balanced search trees cs 302 - data structures mehmet h gunes modified from authors’ slides
TRANSCRIPT
Balanced Search Trees
CS 302 - Data StructuresMehmet H Gunes
Modified from authors’ slides
Contents
• Balanced Search Trees• 2-3 Trees• 2-3-4 Trees• Red-Black Trees• AVL Trees
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Balanced Search Trees
• Height of a binary search tree sensitive to order of insertions and removals– Minimum = log2 (n + 1)
– Maximum = n
• Various search trees can retain balance despite insertions and removals
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Balanced Search Trees
• (a) A binary search tree of maximum height; (b) a binary search tree of minimum height
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2-3 Trees
• A 2-3 tree of height 3
• A 2-3 tree is not a binary tree• A 2-3 tree never taller than a minimum-
height binary tree
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2-3 Trees
• Placing data items in nodes of a 2-3 tree– A 2-node (has two children) must contain single
data item greater than left child’s item(s) and less than right child’s item(s)
– A 3-node (has three children) must contain two data items, S and L , such that
• S is greater than left child’s item(s) and less than middle child’s item(s);
• L is greater than middle child’s item(s) and less than right child’s item(s).
– Leaf may contain either one or two data items.Data Structures and Problem Solving with C++: Walls and Mirrors, Carrano and Henry, © 2013
2-3 Trees
• Nodes in a 2-3 tree: (a) a 2-node; (b) a 3-node
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2-3 Trees
• A 2-3 tree
View Header file for a class of nodes for a 2-3 tree, Listing 19-1
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Traversing a 2-3 Tree
• Traverse 2-3 tree in sorted order by performing analogue of inorder traversal on binary tree:
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Searching a 2-3 Tree
• Retrieval operation for 2-3 tree similar to retrieval operation for binary search tree
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Searching a 2-3 Tree
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Searching a 2-3 Tree
• Possible to search 2-3 tree and shortest binary search tree with approximately same efficiency, because:– Binary search tree with n nodes cannot be shorter
than log2 (n + 1)
– 2-3 tree with n nodes cannot be taller than log2 (n + 1)
– Node in a 2-3 tree has at most two items
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Searching a 2-3 Tree
• A balanced binary search tree;
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Searching a 2-3 Tree
• a 2-3 tree with the same entries
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Searching a 2-3 Tree
• (a) The binary search tree of Figure 19-5a after inserting the sequence of values 32 through 39
• (b) the 2-3 tree of Figure 19-5 b after the same insertions
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Inserting Data into a 2-3 Tree
• After inserting 39 into the tree
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Inserting Data into a 2-3 Tree
• The steps for inserting 38 into the tree: (a) The located node has no room;
(b) the node splits; (c) the resulting tree
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Inserting Data into a 2-3 Tree
• After inserting 37 into the tree
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Inserting Data into a 2-3 Tree
• (a), (b), (c) The steps for inserting 36 into the tree
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Inserting Data into a 2-3 Tree
• (d) the resulting tree
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Inserting Data into a 2-3 Tree
• The tree after the insertion of 35, 34, and 33 into the tree
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Inserting Data into a 2-3 Tree
• Splitting a leaf in a 2-3 tree when the leaf is a (a) left child; (b) right child
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Inserting Data into a 2-3 Tree
• Splitting an internal node in a 2-3 tree when the node is a (a) left child; (b) right child
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Inserting Data into a 2-3 Tree
• Splitting the root of a 2-3 tree
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Inserting Data into a 2-3 Tree
• Summary of insertion strategy
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Inserting Data into a 2-3 Tree
• Summary of insertion strategy
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Removing Data from a 2-3 Tree
• A 2-3 tree; (b), (c), (d), (e) the steps for removing 70;
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Removing Data from a 2-3 Tree
• (f) the resulting tree;
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Removing Data from a 2-3 Tree
• (a), (b), (c) The steps for removing 100 from the tree; (d) the resulting tree
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Removing Data from a 2-3 Tree
• The steps for removing 80 from the tree
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Removing Data from a 2-3 Tree
• The steps for removing 80 from the tree
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Removing Data from a 2-3 Tree
• Results of removing 70, 100, and 80 from (a) the 2-3 tree and (b) the binary search tree
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Removing Data from a 2-3 Tree
• Algorithm for removing data from a 2-3 tree
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Removing Data from a 2-3 Tree
• Algorithm for removing data from a 2-3 tree
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Removing Data from a 2-3 Tree
• Algorithm for removing data from a 2-3 tree
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Removing Data from a 2-3 Tree
• (a) Redistributing values; (b) merging a leaf;
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Removing Data from a 2-3 Tree
• (c) redistributing values and children; • (d) merging internal nodes
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Removing Data from a 2-3 Tree
• (e) deleting the root
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2-3-4 Trees
• A 2-3-4 tree with the same data items as the 2-3 tree
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2-3-4 Trees
• Rules for placing data items in the nodes of a 2-3-4 tree– 2-node (two children), must contain a single data
item that satisfies relationships – 3-node (three children), must contain two data
items that satisfies relationships – . . .
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2-3-4 Trees– 4-node (four children) must contain three data
items S , M , and L that satisfy:• S is greater than left child’s item(s) and less than
middle-left child’s item(s)• M is greater than middle-left child’s item(s) and less
than middle-right child’s item(s);• L is greater than middle-right child’s item(s) and less
than right child’s item(s).
– A leaf may contain either one, two, or three data items
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2-3-4 Trees
• A 4-node in a 2-3-4 tree
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2-3-4 Trees
• Has more efficient insertion and removal operations than a 2-3 tree
• Has greater storage requirements due to the additional data members in its 4-nodes
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2-3-4 Trees
• Searching and Traversing a 2-3-4 Tree– Simple extensions of the corresponding algorithms
for a 2-3 tree
• Inserting Data into a 2-3-4 Tree– Insertion algorithm splits a node by moving one of
its items up to its parent node– Splits 4-nodes as soon as it encounters them on
the way down the tree from the root to a leaf
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2-3-4 Trees
• Inserting 20 into a one-node 2-3-4 tree (a) the original tree; (b) after splitting the node; (c) after inserting 20
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2-3-4 Trees
• After inserting 50 and 40 into the tree
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2-3-4 Trees
• The steps for inserting 70 into the tree : (a) after splitting the 4-node; (b) after inserting 70
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2-3-4 Trees
• After inserting 80 and 15 into the tree
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2-3-4 Trees
• The steps for inserting 90 into the tree
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2-3-4 Trees
• The steps for inserting 100 into the tree
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2-3-4 Trees
• Splitting a 4-node root during insertion into a 2-3-4 tree
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2-3-4 Trees
• Splitting a 4-node whose parent is a 2-node during insertion into a 2-3-4 tree, when the 4-node is a (a) left child; (b) right child
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2-3-4 Trees
• Splitting a 4-node whose parent is a 3-node during insertion into a 2-3-4 tree, when the 4-node is a (a) left child
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2-3-4 Trees
• Splitting a 4-node whose parent is a 3-node during insertion into a 2-3-4 tree, when the 4-node is a (b) middle child
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2-3-4 Trees
• Splitting a 4-node whose parent is a 3-node during insertion into a 2-3-4 tree, when the 4-node is a (c) right child
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2-3-4 Trees
• Removing Data from a 2-3-4 Tree– Removal algorithm has same beginning as removal
algorithm for a 2-3 tree– Locate the node n that contains the item I you
want to remove– Find I ’s inorder successor and swap it with I so
that the removal will always be at a leaf– If leaf is either a 3-node or a 4-node, remove I .
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Modified from Dr George Bebis and Dr Monica Nicolescu
Red-Black Trees
• Use a special binary search tree—a red-black tree —to represent a 2-3-4 tree
• Retains advantages of a 2-3-4 tree without storage overhead
• The idea is to represent each 3-node and 4-node as an equivalent binary search tree
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Red-Black Trees
• Red-black representation of (a) a 4-node; (b) a 3-node
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Red-Black Trees
• A red-black tree that represents the 2-3-4 tree
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Red-Black Trees
• Searching and traversing– Red-black tree is a binary search tree, search and
traverse it by using algorithms for binary search tree
• Inserting, removing with a red-black tree– Adjust the 2-3-4 insertion algorithms to
accommodate the red-black representation
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Red-Black Trees
• Splitting a red-black representation of a 4-node that is the root
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Red-Black Trees
• Splitting a red-black representation of a 4-node whose parent is a 2-node, when the 4-node is a (a) left child
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Red-Black Trees
• Splitting a red-black representation of a 4-node whose parent is a 2-node, when the 4-node is a (b) right child
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Red-Black Trees
• Splitting a red-black representation of a 4-node whose parent is a 3-node
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Red-Black Trees
• Splitting a red-black representation of a 4-node whose parent is a 3-node
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Red-Black Trees
• Splitting a red-black representation of a 4-node whose parent is a 3-node
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Red-Black-Trees Properties(**Binary search tree property is satisfied**)
1. Every node is either red or black
2. The root is black
3. Every leaf (NIL) is black
4. If a node is red, then both its children are black
• No two consecutive red nodes on a simple path from the root to a leaf
5. For each node, all paths from that node to a leaf contain the same number of black nodes
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Example: RED-BLACK-TREE
• For convenience, we add NIL nodes and refer to them as the leaves of the tree.– Color[NIL] = BLACK
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30 47
38 50
NIL NIL
NIL
NIL NIL NIL NIL
NIL
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Definitions
• Height of a node: the number of edges in the longest path to a leaf
• Black-height bh(x) of a node x: the number of black nodes (including NIL) on the path from x to a leaf, not counting x
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30 47
38 50
NIL NIL
NIL
NIL NIL NIL NIL
NIL
h = 4bh = 2
h = 3bh = 2
h = 2bh = 1
h = 1bh = 1
h = 1bh = 1
h = 2bh = 1 h = 1
bh = 1
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Height of Red-Black-Trees
A red-black tree with n internal nodes has height at most 2log(N+1)
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Insert Item
What color to make the new node?• Red?
– Let’s insert 35!
• Property 4 is violated: if a node is red, then both children are black• Black?
– Let’s insert 14!
• Property 5 is violated: all paths from a node to its leaves contain the same number of black nodes
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30 47
38 50
Delete Item
What color was the node that was removed? Red? 1. Every node is either red or black2. The root is black3. Every leaf (NIL) is black4. If a node is red, then both its children are black
5. For each node, all paths from the node to descendant leaves contain the same number of black nodes
OK!
OK!
OK!
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30 47
38 50
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OK!
OK!
Delete Item
What color was the node that was removed? Black? 1. Every node is either red or black2. The root is black3. Every leaf (NIL) is black4. If a node is red, then both its children are black
5. For each node, all paths from the node to descendant leaves contain the same number of black nodes
OK!
OK!
Not OK! Could createtwo red nodes in a row
Not OK! Could change theblack heights of some nodes
Not OK! If removing the root and the child that replaces it is red
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26
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30 47
38 50
Rotations• Operations for re-structuring the tree after
insert and delete operations– Together with some node re-coloring, they help
restore the red-black-tree property
– Change some of the pointer structure
– Preserve the binary-search tree property
• Two types of rotations:– Left & right rotations
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Left Rotations• Assumptions for a left rotation on a node x:
– The right child y of x is not NIL
• Idea:– Pivots around the link from x to y– Makes y the new root of the subtree– x becomes y ’s left child– y ’s left child becomes x ’s right child
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Example: LEFT-ROTATE
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LEFT-ROTATE(T, x)1. y ← right[x] ►Set y
2. right[x] ← left[y] ► y’s left subtree becomes x’s right subtree
3. if left[y] NIL4. then p[left[y]] ← x ► Set the parent relation from left[y] to x
5. p[y] ← p[x] ► The parent of x becomes the parent of y
6. if p[x] = NIL7. then root[T] ← y8. else if x = left[p[x]]9. then left[p[x]] ← y10. else right[p[x]] ← y11.left[y] ← x ► Put x on y’s left
12.p[x] ← y ► y becomes x’s parent
• Assumptions for a right rotation on a node x:– The left child x of y is not NIL
• Idea:– Pivots around the link from y to x– Makes x the new root of the subtree– y becomes x ’s right child– x ’s right child becomes y ’s left child
Right Rotations
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Insert Item• Goal:
– Insert a new node z into a red-black tree
• Idea:– Insert node z into the tree as for an ordinary
binary search tree– Color the node red– Restore the red-black tree properties
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RB-INSERT(T, z)1. y ← NIL
2. x ← root[T]
3. while x NIL
4. do y ← x
5. if key[z] < key[x]
6. then x ← left[x]
7. else x ← right[x]
8. p[z] ← y
• Initialize nodes x and y• Throughout the algorithm y points to the parent of x
• Go down the tree untilreaching a leaf• At that point y is theparent of the node to beinserted
• Sets the parent of z to be y
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38 50
RB-INSERT(T, z)
9. if y = NIL
10. then root[T] ← z
11. else if key[z] < key[y]
12. then left[y] ← z
13. else right[y] ← z
14. left[z] ← NIL
15. right[z] ← NIL
16. color[z] ← RED
17. RB-INSERT-FIXUP(T, z)
The tree was empty: set the new node to be the root
Otherwise, set z to be the left orright child of y, depending on whether the inserted node is smaller or larger than y’s key
Set the fields of the newly added node
Fix any inconsistencies that could have been introduced by adding this new red node
RB Properties Affected by Insert
1. Every node is either red or black
2. The root is black
3. Every leaf (NIL) is black
4. If a node is red, then both its children are black
5. For each node, all paths
from the node to descendant
leaves contain the same number
of black nodes
OK!
If z is the root not OK
OK!
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17 41
4738
50
If p(z) is red not OKz and p(z) are both redOK!
RB-INSERT-FIXUP
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Case 1: z ’s “uncle” (y) is red (z could be either left or right child)
Idea: • p[p[z]] (z ’s grandparent) must be black
• color p[z] black
• color y black
• color p[p[z]] red
• z = p[p[z]]
– Push the “red” violation up the tree
RB-INSERT-FIXUP
Case 2: • z ’s “uncle” (y) is black• z is a left child
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Case 2
Idea:• color p[z] black • color p[p[z]] red• RIGHT-ROTATE(T, p[p[z]])• No longer have 2 reds in a row• p[z] is now black
RB-INSERT-FIXUP
Case 3: • z ’s “uncle” (y) is black• z is a right childIdea:• z p[z]• LEFT-ROTATE(T, z) now z is a left child, and both z and p[z] are red case 2
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Case 3 Case 2
Example
11Insert 4
2 14
1 157
85
4
y
11
2 14
1 157
85
4
z
Case 1
y
z and p[z] are both redz’s uncle y is redz
z and p[z] are both redz’s uncle y is blackz is a right child
Case 3
11
2
14
1
15
7
8
5
4
z
y Case 2
z and p[z] are redz’s uncle y is blackz is a left child
112
141
15
7
85
4
z
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RB-INSERT-FIXUP(T, z)1. while color[p[z]] = RED
2. if p[z] = left[p[p[z]]]
3. then y ← right[p[p[z]]]
4. if color[y] = RED
5. then Case1
6. else if z = right[p[z]]
7. then Case3
8. Case2
9. else (same as then clause with “right” and “left” exchanged for lines 3-4)
10. color[root[T]] ← BLACK
The while loop repeats only whencase1 is executed: O(logN) times
Set the value of x’s “uncle”
We just inserted the root, orThe red violation reached the root
Analysis of InsertItem
• Inserting the new element into the tree O(logN)
• RB-INSERT-FIXUP– The while loop repeats only if CASE 1 is executed– The number of times the while loop can be
executed is O(logN)
• Total running time of Insert Item: O(logN)
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Delete Item• Delete as usually, then re-color/rotate
• A bit more complicated though …
• Demo– http://gauss.ececs.uc.edu/RedBlack/redblack.html
90
Problems
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• What red-black tree property is violated in the tree below? How would you restore the red-black tree property in this case?– Property violated: if a node is red, both its children are black– Fixup: color 7 black, 11 red, then right-rotate around 11
Problems
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112
141
15
7
85
4
z
Problems
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• Let a, b, c be arbitrary nodes in subtrees , , in the tree below.
• How do the depths of a, b, c change when a left rotation is performed on node x?– a: increases by 1– b: stays the same– c: decreases by 1
Problems
• When we insert a node into a red-black tree, we initially set the color of the new node to red.
Why didn’t we choose to set the color to black?
• Would inserting a new node to a red-black tree and then immediately deleting it, change the tree?
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AVL Trees
• Named for inventors– Adel’son-Vel’skii and Landis
• A balanced binary search tree– Maintains height close to the minimum– After insertion or deletion, check the tree is still
AVL tree • determine whether any node in tree has left and right
subtrees whose heights differ by more than 1
• Can search AVL tree almost as efficiently as minimum-height binary search tree.
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AVL Trees
• (a) An unbalanced binary search tree; (b) a balanced tree after rotation; (c) a balanced tree after insertion
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• (a) Before;• (b) and after a single left rotation that decreases the
tree’s height; • (c) the rotation in general
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AVL Trees
• (a) Before; • (b) and after a single left rotation that does not affect the tree’s height; • (c) the rotation in general
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AVL Trees
AVL Trees
• (d) the double rotation in general
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