1. A Review of Binary Search Trees Dr. Gang Qian Department of Computer Science University of Central Oklahoma

2. Objectives (Sections 10.1 and 10.2) Binary tree  Definition  Traversal Binary search tree  Definition  Tree search  Insertion  Deletion

3. Binary Tree Definition (Mathematical Structure)  A binary tree is either empty, or it consists of a node called the root together with two binary trees called the left subtree and the right subtree of the root Note  Linked implementation is natural  Other implementation is also possible

4. The concept of left and right is important for binary trees  Binary trees with two nodes  Binary trees with three nodes  Not a binary tree

5. Traversal of Binary Trees Traversal  Moving through all nodes of the binary tree, visiting each node in turn  The order of traversal should be logical At any given node in a binary tree, there are three tasks to do:  Visit the node itself (V)  Traverse its left subtree (L)  Traverse its right subtree (R)

6. There are six ways to arrange the three tasks:  V L R; L V R; L R V; V R L; R V L; R L V They are reduced to three if we always consider the left subtree before the right  Preorder: V L R  Inorder: L V R  Postorder: L R V

7. Example (Expression Trees)  Preorder traversal - a x b c  Inorder traversal a – b x c  Postorder traversal a b c x –

8. Linked Implementation of Binary Tree

9. Binary tree node class template struct Binary_node { // data members: Entry data; Binary_node *left; Binary_node *right; // constructors: Binary_node( ); Binary_node(const Entry &x); };

10. Binary Tree Class Specification template class Binary_tree { public: Binary_tree( ); bool empty( ) const; void preorder(void (*visit)(Entry &)); void inorder(void (*visit)(Entry &)); void postorder(void (*visit)(Entry &)); int size( ) const; void clear( ); int height( ) const; (continued on next slide)

11. Binary_tree (const Binary_tree &original); Binary_tree & operator = (const Binary_tree &original); ~Binary_tree( ); protected: // Add auxiliary function prototypes here. Binary_node *root; };

12. Implementation of inorder traversal  Use an auxiliary recursive function that applies to subtrees template void Binary_tree :: inorder(void (*visit)(Entry &)) /* Post: The tree has been traversed in inorder sequence. Uses: The function recursive_inorder */ { recursive_inorder(root, visit); }

13. template void Binary tree :: recursive_inorder(Binary_node *sub_root, void (*visit)(Entry &)) /* Pre: sub_root is either NULL or points to a subtree of the Binary_tree Post: The subtree has been traversed in inorder sequence Uses: The function recursive_inorder recursively */ { if (sub_root != NULL) { recursive_inorder(sub_root->left, visit); (*visit)(sub_root->data); recursive_inorder(sub_root->right, visit); }

14. Binary Search Tree Motivation  Binary search O(log n) is much more efficient than sequential search O(n) We can use a contiguous implementation We cannot use linked list implementation What if the data needs frequent updates  Need an implementation for ordered lists that searches quickly (as with binary search on a contiguous list) makes insertions and deletions quickly (as with a linked list)

15. Definition  A binary search tree is a binary tree that is either empty or in which the data entry of every node has a key and satisfies the following conditions: The key of the left child of a node is less than the key of its parent node The key of the right child of a node is greater than the key of its parent node The left and right subtrees of the root are also binary search trees  Additional requirements No two entries in a binary search tree may have equal keys

17. Binary Search Tree Class The binary search tree class is derived from the binary tree class  All binary tree methods are inherited template class Search_tree: public Binary_tree { public: Error_code insert(const Record &new_data); Error_code remove(const Record &old_data); Error_code tree_search(Record &target) const; private: // Add auxiliary function prototypes here. };

18. The inherited methods include the constructors, the destructor, clear, empty, size, height, and the traversals preorder, inorder, and postorder Record class  Each record is associated with a Key  The keys can be compared with the usual comparison operators  By casting records to their corresponding keys, the comparison operators apply to records as well as to keys

19. Tree Search To search for the target, first compare it with the entry at the root of the tree.  If their keys match, then search finishes  Otherwise, depending on whether the target is smaller than or greater than the root, search goes to the left subtree or the right subtree as appropriate and repeat the search in that subtree The process is implemented by calling an auxiliary recursive function

20. Recursive auxiliary function template Binary_node *Search_tree :: search_for_node(Binary_node * sub_root, const Record &target) const { if (sub_root == NULL || sub_root->data == target) return sub_root; else if (sub_root->data < target) return search_for_node(sub_root->right, target); else return search_for_node(sub_root->left, target); }  Tail Recursion  Recursion tree will be a chain

21. Non-recursive version template Binary_node *Search_tree :: search_for_node(Binary_node * sub_root, const Record &target) const { while (sub_root != NULL && sub_root->data != target) if (sub_root->data right; else sub_root = sub_root->left; return sub_root; }

22. Public method tree_search template Error_code Search_tree :: tree_search(Record &target) const /* Post: If there is an entry in the tree whose key matches that in target, the parameter target is replaced by the corresponding record from the tree and a code of success is returned. Otherwise a code of not_present is returned. Uses: function search_for_node */ { Error_code result = success; Binary_node *found = search_for_node(root, target); if (found == NULL) result = not_present; else target = found->data; return result; }

23. Analysis of tree search  The same keys may be built into binary search trees of many different shapes

24.  If a binary search tree is nearly balanced (“bushy”), then search on a tree with n vertices will do O(log n) comparisons of keys  The bushier the tree, the smaller the number of comparisons The number of vertices between the root and the target, inclusive, is the number of comparisons needed to find the target  If the tree degenerates into a chain, then tree search becomes the same as sequential search, doing O(n) comparisons on n vertices The worst case for tree search

25.  Often impossible to predict the shape of the tree  If the keys are inserted in random order, then tree search usually performs almost as well as binary search  If the keys are inserted in sorted order into an empty tree, the degenerate case will occur

26. Insertion into A Binary Search Tree Find the location in the tree suitable to the new record

27. Insertion method  Call an auxiliary recursive function template Error_code Search_tree :: search_and_insert( Binary_node * &sub_root, const Record &new_data) { if (sub_root == NULL) { sub_root = new Binary_node (new_data); return success; } else if (new_data data) return search_and_insert(sub_root->left, new_data); else if (new_data > sub_root->data) return search_and_insert(sub_root->right, new_data); else return duplicate_error; }

28.  Public method: insert template Error_code Search_tree :: insert( const Record &new_data) { return search_and_insert(root, new_data); }  The method insert can usually insert a new node into a random binary search tree with n nodes in O(log n) steps

29. Removal from A Binary Search Tree Key Issue:  The integrity of the tree has to be kept after deletion

31. Auxiliary recursive function template Error_code Search_tree :: search_and_delete( Binary_node * &sub_root, const Record &target) /* Pre: sub_root is either NULL or points to a subtree Post: If the key of target is not in the subtree, a code of not present is returned. Otherwise, a code of success is returned and the subtree node containing target has been removed in such a way that the properties of a binary search tree is preserved. Uses: Functions search_and_delete recursively */ { if (sub_root == NULL) return not_present; else if (sub_root->data == target) { if (sub_root->right == NULL) { // No right child Binary_node *to_delete = sub_root; sub_root = sub_root->left; delete to_delete; } (continued on next slide)

32. else if (sub_root->left == NULL) { // No left child Binary_node *to_delete = sub_root; sub_root = sub_root->right; delete to_delete; } else { // subroot has two children // search for the immediate predecessor Binary_node * predecessor_node = sub_root->left; while (predecessor_node->right != NULL) { predecessor_node = predecessor_node->right; } // replace the target with the immediate predecessor sub_root->data = predecessor_node->data; // delete the redundant immediate predecessor search_and_delete(sub_root->left, sub_root->data); } (continued on next slide)

33. else if (target data) return search_and_delete(sub_root->left, target); else return search_and_delete(sub_root->right, target); return success; }

34. Public remove method template Error_code Search_tree :: remove( const Record &target) /* Post: If a Record with a key matching that of target belongs to the Search_tree, a code of success is returned and the corresponding node is removed from the tree. Otherwise, a code of not_present is returned Uses: Function search_and_delete */ { return search_and_delete(root, target); }  Uses an auxiliary recursive function that refers to the actual nodes in the tree

35. Building a Binary Search Tree Build a bushy binary search tree from sorted keys Textbook: pp. 463 -- 470

36. Random Search Trees and Optimality The average binary search tree requires approximately 1.39 times as many comparisons as a completely balanced tree.