap
HashMap
Hashtable
HashMap
LinkedHashMap
HashMap
LinkedHashMap
Hashtable
HashMap
ConcurrentHashMap
https://blog.csdn.net/Alexshi5/article/details/79659631