Sunday, August 7, 2016

LeetCode 380 - Insert Delete GetRandom O(1)


http://www.cnblogs.com/grandyang/p/5740864.html
Design a data structure that supports all following operations in average O(1) time.

  1. insert(val): Inserts an item val to the set if not already present.
  2. remove(val): Removes an item val from the set if present.
  3. getRandom: Returns a random element from current set of elements. Each element must have the same probability of being returned.

Example:
// Init an empty set.
RandomizedSet randomSet = new RandomizedSet();

// Inserts 1 to the set. Returns true as 1 was inserted successfully.
randomSet.insert(1);

// Returns false as 2 does not exist in the set.
randomSet.remove(2);

// Inserts 2 to the set, returns true. Set now contains [1,2].
randomSet.insert(2);

// getRandom should return either 1 or 2 randomly.
randomSet.getRandom();

// Removes 1 from the set, returns true. Set now contains [2].
randomSet.remove(1);

// 2 was already in the set, so return false.
randomSet.insert(2);

// Since 1 is the only number in the set, getRandom always return 1.
randomSet.getRandom();

此题的正确解法是利用到了一个一维数组和一个哈希表,其中数组用来保存数字,哈希表用来建立每个数字和其在数组中的位置之间的映射,对于插入操作,我们先看这个数字是否已经在哈希表中存在,如果存在的话直接返回false,不存在的话,我们将其插入到数组的末尾,然后建立数字和其位置的映射。删除操作是比较tricky的,我们还是要先判断其是否在哈希表里,如果没有,直接返回false。由于哈希表的删除是常数时间的,而数组并不是,为了使数组删除也能常数级,我们实际上将要删除的数字和数组的最后一个数字调换个位置,然后修改对应的哈希表中的值,这样我们只需要删除数组的最后一个元素即可,保证了常数时间内的删除。而返回随机数对于数组来说就很简单了,我们只要随机生成一个位置,返回该位置上的数字即可

ArrayList 的本质是 Array,
so list.remove(list.size() - 1) -> time complexity is only O(1)
public class RandomizedSet {
    private HashMap<Integer, Integer> map; // value -> index
    private ArrayList<Integer> list; // value
    private Random rand;
    /** Initialize your data structure here. */
    public RandomizedSet() {
        map = new HashMap<Integer, Integer>();
        list = new ArrayList<Integer>();
        rand = new Random();
    }

    /** Inserts a value to the set. Returns true if the set did not already contain the specified element. */
    public boolean insert(int val) {
        if (map.containsKey(val)) {
            return false;
        }
        else {
            map.put(val, list.size());
            list.add(val);
            return true;
        }
    }

    /** Removes a value from the set. Returns true if the set contained the specified element. */
    public boolean remove(int val) {
        if (!map.containsKey(val)) {
            return false;
        }
        else {
            int index = map.remove(val);
            int last = list.remove(list.size() - 1);
            if (last != val) {
                list.set(index, last);
                map.put(last, index);
            }
            return true;
        }
    }

    /** Get a random element from the set. */
    public int getRandom() {
        int index = rand.nextInt(list.size());
        return list.get(index);
    }
}
X. 
    private HashMap<Integer, Integer> keyMap = null;
    private HashMap<Integer, Integer> valueMap = null;
    int count;

    /** Initialize your data structure here. */
    public RandomizedSet() {
        keyMap = new HashMap<Integer, Integer>();
        valueMap = new HashMap<Integer, Integer>();
    }
    
    /** Inserts a value to the set. Returns true if the set did not already contain the specified element. */
    public boolean insert(int val) {
        if(keyMap.containsKey(val)) {
            return false;
        } else {
            keyMap.put(val, count);
            valueMap.put(count, val);
            count = keyMap.size();
            return true;
        }
    }
    
    /** Removes a value from the set. Returns true if the set contained the specified element. */
    public boolean remove(int val) {
        if(!keyMap.containsKey(val)) {
            return false;
        } else {
            int valueKey = keyMap.get(val);
            keyMap.remove(val);
            if(valueKey != valueMap.size() - 1) {
                valueMap.put(valueKey, valueMap.get(valueMap.size() - 1));
                keyMap.put(valueMap.get(valueMap.size() - 1), valueKey);
                valueMap.remove(valueMap.size() - 1);
            } else {
                valueMap.remove(valueKey);
            }
            count = keyMap.size();
            return true;
        }
    }
    
    /** Get a random element from the set. */
    public int getRandom() {
        Random random = new Random();
        int n = random.nextInt(keyMap.size());
        return valueMap.get(n);
    }
We can use two hashmaps to solve this problem. One uses value as keys and the other uses index as the keys.
public class RandomizedSet {
 
    HashMap<Integer, Integer> map1;
    HashMap<Integer, Integer> map2;
    Random rand;
 
    /** Initialize your data structure here. */
    public RandomizedSet() {
        map1  = new HashMap<Integer, Integer>();
        map2  = new HashMap<Integer, Integer>();
        rand = new Random(System.currentTimeMillis());
    }
 
    /** Inserts a value to the set. Returns true if the set did not already contain the specified element. */
    public boolean insert(int val) {
        if(map1.containsKey(val)){
            return false;
        }else{
            map1.put(val, map1.size());
            map2.put(map2.size(), val);
        }
        return true;
    }
 
    /** Removes a value from the set. Returns true if the set contained the specified element. */
    public boolean remove(int val) {
        if(map1.containsKey(val)){
            int index = map1.get(val);
 
            //remove the entry from both maps
            map1.remove(val);
            map2.remove(index);
 
            if(map1.size()==0){
                return true;
            }
 
            //if last is deleted, do nothing 
            if(index==map1.size()){
                return true;
            }    
 
            //update the last element's index     
            int key1 = map2.get(map2.size());
 
            map1.put(key1, index);
            map2.remove(map2.size());
            map2.put(index, key1);
 
        }else{
            return false;
        }
 
        return true;
    }
 
    /** Get a random element from the set. */
    public int getRandom() {
        if(map1.size()==0){
            return -1; 
        }
 
        if(map1.size()==1){
            return map2.get(0);    
        }    
 
        return map2.get(new Random().nextInt(map1.size()));
        //return 0;
    }
}
X. Follow up
https://discuss.leetcode.com/topic/53216/java-solution-using-a-hashmap-and-an-arraylist-along-with-a-follow-up-131-ms/4
How do you modify your code to allow duplicated number?
The follow-up: allowing duplications.
For example, after insert(1), insert(1), insert(2), getRandom() should have 2/3 chance return 1 and 1/3 chance return 2.
Then, remove(1), 1 and 2 should have an equal chance of being selected by getRandom().
The idea is to add a set to the hashMap to remember all the locations of a duplicated number.
public class RandomizedSet {
     ArrayList<Integer> nums;
     HashMap<Integer, Set<Integer>> locs;
     java.util.Random rand = new java.util.Random();
     /** Initialize your data structure here. */
     public RandomizedSet() {
         nums = new ArrayList<Integer>();
         locs = new HashMap<Integer, Set<Integer>>();
     }
     
     /** Inserts a value to the set. Returns true if the set did not already contain the specified element. */
     public boolean insert(int val) {
         boolean contain = locs.containsKey(val);
         if ( ! contain ) locs.put( val, new HashSet<Integer>() ); 
         locs.get(val).add(nums.size());        
         nums.add(val);
         return ! contain ;
     }
     
     /** Removes a value from the set. Returns true if the set contained the specified element. */
     public boolean remove(int val) {
         boolean contain = locs.containsKey(val);
         if ( ! contain ) return false;
         int loc = locs.get(val).iterator().next();
         if (loc < nums.size() - 1 ) {
             int lastone = nums.get(nums.size() - 1 );
             nums.set( loc , lastone );
             locs.get(lastone).remove(nums.size() - 1);
             locs.get(lastone).add(loc);
         }
         nums.remove(nums.size() - 1);
         locs.get(val).remove(loc);
         if (locs.get(val).isEmpty()) locs.remove(val);
         return true;
     }
     
     /** Get a random element from the set. */
     public int getRandom() {
         return nums.get( rand.nextInt(nums.size()) );
     }
 }
https://reeestart.wordpress.com/2016/06/10/google-design-a-data-structure-to-support-add-get-set-delete-getrandom-in-o1-time/
2 Hash Table + 1 List
valueMap<Integer, Integer> stores key-value pair
indexMap<Integer, Integer> stores key-index pair, where index is the index in the List. This hash table is to support delete operation in O(1) time.
List is for getRandom() method.
There is another tricky place in Delete operation. Instead of compare the trade off between ArrayList and LinkedList, maintain a variable N to store the number of keys in this data structure, whenever a delete operation is executed, just swap the keys with the last one and decrement N by one. In this way, delete operation is guaranteed in Constant time.
class RandomizedHashTable {
   
  Map<Integer, Integer> valueMap = new HashMap<>();
  Map<Integer, Integer> indexMap = new HashMap<>();
  List<Integer> keys = new ArrayList<>();
  int N = 0;
   
  /* Insert a key-value */
  public void insert(int key, int val) {
    if (valueMap.containsKey(key)) {
      return;
    }
     
    valueMap.put(key, val);
    keys.add(key);
    indexMap.put(key, keys.size() - 1);
    N++;
  }
   
  /* Get the value */
  public int get(int key) {
    if (!valueMap.containsKey(key)) {
      throw new IllegalArgumentException();
    }
     
    return valueMap.get(key);
  }
   
  /* Update the value */
  public void set(int key, int val) {
    if (!valueMap.containsKey(key)) {
      return;
    }
     
    valueMap.put(key, val);
  }
   
  /* Get a random value */
  public int getRandom() {
    int index = new Random().randInt(keys.size());
    return valueMap.get(keys.get(index));
  }
   
  /* Delete a key-value */
  public void delete(int key) {
    if(!valueMap.containsKey(key)) {
      return;
    }
     
    valueMap.remove(key); // ALSO indexMap.remove(key);
    int index = indexMap.get(key);
    int last = keys.get(N - 1);
    indexMap.put(last, index);
    swap(keys, index, N - 1);
    N--;
  }
   
  private void swap(List<Integer> keys, int l, int r) {
    int tmp = keys.get(l);
    keys.set(l, keys.get(r);
    keys.set(r, tmp);
  }
}

https://reeestart.wordpress.com/2016/06/14/google-design-a-data-structure-to-support-insert-delete-medium-and-mode/
Design a data structure to support insert(), delete(), medium() and mode()
mode() 是众数,出现最多的那个。
BST + Heap
class TNode {
int val;
int freq;
int leftSize;
}
insert() O(logn)
delete() O(logn)
medium() O(logn)
mode() O(1)
Use a PriorityQueue to on TNode.freq to get mode number.


No comments:

Post a Comment

Labels

GeeksforGeeks (959) Algorithm (811) LeetCode (639) to-do (598) Review (343) Classic Algorithm (334) Classic Interview (299) Dynamic Programming (263) Google Interview (233) LeetCode - Review (229) Tree (146) POJ (137) Difficult Algorithm (136) EPI (127) Different Solutions (118) Bit Algorithms (110) Cracking Coding Interview (110) Smart Algorithm (109) Math (91) HackerRank (85) Lintcode (83) Binary Search (73) Graph Algorithm (73) Greedy Algorithm (61) Interview Corner (61) List (58) Binary Tree (56) DFS (56) Algorithm Interview (53) Advanced Data Structure (52) Codility (52) ComProGuide (52) LeetCode - Extended (47) USACO (46) Geometry Algorithm (45) BFS (43) Data Structure (42) Mathematical Algorithm (42) ACM-ICPC (41) Interval (38) Jobdu (38) Recursive Algorithm (38) Stack (38) String Algorithm (38) Binary Search Tree (37) Knapsack (37) Codeforces (36) Introduction to Algorithms (36) Matrix (36) Must Known (36) Beauty of Programming (35) Sort (35) Array (33) Trie (33) prismoskills (33) Segment Tree (32) Space Optimization (32) Union-Find (32) Backtracking (31) HDU (31) Google Code Jam (30) Permutation (30) Puzzles (30) Array O(N) (29) Data Structure Design (29) Company-Zenefits (28) Microsoft 100 - July (28) to-do-must (28) Random (27) Sliding Window (26) GeeksQuiz (25) Logic Thinking (25) hihocoder (25) High Frequency (23) Palindrome (23) Algorithm Game (22) Company - LinkedIn (22) Graph (22) Queue (22) DFS + Review (21) Hash (21) TopCoder (21) Binary Indexed Trees (20) Brain Teaser (20) CareerCup (20) Company - Twitter (20) Pre-Sort (20) Company-Facebook (19) UVA (19) Probabilities (18) Follow Up (17) Codercareer (16) Company-Uber (16) Game Theory (16) Heap (16) Shortest Path (16) String Search (16) Topological Sort (16) Tree Traversal (16) itint5 (16) Iterator (15) Merge Sort (15) O(N) (15) Bisection Method (14) Difficult (14) Number (14) Number Theory (14) Post-Order Traverse (14) Priority Quieue (14) Amazon Interview (13) BST (13) Basic Algorithm (13) Codechef (13) Majority (13) mitbbs (13) Combination (12) Computational Geometry (12) KMP (12) Long Increasing Sequence(LIS) (12) Modify Tree (12) Reconstruct Tree (12) Reservoir Sampling (12) 尺取法 (12) AOJ (11) DFS+Backtracking (11) Fast Power Algorithm (11) Graph DFS (11) LCA (11) LeetCode - DFS (11) Ordered Stack (11) Princeton (11) Tree DP (11) 挑战程序设计竞赛 (11) Binary Search - Bisection (10) Company - Microsoft (10) Company-Airbnb (10) Euclidean GCD (10) Facebook Hacker Cup (10) HackerRank Easy (10) Reverse Thinking (10) Rolling Hash (10) SPOJ (10) Theory (10) Tutorialhorizon (10) X Sum (10) Coin Change (9) Lintcode - Review (9) Mathblog (9) Max-Min Flow (9) Stack Overflow (9) Stock (9) Two Pointers (9) Book Notes (8) Bottom-Up (8) DP-Space Optimization (8) Divide and Conquer (8) Graph BFS (8) LeetCode - DP (8) LeetCode Hard (8) Prefix Sum (8) Prime (8) System Design (8) Tech-Queries (8) Time Complexity (8) Use XOR (8) 穷竭搜索 (8) Algorithm Problem List (7) DFS+BFS (7) Facebook Interview (7) Fibonacci Numbers (7) Game Nim (7) HackerRank Difficult (7) Hackerearth (7) Interval Tree (7) Linked List (7) Longest Common Subsequence(LCS) (7) Math-Divisible (7) Miscs (7) O(1) Space (7) Probability DP (7) Radix Sort (7) Simulation (7) Suffix Tree (7) Xpost (7) n00tc0d3r (7) 蓝桥杯 (7) Bucket Sort (6) Catalan Number (6) Classic Data Structure Impl (6) DFS+DP (6) DP - Tree (6) How To (6) Interviewstreet (6) Knapsack - MultiplePack (6) Level Order Traversal (6) Manacher (6) Minimum Spanning Tree (6) One Pass (6) Programming Pearls (6) Quick Select (6) Rabin-Karp (6) Randomized Algorithms (6) Sampling (6) Schedule (6) Suffix Array (6) Threaded (6) reddit (6) AI (5) Art Of Programming-July (5) Big Data (5) Brute Force (5) Code Kata (5) Codility-lessons (5) Coding (5) Company - WMware (5) Crazyforcode (5) DFS+Cache (5) DP-Multiple Relation (5) DP-Print Solution (5) Dutch Flag (5) Fast Slow Pointers (5) Graph Cycle (5) Hash Strategy (5) Immutability (5) Inversion (5) Java (5) Kadane - Extended (5) Kadane’s Algorithm (5) Matrix Chain Multiplication (5) Microsoft Interview (5) Morris Traversal (5) Pruning (5) Quadtrees (5) Quick Partition (5) Quora (5) SPFA(Shortest Path Faster Algorithm) (5) Subarray Sum (5) Sweep Line (5) Traversal Once (5) TreeMap (5) jiuzhang (5) to-do-2 (5) 单调栈 (5) 树形DP (5) 1point3acres (4) Anagram (4) Approximate Algorithm (4) Backtracking-Include vs Exclude (4) Brute Force - Enumeration (4) Chess Game (4) Company-Amazon (4) Consistent Hash (4) Convex Hull (4) Cycle (4) DP-Include vs Exclude (4) Dijkstra (4) Distributed (4) Eulerian Cycle (4) Flood fill (4) Graph-Classic (4) HackerRank AI (4) Histogram (4) Kadane Max Sum (4) Knapsack - Mixed (4) Knapsack - Unbounded (4) Left and Right Array (4) MinMax (4) Multiple Data Structures (4) N Queens (4) Nerd Paradise (4) Parallel Algorithm (4) Practical Algorithm (4) Pre-Sum (4) Probability (4) Programcreek (4) Quick Sort (4) Spell Checker (4) Stock Maximize (4) Subsets (4) Sudoku (4) Symbol Table (4) TreeSet (4) Triangle (4) Water Jug (4) Word Ladder (4) algnotes (4) fgdsb (4) 最大化最小值 (4) A Star (3) Abbreviation (3) Algorithm - Brain Teaser (3) Algorithm Design (3) Anagrams (3) B Tree (3) Big Data Algorithm (3) Binary Search - Smart (3) Caterpillar Method (3) Coins (3) Company - Groupon (3) Company - Indeed (3) Cumulative Sum (3) DP-Fill by Length (3) DP-Two Variables (3) Dedup (3) Dequeue (3) Dropbox (3) Easy (3) Edit Distance (3) Expression (3) Finite Automata (3) Forward && Backward Scan (3) Github (3) GoLang (3) Include vs Exclude (3) Joseph (3) Jump Game (3) Knapsack-多重背包 (3) LeetCode - Bit (3) LeetCode - TODO (3) Linked List Merge Sort (3) LogN (3) Master Theorem (3) Maze (3) Min Cost Flow (3) Minesweeper (3) Missing Numbers (3) NP Hard (3) Online Algorithm (3) Pascal's Triangle (3) Pattern Match (3) Project Euler (3) Rectangle (3) Scala (3) SegmentFault (3) Stack - Smart (3) State Machine (3) Streaming Algorithm (3) Subset Sum (3) Subtree (3) Transform Tree (3) Two Pointers Window (3) Warshall Floyd (3) With Random Pointer (3) Word Search (3) bookkeeping (3) codebytes (3) Activity Selection Problem (2) Advanced Algorithm (2) AnAlgorithmADay (2) Application of Algorithm (2) Array Merge (2) BOJ (2) BT - Path Sum (2) Balanced Binary Search Tree (2) Bellman Ford (2) Binomial Coefficient (2) Bit Mask (2) Bit-Difficult (2) Bloom Filter (2) Book Coding Interview (2) Branch and Bound Method (2) Clock (2) Codesays (2) Company - Baidu (2) Complete Binary Tree (2) DFS+BFS, Flood Fill (2) DP - DFS (2) DP-3D Table (2) DP-Classical (2) DP-Output Solution (2) DP-Slide Window Gap (2) DP-i-k-j (2) DP-树形 (2) Distributed Algorithms (2) Divide and Conqure (2) Doubly Linked List (2) GoHired (2) Graham Scan (2) Graph - Bipartite (2) Graph BFS+DFS (2) Graph Coloring (2) Graph-Cut Vertices (2) Hamiltonian Cycle (2) Huffman Tree (2) In-order Traverse (2) Include or Exclude Last Element (2) Information Retrieval (2) Interview - Linkedin (2) Invariant (2) Islands (2) Knuth Shuffle (2) LeetCode - Recursive (2) Linked Interview (2) Linked List Sort (2) Longest SubArray (2) Lucene-Solr (2) MST (2) MST-Kruskal (2) Math-Remainder Queue (2) Matrix Power (2) Minimum Vertex Cover (2) Negative All Values (2) Number Each Digit (2) Numerical Method (2) Object Design (2) Order Statistic Tree (2) Palindromic (2) Parentheses (2) Parser (2) Peak (2) Programming (2) Range Minimum Query (2) Reuse Forward Backward (2) Robot (2) Rosettacode (2) Scan from right (2) Search (2) Shuffle (2) Sieve of Eratosthenes (2) SimHash (2) Simple Algorithm (2) Skyline (2) Spatial Index (2) Stream (2) Strongly Connected Components (2) Summary (2) TV (2) Tile (2) Traversal From End (2) Tree Sum (2) Tree Traversal Return Multiple Values (2) Word Break (2) Word Graph (2) Word Trie (2) Young Tableau (2) 剑指Offer (2) 数位DP (2) 1-X (1) 51Nod (1) Akka (1) Algorithm - How To (1) Algorithm - New (1) Algorithm Series (1) Algorithms Part I (1) Analysis of Algorithm (1) Array-Element Index Negative (1) Array-Rearrange (1) Auxiliary Array (1) Auxiliary Array: Inc&Dec (1) BACK (1) BK-Tree (1) BZOJ (1) Basic (1) Bayes (1) Beauty of Math (1) Big Integer (1) Big Number (1) Binary (1) Binary Tree Variant (1) Bipartite (1) Bit-Missing Number (1) BitMap (1) BitMap index (1) BitSet (1) Bug Free Code (1) BuildIt (1) C/C++ (1) CC Interview (1) Cache (1) Calculate Height at Same Recusrion (1) Cartesian tree (1) Check Tree Property (1) Chinese (1) Circular Buffer (1) Code Quality (1) Codesolutiony (1) Company - Alibaba (1) Company - Palantir (1) Company - WalmartLabs (1) Company-Apple (1) Company-Epic (1) Company-Salesforce (1) Company-Snapchat (1) Company-Yelp (1) Compression Algorithm (1) Concurrency (1) Convert BST to DLL (1) Convert DLL to BST (1) Custom Sort (1) Cyclic Replacement (1) DFS-Matrix (1) DP - Probability (1) DP Fill Diagonal First (1) DP-Difficult (1) DP-End with 0 or 1 (1) DP-Fill Diagonal First (1) DP-Graph (1) DP-Left and Right Array (1) DP-MaxMin (1) DP-Memoization (1) DP-Node All Possibilities (1) DP-Optimization (1) DP-Preserve Previous Value (1) DP-Print All Solution (1) Database (1) Detect Negative Cycle (1) Directed Graph (1) Do Two Things at Same Recusrion (1) Domino (1) Dr Dobb's (1) Duplicate (1) Equal probability (1) External Sort (1) FST (1) Failure Function (1) Fraction (1) Front End Pointers (1) Funny (1) Fuzzy String Search (1) Game (1) Generating Function (1) Generation (1) Genetic algorithm (1) GeoHash (1) Geometry - Orientation (1) Google APAC (1) Graph But No Graph (1) Graph Transpose (1) Graph Traversal (1) Graph-Coloring (1) Graph-Longest Path (1) Gray Code (1) HOJ (1) Hanoi (1) Hard Algorithm (1) How Hash (1) How to Test (1) Improve It (1) In Place (1) Inorder-Reverse Inorder Traverse Simultaneously (1) Interpolation search (1) Interview (1) Interview - Easy (1) Interview - Facebook (1) Isomorphic (1) JDK8 (1) K Dimensional Tree (1) Knapsack - Fractional (1) Knapsack - ZeroOnePack (1) Knight (1) Kosaraju’s algorithm (1) Kruskal (1) Kruskal MST (1) Kth Element (1) Least Common Ancestor (1) LeetCode - Binary Tree (1) LeetCode - Coding (1) LeetCode - Detail (1) LeetCode - Related (1) LeetCode Diffcult (1) Linked List Reverse (1) Linkedin (1) Linkedin Interview (1) Local MinMax (1) Logic Pattern (1) Longest Common Subsequence (1) Longest Common Substring (1) Longest Prefix Suffix(LPS) (1) Manhattan Distance (1) Map && Reverse Map (1) Math - Induction (1) Math-Multiply (1) Math-Sum Of Digits (1) Matrix - O(N+M) (1) Matrix BFS (1) Matrix Graph (1) Matrix Search (1) Matrix+DP (1) Matrix-Rotate (1) Max Min So Far (1) Median (1) Memory-Efficient (1) MinHash (1) MinMax Heap (1) Monotone Queue (1) Monto Carlo (1) Multi-Reverse (1) Multiple DFS (1) Multiple Tasks (1) Next Successor (1) Offline Algorithm (1) PAT (1) Partition (1) Path Finding (1) Patience Sort (1) Persistent (1) Pigeon Hole Principle (1) Power Set (1) Pratical Algorithm (1) Probabilistic Data Structure (1) Proof (1) Python (1) Queue & Stack (1) RSA (1) Ranking (1) Rddles (1) ReHash (1) Realtime (1) Recurrence Relation (1) Recursive DFS (1) Recursive to Iterative (1) Red-Black Tree (1) Region (1) Regular Expression (1) Resources (1) Reverse Inorder Traversal (1) Robin (1) Selection (1) Self Balancing BST (1) Similarity (1) Sort && Binary Search (1) String Algorithm. Symbol Table (1) String DP (1) String Distance (1) SubMatrix (1) Subsequence (1) System of Difference Constraints(差分约束系统) (1) TSP (1) Ternary Search Tree (1) Test (1) Thread (1) TimSort (1) Top-Down (1) Tournament (1) Tournament Tree (1) Transform Tree in Place (1) Tree Diameter (1) Tree Rotate (1) Trie + DFS (1) Trie and Heap (1) Trie vs Hash (1) Trie vs HashMap (1) Triplet (1) Two Data Structures (1) Two Stacks (1) USACO - Classical (1) USACO - Problems (1) UyHiP (1) Valid Tree (1) Vector (1) Wiggle Sort (1) Wikipedia (1) Yahoo Interview (1) ZOJ (1) baozitraining (1) codevs (1) cos126 (1) javabeat (1) jum (1) namic Programming (1) sqrt(N) (1) 两次dijkstra (1) 九度 (1) 二进制枚举 (1) 夹逼法 (1) 归一化 (1) 折半枚举 (1) 枚举 (1) 状态压缩DP (1) 男人八题 (1) 英雄会 (1) 逆向思维 (1)

Popular Posts