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Heap Sort

A Heap is a Binary Tree

Height of tree longest path from root to

leaf Q(lgn)

A heap is a binary tree satisfying the heap

condition At every node in a heap, the node

value is gt all the values in its subtrees. A

heap with heap_size elements can be represented

as an array segment A1..heap_size

Ordering of Nodes

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Each node is filled in order from left to

right. All rows are filled except, possibly, the

last row.

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Address of left child of node i gt 2i Address of

right child of node i gt 2i1 Address of parent

of node i gt

Example Left(4) 8 Right(4)

9 Parent(4) 2

Heap properties

- At every node in the heap, that node's value is

greater than or equal to all the values in the

subtrees of that node. - Heaps can be represented as an array. The

elements in the array occur in the order of their

heap addresses.

A 16 14 10 8 7 9 3 2 4 1

Note that the array is not sorted. Can guarantee

that A1 is the maximum value in the array.

Building a Heap

- To perform a heap sort, we must first build a

heap out of our unsorted array. - To build a heap, we will use the function

heapify, which creates a heap from a node i and

two subtrees that are already heaps.

Heapify

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Example i 2 Want to move the value, 3, to a

position consistent with the heap property.

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- In English
- Compare the value at node i with the values at

each child node. - Swap the value at node i with the largest of

those values. - Repeat with the subtree that ith value was

swapped into. - Note If the value at i is in the correct place,

it will be swapped with itself and the algorithm

is done.

Pseudocode for Heapify

Heapify (A, i) lft lt- Left(i) rt lt-

Right(i) if lft lt heap-sizeA and Alft gt

Ai then largest lt- lft else largest lt-

i if rt lt heap-sizeA and Art gt

Alargest then largest lt- rt if largest ! i

then Swap( A, i, largest ) Heapify (A,

largest)

Final Tree from example

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i2

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Can show that heapify runs in O(lgn) time. (Why?)

Building a Heap

- Each leaf of a binary tree is already a heap of

size 1. - Build-Heap will start with the highest addressed,

non-leaf node and heapify it. It then repeats

with each node addressed 1 less than the previous

node. - In a binary tree, the leaf nodes have addresses
- We want to heapify starting with node

Build-Heap(A) heap-size lt- lengthA for i lt-

lengthA / 2 downto 1 do Heapify(A, i)

Example

A 4 7 16 3 27 9 10 34 1 25

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We will work this out in class.

Heap Sort

- In English
- We know that the maximum value is at A1 in a

heap. - We put that value at the end of the array, by

swapping it with the last element of the heap. - We then reduce the size of the heap by 1, so

that the sorted element is no longer part of the

heap. - Heapify the new, smaller heap (A1 is not

necessarily in the correct position for a heap).

This will put a new maximum at the top. - Repeat this process until all the nodes have

been sorted.

Pseudocode for Heapsort

Heapsort(A) Build-Heap(A) for i lt- lengthA

downto 2 do Swap(A, 1, i) heap-sizeA lt-

heap-sizeA - 1 Heapify(A, 1)

What is the running time of Heapsort? Must find

running times of Build-Heap and Heapify first.

Recall Heapify

Heapify( ) is a function that creates a heap from

a node i and two subtrees that are already heaps.

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Example i 2

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i2

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Swap the value at node i with the largest of the

values at child nodes. If value at node i is

larger than those of children, stop. Otherwise,

continue with the next level.

Running time of Heapify

Maximum number of swaps in Heapify is the height

of the heap.

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Height of n element heap is

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Therefore, the running time of heapify is O(lgn)

Running time of Build heap

Recall that Build-Heap starts with the last

element and repeatedly calls Heapify to create

heaps from the bottom up.

If the height of a node is given by the longest

distance from a leaf to that node, then the

number of nodes at a given height h is at most

Why?

Build-Heap calls heapify for each node at a given

height

T(n) ?

number of nodes at height h

cost of Heapify at height h

Cost of Heap-Sort

Heapsort(A) Build-Heap(A) for i lt- lengthA

downto 2 do Swap(A, 1, i) heap-sizeA lt-

heap-sizeA - 1 Heapify(A, 1)

O(n) n nconst nconst nO(lgn)

T(n) O(n) nO(lgn) O(nlgn)

Using a Heap in Priority Queues

A priority queue is a queue in which the element

with the highest value is retrieved first. WIPO

Whatever in, Priority out. Priority Queue

Operations Insert(S, x) Inserts element x into

set S Maximum(S) Returns the element of S with

the largest value (or key) Extract-Max(S) Removes

and returns the element of S with the largest

value. Heaps are useful data structures for

priority queue functions.

Extracting the Maximum

Idea Extract the maximum element, reduce the

heap size by 1, then heapify.

Heap-Extract-Max(A) if heap_sizeA lt 1 then

error "heap underflow" max lt- A1 root

value is max A1 lt- Aheap_sizeA put

Aheap_sizeA in root heap_sizeA lt-

heap_sizeA - 1 decrease heap size

Heapify(A, 1) enforce heap property

return max

Example

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We will work this through in class.

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Heap-Insert

Idea To insert an element at the proper place in

the heap, traverse the tree from leaf to root and

find the correct place.

HeapInsert(A, key) heap_sizeA lt-

heap_sizeA 1 increase size of heap i lt-

heap-sizeA while i gt 1 and AParent(i) lt key

do Ai lt- AParent(i) i lt-

Parent(i) Ai lt- key

Example

Insert the number, 15. We will work through this

in class.

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Binary Heap Definition

- Binary heap.
- Almost complete binary tree.
- filled on all levels, except last, where filled

from left to right - Min-heap ordered.
- every child greater than (or equal to) parent

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Binary Heap Properties

- Properties.
- Min element is in root.
- Heap with N elements has height ?log2 N?.

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N 14Height 3

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Binary Heaps Array Implementation

- Implementing binary heaps.
- Use an array no need for explicit parent or

child pointers. - Parent(i) ?i/2?
- Left(i) 2i
- Right(i) 2i 1

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Binary Heap Insertion

- Insert element x into heap.
- Insert into next available slot.
- Bubble up until it's heap ordered.
- Peter principle nodes rise to level of

incompetence

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Binary Heap Insertion

- Insert element x into heap.
- Insert into next available slot.
- Bubble up until it's heap ordered.
- Peter principle nodes rise to level of

incompetence

swap with parent

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Binary Heap Insertion

- Insert element x into heap.
- Insert into next available slot.
- Bubble up until it's heap ordered.
- Peter principle nodes rise to level of

incompetence

swap with parent

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14

45

78

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47

77

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81

84

83

64

99

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Binary Heap Insertion

- Insert element x into heap.
- Insert into next available slot.
- Bubble up until it's heap ordered.
- Peter principle nodes rise to level of

incompetence - O(log N) operations.

stop heap ordered

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Binary Heap Decrease Key

- Decrease key of element x to k.
- Bubble up until it's heap ordered.
- O(log N) operations.

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Binary Heap Delete Min

- Delete minimum element from heap.
- Exchange root with rightmost leaf.
- Bubble root down until it's heap ordered.
- power struggle principle better subordinate is

promoted

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Binary Heap Delete Min

- Delete minimum element from heap.
- Exchange root with rightmost leaf.
- Bubble root down until it's heap ordered.
- power struggle principle better subordinate is

promoted

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Binary Heap Delete Min

- Delete minimum element from heap.
- Exchange root with rightmost leaf.
- Bubble root down until it's heap ordered.
- power struggle principle better subordinate is

promoted

exchange with left child

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78

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91

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84

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Binary Heap Delete Min

- Delete minimum element from heap.
- Exchange root with rightmost leaf.
- Bubble root down until it's heap ordered.
- power struggle principle better subordinate is

promoted

exchange with right child

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53

42

78

18

45

47

77

91

81

84

83

64

99

Binary Heap Delete Min

- Delete minimum element from heap.
- Exchange root with rightmost leaf.
- Bubble root down until it's heap ordered.
- power struggle principle better subordinate is

promoted - O(log N) operations.

stop heap ordered

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Binary Heap Heapsort

- Heapsort.
- Insert N items into binary heap.
- Perform N delete-min operations.
- O(N log N) sort.
- No extra storage.

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