1. Searching and Binary Search Trees CSCI 3333 Data Structures

2. Acknowledgement  Dr. Yue  Mr. Charles Moen  Dr. Wei Ding  Ms. Krishani Abeysekera  Dr. Michael Goodrich  Dr. Richard F. Gilberg

3. Searching  Google: era of searching!  Many type of searching: Search engines: best match. Key searching: searching for records with specified key values.  Keys are usually assumed to come from a total order (e.g. integers or strings)  Keys are usually assumed to be unique. No two records have the same key.

4. Naïve Linear Search Algorithm LinearSearch(r, key) Input: r: an array of record. Each record contains a unique key. key: key to be found. Output: i: the index such that r[i] contains the key, or -1 if not found. foreach element in r with index i if r[i].key = key return i; end foreach return -1

5. Time Complexity OperationsAverage Case Worst Case FindO(n) InsertO(1) DeleteO(n) Sorted order traversal Need to sort first

6. Naïve Linear Search  Linear time in finding!  Only suitable when n is very small.  No ‘pre-processing’.

7. Binary Search  Assume the array has already been sorted in ascending key value.  Example: find(7) 13457 8 91114161819 1 3 457891114161819 134 5 7891114161819 1345 7 891114161819 0 0 0 0 m l h m l h m l h l  m  h

8. Binary Search Algorithm Algorithm BinarySearch(r, key) Input: r: an array of record sorted by its key values. Each record contains a unique key. key: key to be found. Output: i: the index such that r[i] contains the key, or -1 if not found. low = 0; high = r.size – 1 while (low <= high) do mid = (high – low) / 2 if r[mid].key > key then high = mid-1 if r[mid].key = key then return mid; if r[mid].key < key then low = mid + 1 end while return -1;

9. Time Complexity OperationsAverage Case Worst Case FindO(lg n) InsertO(n) DeleteO(n) Sorted order traversal O(n)

10. Binary Search  O(lg n) in finding!  Especially suitable for relatively static sets of records.  Question: can we find a searching algorithm of O(1)?  Question: can we find a searching algorithm of O(lg N), where the time complexity for insert and delete is better? Average case Worst case

11. Binary Search Trees  A binary search tree (BST) is a binary tree which has the following properties: Each node has a unique key value. The left subtree of a node contains only values less than the node's value. The right subtree of a node contains only key values greater than the node's value. Each subtree is itself a binary search tree. Each subtree is itself a binary search tree.

12. BST  An inorder traversal of a binary search trees visits the keys in increasing order.  Note that there are variations in the formulation of BST. The one defined in the textbook is somewhat different and is best mapped to implement a dictionary. Duplicate key values allowed. Records stored in leaf nodes. Leaf nodes store no key.  The version of BST used here is more basic, easier to understand, and more popular.

13. Searching a BST  Similar to binary search.  Each key comparison with a node will result in one of these: target search left subtree. target = node key => found! target > node key => search right subtree.  Difference with binary search: do not always eliminate about half of the candidates. The BST may not be balanced.

14. BST Find Algorithm (Recursive) Algorithm Find(r, target) Input: r: root of a BST. target: key to be found. Output: p: the node in the BST that contains the target; null if not found. if r = null return null; if target = r.key return r; if target < r.key return find(r.leftChild(),target) else // target > r.key return find(r.rightChild(),target) end if

15. BST Find Algorithm (Iterative) Algorithm Find(r, target) Input: r: root of a BST. target: key to be found. Output: p: the node in the BST that contains the target; null if not found. result = r while result != null do if target = result.key return result; if target < result.key then result = result.leftChild() else // target > r.key result = result.rightChild() end if end while return result

16. Time Complexity  Depend on the shape of the BST.  Worst case: O(n) for unbalanced trees.  Average case: O(lg N)

17. BST Insert  Insert a record rec with key rec.key.  Idea: search for rec.key in the BST.  If found => error (keys are supposed to be unique)  If not found => ready to insert (however, need to remember the parent node for insertion).

18. BST Insert Algorithm (Iterative) Algorithm Insert(r, rec) Input: r: root of a BST. rec: record to be found. Output: whether the insertion is successful. Side Effect: r may be updated. curr = r parent = null //find the location for insertion. while (curr != null) do if curr.key = rec.key then return false // duplicate key: unsuccessful insertion else if rec.key < curr.key then curr = curr.leftChild() parent = curr else // rec.key > curr.key curr = curr.rightChild() parent = curr end if end while

19. BST Insert (Continue) // create new node for inserrtion newNode = new Node(rec) newNode.parent = parent // Insertion. if parent = null then r = newNode; else if rec.key < parent.key then parent.leftChild = newNode else parent.rightChild = newNode end if return true

20. BST Delete  More complicated Cannot delete a parent without arranging for the children! Who take care of the children? Grandparent. If there is no grandparent? The root is deleted and a child will become the root. Need to keep track of the parent of the node to be deleted.

21. BST Delete  Find the node with the key to be deleted.  If not found, deletion is not successful.  Deletion of the node p: No child: simple deletion Single child: connect the parent of p to the single child of p. Both children:  Connect the parent of p to the right child of p.  Connect the left child of p as the left child of the immediate successor of p.

22. BST Delete Algorithm Algorithm Delete(r, target) Input: r: root of a BST. target: key of the record to be found. Output: whether the deletion is successful. Side Effect: r is updated if the root node is deleted. curr = r; parent = null //find the location for insertion. while (curr != null and curr.key != target) do if target < curr.key then curr = curr.left parent = curr else // target > curr.key curr = curr.right parent = curr end if end while

23. BST Delete Algorithm (Continue) if curr = null then return false; // not found end if if curr.right = null then // no right child if parent = null then // root deleted r = r.left else if target < parent.key then parent.left = curr.left else parent.right = curr.left end if else

24. BST Delete Algorithm (Continue) // has right child if curr.left = null then // has a right child but no left child if parent = null then // root deleted r = r.right else if target < parent.key then parent.left = curr.right else parent.right = curr.right end if else

25. BST Delete Algorithm (Continue) // has both left and right children // find immediate successor. immedSucc = curr.right while (immedSucc.left != null) immedSucc = immedSucc.left end while immedSucc.left = curr.left

26. BST Delete Algorithm (Continue) if parent = null then // root deleted r = r.right else if target < parent.key then parent.left = curr.right else parent.right = curr.right end if delete curr end if

27. Time Complexity  Worst case: O(N)  Result in unbalanced tree.

28. Time Complexity (general BST) OperationsAverage Case Worst Case FindO(lg n)O(n) InsertO(lg n)O(n) DeleteO(lg n)O(n) Sorted order traversal O(n)

29. Unbalanced BST  Two approaches: Reorganization when the trees become very unbalanced. Use balanced BST  Balanced BST Many potential definitions. Height = O(lg n)

30. Time Complexity (balanced BST) OperationsAverage Case Worst Case FindO(lg n) InsertO(lg n) DeleteO(lg n) Sorted order traversal O(n)O(lg n)

31. Questions and Comments?