avl trees when bad trees happen to good programmers
TRANSCRIPT
AVL Trees
When bad trees happen to good programmers.
Good tree.
Bad tree.
Bad tree.
Left and right subtrees have the same height!
For every node in the tree, the left and right subtrees of that node have the same height.
Too rigid.
AVL Trees (Adelson-Velskii and Landis) Binary search trees with an additional
property. For every node in the tree, the height of
the left and right subtrees differ by at most 1.
AVL Property
If N is a node in a binary tree, node N has AVL property if the heights of the left sub-tree and right sub-tree are equal or if they differ by 1.
Height of a Tree
Definition is same as level. Height of a tree is the length of the longest path from root to some leaf node.
Height of an empty tree is -1. Height of a single node tree is 0. Recursive definition: height(t) = 0 if number of nodes = 1
= -1 if T is empty = 1+ max(height(LT), height(RT))
otherwise
Checking Balance
0
0 0
00
0000
0 1 1 1
1
1
12
22
3
3
4
5
Examples4
6
5 8
7 9
3
2
7
9
8
4 6
5
3
3
2
6 8
7
5
4
8
6
16 23
18
12
94 5
Computing Height
height( T:bin_tree ){ if T = null then return -1 HL = height( T->Left) HR = height( T->Right) H = max( HL, HR ) + 1
return H}
Closer Inspection of “Balance” A tree is balanced if for every node in the
tree, the height of the left and right subtrees differ by at most 1.
A node is balanced if The left and right subtrees are balanced and The height of the left and right subtrees differ by at
most 1
A tree is balanced if its root node is balanced.
Checking Balance( Height, Boolean ) balance_test( T:bin_tree ){ if T = null then return (-1, true) ( HL, BL ) = balance_test( T->Left) ( HR, BR ) = balance_test( T->Right) H = max( H_L, H_R ) + 1 if (abs(HL-HR)<=1 && BL=true && BR=true) then B=true else B=false
return ( H, B )}
Operations Find, FindMin, FindMax
O(log N) time Insert
Need to maintain balance O(log N) time
Delete a little Complicated
For starters…
binary search tree permutations of 1, 2, 3
Details
Balance Factor the difference between the height of a
node’s left subtree and the height of its right subtree
Height-Balanced Trees – all of its nodes have a balance factor of 1, 0, or -1.
Examples
BF = 0
BF = -1
BF = -2
Examples
BF = 0
BF = 1
BF = 2
Inserting a value into an AVL tree
1. Follow the insertion path (if < go left, otherwise go right).
2. Remember the deepest node with a balance factor of +1 or -1.
(This is called the pivot node.)3. Insert the node at the appropriate point.
4. Recompute balance factors from the pivot node on down, including the pivot node.
5. Has the absolute value of the pivot node’s balance factor increased from 1 to 2?
Inserting a value into an AVL tree
If yes, rebalance the tree!!!
Rebalancing the tree
This has the visual effect of rotating the subtree of the pivot node.
This is also called an AVL rotation. (Betcha didn’t see that one coming! ☺)
AVL Balancing : Four Rotations
Single right
1
2
3
AVL Balancing : Four Rotations
Single right
3
2
1
2
1 3
1
2
3
Single left
2
3 1 3
2
1
Double right3
2 1
3
1 22
3
1
Double left
Let’s take a closer look at the cases
The one selected dependson the direction of the“Guilty” insertion,relative to the pivot node.
Case 1 The insertion that unbalanced the tree
occurred in the left subtree of the left child of the pivot node.
10
5 12
3 7
insert 1BF=1
BF=0BF=0
BF=0BF=0
pivotnode
Case 1
10
5 12
3 7
1
BF=1
BF=0BF=0
BF=0BF=0
BF=2
BF=1
BF=1
BF=0
Case 1 steps for AVL rotation
pivot’s left child becomes new pivot pivots left child retains its left subtree old pivot becomes the right child of new
pivot (it’s old left child) old pivot takes the right subtree of its
old left child (the new pivot now) and makes it the left subtree
old pivot retains its right subtree
Case 110
5 12
3 7
1
pivotnode
pivot’s left child becomes new pivot
Case 15
3
1
newpivotnode
pivots left child retains its left subtreepivot’s left child becomes new pivot
Case 15
3
1
newpivotnode
pivots left child retains its left subtreeold pivot becomes the right child of new pivot (it’s old left child)
10
Case 15
7
3
1
newpivotnode
old pivot becomes the right child of new pivot (it’s old left child)
10
old pivot takes the right subtree of its left child (the new pivot now) and makes it the left subtree
Case 15
127
3
1
newpivotnode
10
old pivot takes the right subtree of its left child (the new pivot now) and makes it the left subtree
old pivot retains its right subtree
Case 2 The insertion that unbalanced the tree
occurred in the right subtree of the right child of the pivot node.
Case 3 The insertion that unbalanced the tree
occurred in the right subtree of the left child of the pivot node.
3 subcases
Case 4 The insertion that unbalanced the tree
occurred in the left subtree of the right child of the pivot node.
3 subcases
Parting thoughts on AVL trees Performance really depends on the
unreliability of the input data. i.e., you need almost sorted data to make it
worth it! A lot of design time is involved. A lot of work, too! Make sure you need it!