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Binary Search Trees
1. Binary Search Trees
SDP42. Binary Trees
Binary tree isA
a root
left subtree (maybe empty)
right subtree (maybe empty)
C
D
Properties
B
E
max # of leaves:
max # of nodes:
average depth for N nodes:
Representation:
Data
F
G
I
H
J
left
right
pointer pointer
Binary Search Trees
3.
Binary Tree RepresentationA
A
left right
child child
B
B
C
left right
child child
left right
child child
D
E
F
left right
child child
left right
child child
left right
child child
D
C
E
F
Binary Search Trees
4. Dictionary ADT
Dictionary operationscreate
destroy
insert
find
delete
Donald
insert
l33t haxtor
Adrien
Rollerblade demon
Hannah
C++ guru
Adrien
find(Adrien) Dave
Rollerblade demon
Older than dirt
…
Stores values associated with userspecified
keys
values may be any (homogeneous) type
keys may be any (homogeneous) comparable
type
Binary Search Trees
5. Dictionary ADT: Used Everywhere
ArraysSets
Dictionaries
Router tables
Page tables
Symbol tables
C++ structures
…
Anywhere we need to find things fast based on a key
Binary Search Trees
6. Search ADT
Dictionary operationscreate
destroy
insert
find
delete
Donald
Adrien
insert
Adrien
Hannah
Dave
find(Adrien) …
Stores only the keys
keys may be any (homogenous) comparable
quickly tests for membership
Simplified dictionary, useful for examples (e.g. CSE 326)
Binary Search Trees
7. Dictionary Data Structure: Requirements
Fast insertionFast searching
runtime:
runtime:
Fast deletion
runtime:
Binary Search Trees
8. Naïve Implementations
unsortedarray
sorted
array
linked list
insert
O(n)
find + O(n) O(1)
find
O(n)
O(log n)
delete
find + O(1) find + O(1) find + O(1)
O(n)
(markasdeleted) (markasdeleted)
Binary Search Trees
9. Binary Search Tree Dictionary Data Structure
Binary tree propertySearch tree property
8
each node has 2 children
result:
storage is small
operations are simple
average depth is small
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
Insert/delete by changing links
5
2
11
6
4
10
7
9
12
14
13
Binary Search Trees
10. Example and CounterExample
85
5
4
8
2
1
11
7
7
6
10
18
11
4
15
20
3
BINARY SEARCH TREE
NOT A
BINARY SEARCH TREE
Binary Search Trees
21
11. Complete Binary Search Tree
Complete binary search tree(aka binary heap):
Links are completely filled,
except possibly bottom level,
which is filled lefttoright.
8
5
15
3
1
7
4
9
6
Binary Search Trees
17
12. InOrder Traversal
105
visit left subtree
visit node
visit right subtree
15
2
9
7
20
What does this guarantee
with a BST?
17 30
In order listing:
2 5 7 9 10 15 17 20 30
Binary Search Trees
13. Recursive Find
105
15
2
9
7
20
17 30
Runtime:
Bestworse case?
Worstworse case?
f(depth)?
Node *
find(Comparable key, Node * t)
{
if (t == NULL) return t;
else if (key < t>key)
return find(key, t>left);
else if (key > t>key)
return find(key, t>right);
else
return t;
}
Binary Search Trees
14. Iterative Find
105
15
2
9
7
20
17 30
Node *
find(Comparable key, Node * t)
{
while (t != NULL && t>key != key)
{
if (key < t>key)
t = t>left;
else
t = t>right;
}
return t;
}
Binary Search Trees
15. Insert
Concept:Proceed down tree
as in Find
If new key not
found, then insert a
new node at last
spot traversed
void
insert(Comparable x, Node * t)
{
if ( t == NULL ) {
t = new Node(x);
} else if (x < t>key) {
insert( x, t>left );
} else if (x > t>key) {
insert( x, t>right );
} else {
// duplicate
// handling is appdependent
}
Binary Search Trees
16. BuildTree for BSTs
Suppose the data 1, 2, 3, 4, 5, 6, 7, 8, 9 is inserted into aninitially empty BST:
in order
in reverse order
median first, then left median, right median, etc.
Binary Search Trees
17. Analysis of BuildTree
Worst case is O(n2)1 + 2 + 3 + … + n = O(n2)
Average case assuming all orderings equally likely:
O(n log n)
averaging over all insert sequences (not over all binary trees)
equivalently: average depth of a node is log n
proof: see Introduction to Algorithms, Cormen, Leiserson, & Rivest
Binary Search Trees
18. BST Bonus: FindMin, FindMax
Find minimum10
Find maximum
5
15
2
9
7
20
17 30
Binary Search Trees
19. Successor Node
Next larger nodein this node’s subtree
Node * succ(Node * t) {
if (t>right == NULL)
return NULL;
else
return min(t>right);
}
10
5
15
2
9
20
17 30
7
How many children can the successor of a node have?
Binary Search Trees
20. Predecessor Node
Next smaller nodein this node’s subtree
Node * pred(Node * t) {
if (t>left == NULL)
return NULL;
else
return max(t>left);
}
10
5
15
2
9
7
20
17 30
Binary Search Trees
21. Deletion
105
15
2
9
7
20
17 30
Why might deletion be harder than insertion?
Binary Search Trees
22. Lazy Deletion
Instead of physically deleting nodes, justmark them as deleted
+
+
+

simpler
physical deletions done in batches
some adds just flip deleted flag
extra memory for deleted flag
many lazy deletions slow finds
some operations may have to be
modified (e.g., min and max)
10
5
15
2
9
20
17 30
7
Binary Search Trees
23. Lazy Deletion
Delete(17)10
Delete(15)
5
Delete(5)
Find(9)
15
2
9
20
Find(16)
7
17 30
Insert(5)
Find(17)
Binary Search Trees
24. Deletion  Leaf Case
10Delete(17)
5
15
2
9
7
20
17 30
Binary Search Trees
25. Deletion  One Child Case
10Delete(15)
5
15
2
9
7
20
30
Binary Search Trees
26. Deletion  Two Child Case
Replace node with descendantwhose value is guaranteed to
be between left and right
subtrees: the successor
10
5
20
2
Delete(5)
9
30
7
Could we have used predecessor instead?
Binary Search Trees
27. Delete Code
void delete(Comparable key, Node *& root) {Node *& handle(find(key, root));
Node * toDelete = handle;
if (handle != NULL) {
if (handle>left == NULL) {
// Leaf or one child
handle = handle>right;
delete toDelete;
} else if (handle>right == NULL) { // One child
handle = handle>left;
delete toDelete;
} else {
// Two children
successor = succ(root);
handle>data = successor>data;
delete(successor>data, handle>right);
}
}
}
Binary Search Trees
28. Thinking about Binary Search Trees
ObservationsEach operation views two new elements at a time
Elements (even siblings) may be scattered in memory
Binary search trees are fast if they’re shallow
Realities
For large data sets, disk accesses dominate runtime
Some deep and some shallow BSTs exist for any data
Binary Search Trees
29. Beauty is Only (log n) Deep
Beauty is Only (log n) DeepBinary Search Trees are fast if they’re shallow:
perfectly complete
complete – possibly missing some “fringe” (leaves)
any other good cases?
What matters?
Problems occur when one branch is much longer than another
i.e. when tree is out of balance
Binary Search Trees
30. Dictionary Implementations
unsortedarray
insert O(n)
sorted
linked
array
list
find + O(n) O(1)
BST
find
O(log n)
O(Depth)
O(n)
O(n)
O(Depth)
delete find + O(1) find + O(1) find + O(1) O(Depth)
(markasdeleted) (markasdeleted)
BST’s looking good for shallow trees, i.e. if Depth is small (log n);
otherwise as bad as a linked list!
Binary Search Trees
31. Digression: Tail Recursion
Tail recursion: when the tail (final operation) of a functionrecursively calls the function
Why is tail recursion especially bad with a linked list?
Why might it be a lot better with a tree? Why might it
not?
Binary Search Trees
32. Making Trees Efficient: Possible Solutions
Keep BSTs shallow by maintaining “balance”AVL
trees
… also exploit mostrecentlyused (mru) info
Splay
trees
Reduce disk access by increasing branching factor
Btrees
Binary Search Trees