Nothing special here. It’s just a blog post for summarising my algorithm learning course. Although this was already taught in the University, it’s still god to summarize here

1. Symbol Tables

Key-value pair abstraction.

  • Insert a value with specified key.
  • Given a key, search for the corresponding value.

Example

domain name IP address
www.cs.princeton.edu 128.112.136.11
www.princeton.edu 128.112.128.15
www.yale.edu 130.132.143.21
www.harvard.edu 128.103.060.55
www.simpsons.com 209.052.165.60

Symbol Table APIs

Symbol Tables act as an associative array, associate one value with each key.

public class ST<Key, Value> {
    void put(Key key, Value, val);
    Value get(Key key);
    void delete(Key key);
    boolean contains(Key key);
    boolean isEmpty();
    int size();
    Iterable<Key> keys();
}

2. Ordered Symbol Tables

If we order the keys in Symbol table, we can support a much wider number of operations, for example min, max operations and especially range operations.

| operations                    | keys     | values  |
|-------------------------------|----------|---------|
| min() ======================> | 09:00:00 | Chicago |
|                               | 09:00:03 | Phoenix |
| get(09:00:13) ==============> | 09:00:13 | Houston |
|                               | 09:00:59 | Chicago |
|                               | 09:01:10 | Houston |
| floor(09:05:00) ============> | 09:03:13 | Chicago |
|                               | 09:10:11 | Seattle |
| select(7) ==================> | 09:10:25 | Seattle |
|                               | 09:14:25 | Phoenix |
| ============================> | 09:19:32 | Chicago |
| ============================> | 09:19:46 | Chicago |
| keys(09:15:00, 09:25:00) ===> | 09:21:05 | Chicago |
| ============================> | 09:22:43 | Seattle |
| ============================> | 09:22:54 | Seattle |
|                               | 09:25:52 | Chicago |
| ceiling(09:30:00) ==========> | 09:35:21 | Chicago |
|                               | 09:36:14 | Seattle |
| max() ======================> | 09:37:44 | Phoenix |
| size(09:15:00, 09:25:00) = 5  |          |         |
| rank(09:10:25) = 7            |          |         |

Ordered Symbol Table APIs

public class ST<Key extends Comparable<Key>, Value> {
    void put(Key key, Value val);
    Value get(Key key);
    void delete(Key key);
    boolean contains(Key key);
    boolean isEmpty();
    Key min();
    Key max();
    Key floor(Key key);
    Key ceiling(Key key);
    int rank(Key key); // number of keys less than key
    Key select(int k); // select key of rank k
    void deleteMin();
    void deleteMax();
    // range operations
    int size(); // size of the whole tree
    int size(Key lo, Key hi); // size of range
    Iterable<Key> keys(); // iterate through all keys
    Iterable<Key> keys(Key lo, Key hi); // iterate though keys in range
}

3. Binary Search Tress

Binary Search Tree is a binary tree in symmetric order. Each node has a key, and every node’s key is

  • Larger than all keys in its left subtree.
  • Sgmaller than all keys in its right subtree

BST

Using BST, we can implement all the operations of Ordered Symbol Tables quite efficiently, as long as keys are inserted in random order.

Node

A Binary Search Tree is a reference to the root Node

class Node {
   private Key key;
   private Value val;
   private Node left, right;
   public Node(Key key, Value val) {
      this.key = key;
      this.val = val;
   }
}

Get method

Return value corresponding to given key, or null if no such key. Traverse from the root node, if less, go left, if greater, go right, if equal, search hit.

public Value get(Key key) {
   Node x = root;
   while (x != null) {
      int cmp = key.compareTo(x.key);
      if      (cmp  < 0) x = x.left;
      else if (cmp  > 0) x = x.right;
      else if (cmp == 0) return x.val;
   }
   return null;
}

Insert method

Search for key, then two cases:

  • Key in tree ⇒ reset value.
  • Key not in tree ⇒ add new node.
private Node put(Node x, Key key, Value val) {
    if (x == null) return new Node(key, val);

    int cmp = key.compareTo(x.key);
    if  (cmp  < 0)
        x.left  = put(x.left,  key, val);
    else if (cmp  > 0)
        x.right = put(x.right, key, val);
    else if (cmp == 0)
        x.val = val;
    return x;
}

Floor/Ceiling method

Floor: Largest key ≤ a given key

  • Case 1: k equals the key at root
    • The floor of k is k
  • Case 2: k is less than the key at root
    • The floor of k is in the left subtree.
  • Case 3: k is greater than the key at root
    • The floor of k is in the right subtree if there is any key ≤ k in right subtree
    • Otherwise it is the key in the root.
private Node floor(Node x, Key key) {
   if (x == null) return null;
   int cmp = key.compareTo(x.key);

   if (cmp == 0)
       return x;
   if (cmp < 0)
       return floor(x.left, key);

   Node t = floor(x.right, key);
   if (t != null)
       return t;
   else
       return x;
}

Ceiling: the reverse way

Subtree counts

In each node, we store the number of nodes in the subtree rooted at that node; To implement size(), return the count at the root.

Rank. How many keys < k ?

Rank

public int rank(Key key) {
    return rank(key, root);
}

private int rank(Key key, Node x) {
   if (x == null) return 0;

   int cmp = key.compareTo(x.key);
   if      (cmp  < 0)
       return rank(key, x.left);
   else if (cmp  > 0)
       return 1 + size(x.left) + rank(key, x.right);
   else if (cmp == 0)
       return size(x.left);
}

In-Order Traversal

  • Traverse left subtree.
  • Enqueue key.
  • Traverse right subtree.
public Iterable<Key> keys() {
    Queue<Key> q = new Queue<Key>();
    inorder(root, q);
    return q;
}

private void inorder(Node x, Queue<Key> q) {
   if (x == null) return;

   inorder(x.left, q);
   q.enqueue(x.key);
   inorder(x.right, q);
}

In-Order traversal of a BST yields keys in ascending order

Lazy Deletion

To remove a node with a given key:

  • Set its value to null.
  • Leave key in tree to guide search (but don’t consider it equal in search)

Lazy Deletion

Drawback: Tombstone (memory) overload

Delete the minimum

To delete the minimum key:

  • Go left until finding a node with a null left link.
  • Replace that node by its right link.
  • Update subtree counts.
public void deleteMin() {
    root = deleteMin(root);
}

private Node deleteMin(Node x) {
   if (x.left == null) return x.right;

   x.left = deleteMin(x.left);
   x.count = 1 + size(x.left) + size(x.right);
   return x;
}

Delete a specific key using Hibbard deletion

To delete a node with key k: search for node t containing key k.

  • Case 0: 0 children Delete t by setting parent link to null Case0
  • Case 1: 1 child Delete t by replacing parent link Case1
  • Case 2: 2 children
    • Find successor x of t, find the x which can replace the position of the node t (the min of t.right).
    • Delete that node x out of the BST using the deleteMin method, but keep the node x in memory.
    • Put x in t’s spot Case2
public void delete(Key key) {
    root = delete(root, key);
}

// delete method returns the new tree after deletion
private Node delete(Node x, Key key) {
    if (x == null) return null;

    // search for key by traserving left/right
    int cmp = key.compareTo(x.key);
    if (cmp < 0)
        // set the subtree to the new tree after deletion
        x.left  = delete(x.left,  key);
    else if (cmp > 0)
        // set the subtree to the new tree after deletion
        x.right = delete(x.right, key);

    else {
        // case 0 and case 1, return null or the left/right subtree to
        // update the link in parent node
        if (x.right == null) return x.left;
        if (x.left  == null) return x.right;

        // case 2, replace with successor
        Node t = x;
        x = min(t.right);
        x.right = deleteMin(t.right);
        x.left = t.left;
    }

    // update subtree counts
    x.count = size(x.left) + size(x.right) + 1;
    return x;
}