Binary Search Trees. A Generic Tree. Binary Trees. Nodes in a binary search tree ( BST) are of the form. P parent. Key. Satellite data L R


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1 Binary Search Trees A Generic Tree Nodes in a binary search tree ( BST) are of the form P parent Key A Satellite data L R B C D E F G H I J The BST has a root node which is the only node whose parent pointer is NIL. K L Binary Trees Binary tree is a root left subtree (maybe empty) right subtree (maybe empty) Properties max # of leaves: max # of nodes: average height for N nodes: Representation: D B A E G C F H 1.Is this binary tree complete? Why not? (C has just one child, right side is much deeper than left). What s the maximum # of leaves a binary tree of depth d/height h can have? d 3.What s the max # of nodes a binary tree of depth d/height h can have? d Minimum? d ; d 4.We won t go into this, but if you take N nodes and assume all distinct trees of the nodes are equally likely, you get an average depth/height of SQRT(N). D B I A E G C F J H left pointer Data right pointer I J Is that bigger or smaller than log n? Bigger, so it s not good enough! We will see we need to impose structure to get the bounds we want
2 Representation Implementations of Binary Trees A representation of a binary tree data node is similar to a doubly linked list in that it has two pointers. A left right pointer pointer B A C The graphic of a node shows a data area, and a left and right pointer. Some trees only store data in the leaf nodes. B left right pointer pointer C left right pointer pointer D E F D left right pointer pointer E left right pointer pointer F left right pointer pointer In this tree of five nodes, the following properties can be seen: Here is an example of a union binary tree implementation where the internal nodes are of a different construct than the leaf nodes. The internal nodes contain pointers, and a data element representing a mathematical operator. The leaf nodes are data nodes only, and contain the operands. there are nodes 3 internal leaf nodes there are a total of pointers 6 of the pointers are null pointers 4 * x * ( * x + a)  c For the above equation: Postorder traversal of the above tree will regenerate the postfix notation of the equation. Preorder traversal regenerates prefix notation Inorder traversal regenerates the form depicted to the left of the tree
3 Binary Search Tree Dictionary Data Structure Every BST satisfies the BST property Binary tree property each node has children result: storage is small operations are simple average depth is small normally Search tree property all keys in left subtree smaller than root s key all keys in right subtree larger than root s key result: easy to find any given key i. If y is in the LEFT subtree of x then key [ y ] < key [x ] ii. if y is in the RIGHT subtree of x then key [ y ] > key [ x ]. This property ensures that data in a BST are stored in such a way as to satisfy the BST property. Examples Examples NEITHER IS A BINARY SEARCH TREE 1 NEITHER IS A BINARY SEARCH TREE 1
4 Binary Search Trees ( BST) The defining property of BST is that each node has left and right links pointing to another binary search tree or to external nodes ( which have no nonnil links). Compare key values in internal nodes with the search key and use result to control progress of the search. Insert ASERCGHIN into an initially empty BST Notice that each insertion follows a search miss at the bottom of the tree. Insertion is as easy to implement as Search. Run Times of BST algorithms depend on the shape of the tree Best Case: Tree is perfectly balanced ~ log n nodes from root to the bottom Worst Case : Could be n nodes from root to the bottom. Searches on BST : On average require about log n comparisons on a tree with n nodes. Proof: # of compares = 1 + distance of node to the root Adding over all nodes gives internal path length If C n = average internal path length of BST with n nodes, Sorting If look at BST in proper manner, it represents a sorted file i.e. read the tree from left to right, ignoring the level (height) of the nodes in the tree i.e. an In Order traversal of the tree ( left subtree => root => right subtree ) BST's are a dual model to quicksort : Node at root corresponds to the pivot element Traversals Many algorithms involve walking through a tree, and performing some computation at each node Walking through a tree is called a traversal Common kinds of traversal Preorder Postorder Levelorder Insert { A S E R A H C G I E N X M P E A L } into an empty BST
5 Consider the following pseudocode : InOrder_Traversal ( x) If x = Nil Then InOrder_Traversal( Left [ x ]) Print key [x ] InOrder_Traversal ( Right [ x ] ) In Order Listing What is printed if this is applied to the BST in the graph? How long does the tree traversal take? 1 0 O ( n )  time for a tree with n items Visiting each node once and printing the value An InOrder_Traversal prints the node values in monotonically increasing order. In order listing: Operations on a BST Find D in the preceding BST : Searching : = NIL
6 What happens if search for C? Maximum and Minimum : Very straightforward from the structure of the BST Tree_ Minimum ( x) While left [ x ] not null Do x left [ x ] Return x 1 Tree_ Maximum( x) While right [ x ] not null Do x right [ x ] Return x 1 0 How long does each procedure take to run? O ( h ) where h = height of the tree. Just traveling down the tree one level at a time. Successor and Predecessor : If all keys are distinct, then the successor of a node x is the node with the smallest key greater than the key [x] If all keys are distinct, then the predecessor of a node x is the node with the largest key less than the key [x] Successor and Predecessor : The structure of the BST allows determination of the successor without any comparison of keys : Tree_Successor (x) 1. If right [ x ] not null. Then return Tree_Minimum ( right [ x ]) 3. y p [ x ] 4. while y not null and x = right [ y ] do x y. y p [ y ] 6. return y What is happening in the situation when the key has no right subtree? In this case, if x has a successor then it is the lowest ancestor of x whose left child is also an ancestor of x. to find the successor, in this case, move up the tree from x until find a node that is the left child of its parent.
7 1. Find successor of 1 right [ x ] is not null so execute a call to Extract_Min on right [ 1 ] points to 18 and returns 1 = x.. Find successor to 13 i. y gets p[x] and points to node ii. y not null and x = right [ y ] iii. x set to point to node ; y set to point to 6 node iv. y not null and x = right [ y ] v. x3 set to point to 6 node ; y3 set to point to 1 node vi. y3 is not null and x3 = right [y3 ] vii. return y3 as long as move left up the subtree, we visit smaller keys our successor is the node of which we are the predecessor What is the running time? In either case follow path up the tree or down the tree (and only one of these paths) O ( h ) run time. What would code look like for the predecessor of x? Theorem : The dynamic set operations : Search, Minimum, Maximum, Successor and Predecessor can run in O ( h ) time on a BST of height h. Idea behind Insertion 1. goal of the algorithm is to find a place to insert a new node. similar to the search code but with a few twists 3. as you go keep two pointers : one to where you are ; one to where you have been ( to allow for a quick connection) 4. trace a path from the root to a null this locates where the node will go. what if there is no tree? set this new node to be the root What if the input string is : B D F H J L and no tree exists at first insert?
8 Insertion and Deletion These operations cause the dynamic set represented by the BST to change. Changes are made so that the BST property is preserved. Insertion : Deleting a Node 1. If the node is an external node, simply replace it with a NIL value. If it is an internal node, then it has 1 or children that cannot simply be orphaned they need to be reattached to the BST tree while preserving the BST property. Case 1 : node has one child : Replace the node with the value ( key) of its child Begin at the root and trace a path downward in the tree  x traces the path ; y retains the pointer to the parent of x directional choices are determined by the compare : key [x] vs key [z] until x is set to nil  nil occupies the location where z is to be stored  Running time : as with others, this is O ( h ) Case : node has two children : Find the successor ( or predecessor ) of the node to be removed replace the node with the value ( key) of the successor ( or predecessor )move to earlier cases to resolve any created orphans. Deletion This operation is a bit more complicated depends basically on whether the node to be deleted, z, has:  No children In this case, remove the node by changing its parent, p[z], by replacing z with NIL as its child  A single child Remove the child and create a spliced link from the parent, p [ z ] to the child of z  Two children A bit more complicated find the successor y that has no left child and replace the contents of z with the contents of y. In this case it s successor is the minimum in its right subtree, and so, that successor has no left children Tree_Delete ( T, z )
9 Theorem : The dynamic set operations Insert and Delete can run in O( h ) time, in a binary search tree of height h. Note : h not n Sorting : Sort ( A ) for i 1 to n do Tree_Insert ( A [ i ] ) InOrder_Traversal (root) What should you expect for a lower bound on the run time? ## Ω ( n lg n ) ### Why?  Is this a comparison based sort? Average Case Analysis ( same as Quicksort ) The algorithm is a quicksort in which the partitioning process maintains the order of the elements in each partition. Consider : given : In turn everything is compared to 3 then to 1 or 8, etc. order is different than quicksort, concept same : namely, at each level n compares, depth ~ lg n Ω ( n lg n ) running time. For a priority queue to extract the minimum : Extract_Min ( x)  returns a pointer to the Min key while left(x) = null do x left [ x ] return x *** examine the first tree ( F B ) and see what happens.
10 Deletion Lazy Deletion 1 0 Instead of physically deleting nodes, just mark them as deleted + Simpler + some adds just flip deleted flag + physical deletions done in batches + extra memory for deleted flag many lazy deletions slow finds some operations may have to be modified (e.g., min and max) Why might deletion be harder than insertion? 1 Lazy Deletion Deletion  Leaf Case Delete(1) Delete(1) Delete() 1 Delete(1) 1 Find() 0 0 Find(16) 1 1 Insert() Find(1)
11 Deletion  One Child Case Deletion  Two Child Case Delete(1) Delete() replace node with value guaranteed to be between the left and right subtrees: the successor Could we have used the predecessor instead? Deletion  Two Child Case Delete() always easy to delete the successor always has either 0 or 1 children! 0 Delete Code void delete(comparable x, Node *& p) { Node * q; if (p!= NULL) { if (p>key < x) delete(x, p>right); else if (p>key > x) delete(x, p >left); else { /* p>key == x */ if (p>left == NULL) p = p>right; else if (p>right == NULL) p = p >left; else { q = successor(p); p>key = q>key; delete(q>key, p>right); } } } }
12 Beauty is Only Θ(log n) Deep Balance Binary Search Trees are fast if they re shallow: e.g.: perfectly complete e.g.: perfectly complete except the fringe (leafs) any other good cases? Balance measure : height(left subtree)  height(right subtree) t zero everywhere perfectly balanced small everywhere balanced enough What matters here? Problems occur when one branch is much longer than the other! Balance between 1 and 1 everywhere maximum height of 1.44 log n
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