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Chapter 6

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Chapter 6

Heapsort

Lee, Hsiu-Hui

Ack: This presentation is based on the lecture slides from Hsu, Lih-Hsing, as well as various materials from the web.

1. Sometimes the need to sort information is inherent in the application.

2. Algorithms often use sorting as a key subroutine.

3. There is a wide variety of sorting algorithms, and they use rich set of techniques.

4. Sorting problem has a nontrivial lower bound

5. Many engineering issues come to fore when implementing sorting algorithms.

Hsiu-Hui Lee

- Insertion sort :
- In place: only a constant number of elements of the input array are even sorted outside the array.

- Merge sort :
- not in place.

- Heap sort : (chapter 6)
- Sorts n numbers in place in O(n lgn)

Hsiu-Hui Lee

- Quick sort : (chapter 7)
- worst time complexity O(n2)
- Average time complexity O(n lg n)

- Decision tree model : (chapter 8)
- Lower bound O (n lg n)
- Counting sort
- Radix sort

- Order statistics

Hsiu-Hui Lee

- Thebinary heap data structure is an array object that can be viewed as a complete tree.

Parent(i)

return

Left(i)

return 2i

Right(i)

return2i+1

Hsiu-Hui Lee

- Max-heap : A[Parent(i)] A[i]
- Min-heap : A[Parent(i)]≤ A[i]
- The height of a node in a tree: the number of edges on the longest simple downward path from the node to a leaf.
- The height of a tree: the height of the root
- Theheight of a heap: O(lg n).

Hsiu-Hui Lee

- Max-Heapify
- Build-Max-Heap
- Heapsort
- Max-Heap-Insert
- Heap-Extract-Max
- Heap-Increase-Key
- Heap-Maximum

Hsiu-Hui Lee

Max-Heapify(A, i)

- To maintain the max-heap property.
- Assume that the binary trees rooted at Left(i) and Right(i) are heaps, but that A[i] may be smaller than its children, thus violating the heap property.

Hsiu-Hui Lee

Max-Heapify(A,i)

1 l Left(i)

2 r Right(i)

3 if l ≤ heap-size[A] and A[l] > A[i]

4 then largest l

5 else largest i

6 if r ≤ heap-size[A] and A[r] > A[largest]

7then largest r

8 if largest i

9 then exchange A[i] A[largest]

10 Max-Heapify(A, largest)

Alternatively O(h) (h: height)

Hsiu-Hui Lee

Max-Heapify(A,2)

-> Max-Heapify(A,4)

->Max-Heapify(A,9)

Hsiu-Hui Lee

Build-Max-Heap(A)

- To produce a max-heap from an unordered input array.
Build-Max-Heap(A)

1 heap-size[A] length[A]

2 fori length[A]/2 downto 1

3 do Max-Heapify(A, i)

Hsiu-Hui Lee

i=5

Max-Heapify(A, i)

i=4

Max-Heapify(A, i)

Hsiu-Hui Lee

i=3

Max-Heapify(A, i)

i=2

Max-Heapify(A, i)

Hsiu-Hui Lee

i=1

Max-Heapify(A, i)

Hsiu-Hui Lee

Hsiu-Hui Lee

Heapsort(A)

- To sort an array in place.
Heapsort(A)

1 Build-Max-Heap(A)

2 fori length[A] down to 2

3 do exchange A[1]A[i]

4 heap-size[A] heap-size[A]-1

5 Max-Heapify(A,1)

Hsiu-Hui Lee

Hsiu-Hui Lee

Analysis: O(n lg n)

Hsiu-Hui Lee

- A priority queue is a data structure that maintain a set S of elements, each with an associated value call a key.
- A max-priority queue support the following operations:
Insert(S, x) O(lg n)

inserts the element x into the set S.

Maximum(S) O(1)

returns the element of S with the largest key.

Hsiu-Hui Lee

Extract-Max(S)O(lg n)

removes and returns the element of S with the largest key.

Increase-Key(S,x,k) O(lg n)

increases the value of element x’s key to the new value k, where k x’s current key value

Hsiu-Hui Lee

Heap-Maximum(A)

1 returnA[1]

Hsiu-Hui Lee

Heap_Extract-Max(A)

1 if heap-size[A] < 1

2 then error “heap underflow”

3 max A[1]

4 A[1] A[heap-size[A]]

5 heap-size[A] heap-size[A] - 1

6 Max-Heapify(A, 1)

7 return max

Hsiu-Hui Lee

Heap-Increase-Key (A, i, key)

1 if key < A[i]

- then error “new key is smaller than
current key”

3 A[i] key

4 whilei > 1 and A[Parent(i)] < A[i]

5 do exchange A[i] A[Parent(i)]

6 i Parent(i)

Hsiu-Hui Lee

Hsiu-Hui Lee

Heap_Insert(A, key)

1 heap-size[A] heap-size[A] + 1

2 A[heap-size[A]] -∞

3 Heap-Increase-Key(A, heap-size[A], key)

Hsiu-Hui Lee