sorting algos > data structures & algorithums
DESCRIPTION
TRANSCRIPT
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Merge Sort
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Merge SortMerge sort is based
on the divide-and-conquer paradigm. It consists of three steps:Divide: partition input
sequence S into two sequences S1 and S2 of about n2 elements each
Recur: recursively sort S1 and S2
Conquer: merge S1 and S2 into a unique sorted sequence
Algorithm mergeSort(S, C)Input sequence S, comparator C Output sequence S sorted
according to Cif S.size() > 1 {
(S1, S2) := partition(S, S.size()/2)
S1 := mergeSort(S1, C)
S2 := mergeSort(S2, C)
S := merge(S1, S2)} return(S)
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Divide-and-Conquer
Merge Sort Execution Tree (recursive calls)An execution of merge-sort is depicted by a binary
treeeach node represents a recursive call of merge-sort and stores
unsorted sequence before the execution and its partition sorted sequence at the end of the execution
the root is the initial call the leaves are calls on subsequences of size 0 or 1
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Divide-and-Conquer
Execution ExamplePartition
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7 2 2 7 9 4 4 9 3 8 3 8 6 1 1 6
7 7 2 2 9 9 4 4 3 3 8 8 6 6 1 1
7 2 9 4 3 8 6 1 1 2 3 4 6 7 8 9
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Divide-and-Conquer
Execution Example (cont.)Recursive call, partition
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7 2 2 7 9 4 4 9 3 8 3 8 6 1 1 6
7 7 2 2 9 9 4 4 3 3 8 8 6 6 1 1
7 2 9 4 3 8 6 1 1 2 3 4 6 7 8 9
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Execution Example (cont.)Recursive call, partition
7 2 9 4 2 4 7 9 3 8 6 1 1 3 8 6
7 2 2 7 9 4 4 9 3 8 3 8 6 1 1 6
7 7 2 2 9 9 4 4 3 3 8 8 6 6 1 1
7 2 9 4 3 8 6 1 1 2 3 4 6 7 8 9
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Divide-and-Conquer
Execution Example (cont.)Recursive call, base case
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7 2 2 7 9 4 4 9 3 8 3 8 6 1 1 6
7 7 2 2 9 9 4 4 3 3 8 8 6 6 1 1
7 2 9 4 3 8 6 1 1 2 3 4 6 7 8 9
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Divide-and-Conquer
Execution Example (cont.)Recursive call, base case
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7 2 2 7 9 4 4 9 3 8 3 8 6 1 1 6
7 7 2 2 9 9 4 4 3 3 8 8 6 6 1 1
7 2 9 4 3 8 6 1 1 2 3 4 6 7 8 9
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Execution Example (cont.)Merge
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7 2 2 7 9 4 4 9 3 8 3 8 6 1 1 6
7 7 2 2 9 9 4 4 3 3 8 8 6 6 1 1
7 2 9 4 3 8 6 1 1 2 3 4 6 7 8 9
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Divide-and-Conquer
Execution Example (cont.)Recursive call, …, base case, merge
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7 2 2 7 9 4 4 9 3 8 3 8 6 1 1 6
7 7 2 2 3 3 8 8 6 6 1 1
7 2 9 4 3 8 6 1 1 2 3 4 6 7 8 9
9 9 4 4
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Execution Example (cont.)Merge
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7 2 2 7 9 4 4 9 3 8 3 8 6 1 1 6
7 7 2 2 9 9 4 4 3 3 8 8 6 6 1 1
7 2 9 4 3 8 6 1 1 2 3 4 6 7 8 9
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Divide-and-Conquer
Execution Example (cont.)Recursive call, …, merge, merge
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7 2 2 7 9 4 4 9 3 8 3 8 6 1 1 6
7 7 2 2 9 9 4 4 3 3 8 8 6 6 1 1
7 2 9 4 3 8 6 1 1 2 3 4 6 7 8 9
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Divide-and-Conquer 14
Execution Example (cont.)Merge
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Static Method mergeSort()Public static void mergeSort(a, int left, int right){// sort a[left:right]if (left < right){// at least two elements int mid = (left+right)/2; //midpoint mergeSort(a, left, mid); mergeSort(a, mid + 1, right); merge(a, b, left, mid, right); //merge from a to b copy(b, a, left, right); //copy result back to a}
}
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MERGE-SORT(A,p,r)1. if lo < hi
2. then mid (lo+hi)/23. MERGE-SORT(A,lo,mid)
4. MERGE-SORT(A,mid+1,hi)
5. MERGE(A,lo,mid,hi)
Call MERGE-SORT(A,1,n) (assume n=length of list A)
A = {10, 5, 7, 6, 1, 4, 8, 3, 2, 9}
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Divide-and-Conquer
Another Analysis of Merge-SortThe height h of the merge-sort tree is O(log n)
at each recursive call we divide in half the sequence, The work done at each level is O(n)
At level i, we partition and merge 2i sequences of size n2i Thus, the total running time of merge-sort is O(n log n)
depth #seqs size Cost for level
0 1 n n
1 2 n2 n
…
i 2i n2i n
… … …
logn 2logn = n n/2logn = 1 n
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Summary of Sorting Algorithms (so far)
Vectors
Algorithm Time Notes
Selection Sort O(n2) Slow, in-placeFor small data sets
Insertion Sort O(n2) WC, ACO(n) BC
Slow, in-placeFor small data sets
Heap Sort O(nlog n) Fast, in-placeFor large data sets
Merge Sort O(nlogn) Fast, sequential data accessFor huge data sets
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Divide-and-Conquer
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Divide-and-Conquer
Quick-SortQuick-sort is a
randomized sorting algorithm based on the divide-and-conquer paradigm:Divide: pick a random
element x (called pivot) and partition S into L elements less than x E elements equal x G elements greater than x
Recur: sort L and GConquer: join L, E and G
x
x
L GE
x
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QuicksortEfficient sorting algorithm
Discovered by C.A.R. HoareExample of Divide and Conquer algorithmTwo phases
Partition phase Divides the work into half
Sort phase Conquers the halves!
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QuicksortPartition
Choose a pivotFind the position for the pivot so that
all elements to the left are less all elements to the right are greater
< pivot > pivot
pivot
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QuicksortConquer
Apply the same algorithm to each half
< pivot > pivot
pivot< p’ p’ > p’ < p” p” > p”
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78 84 93100 61 8170 65 98 78 55 68
78 84 93100 61 8170 65 98 78 55 68
78 84 93100 61 8170 65 98 78 55 68
78 8493100 61 8170 65 98 78 5568
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78 8493100 61 8170 65 98 78 5568
78 849310061 8170 65 9878 5568
78 849310061 8170 65 9878 5568
78 849310061 8170 65 98785568
78 849310061 8170 65 987855 68
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84
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9310061 8170 65 987855 68
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QuicksortImplementation
quicksort( void *a, int low, int high ) { int pivot; /* Termination condition! */ if ( high > low ) { pivot = partition( a, low, high ); quicksort( a, low, pivot-1 ); quicksort( a, pivot+1, high ); } }
Divide
Conquer
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Quicksort - Partition
int partition( int *a, int low, int high ) { int left, right; int pivot_item; pivot_item = a[low]; pivot = left = low; right = high; while ( left < right ) { /* Move left while item < pivot */ while( a[left] <= pivot_item ) left++; /* Move right while item > pivot */ while( a[right] >= pivot_item ) right--; if ( left < right ) SWAP(a,left,right); } /* right is final position for the pivot */ a[low] = a[right]; a[right] = pivot_item; return right; }
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Quicksort - Partition
int partition( int *a, int low, int high ) { int left, right; int pivot_item; pivot_item = a[low]; pivot = left = low; right = high; while ( left < right ) { /* Move left while item < pivot */ while( a[left] <= pivot_item ) left++; /* Move right while item > pivot */ while( a[right] >= pivot_item ) right--; if ( left < right ) SWAP(a,left,right); } /* right is final position for the pivot */ a[low] = a[right]; a[right] = pivot_item; return right; }
This exampleuses int’s
to keep thingssimple!
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low high
Any item will do as the pivot,choose the leftmost one!
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Quicksort - Partition
int partition( int *a, int low, int high ) { int left, right; int pivot_item; pivot_item = a[low]; pivot = left = low; right = high; while ( left < right ) { /* Move left while item < pivot */ while( a[left] <= pivot_item ) left++; /* Move right while item > pivot */ while( a[right] >= pivot_item ) right--; if ( left < right ) SWAP(a,left,right); } /* right is final position for the pivot */ a[low] = a[right]; a[right] = pivot_item; return right; }
Set left and right markers
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low highpivot: 23
left right
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Quicksort - Partitionint partition( int *a, int low, int high ) { int left, right; int pivot_item; pivot_item = a[low]; pivot = left = low; right = high;
while ( left < right ) { /* Move left while item < pivot */ while( a[left] <= pivot_item ) left++; /* Move right while item > pivot */ while( a[right] >= pivot_item ) right--; if ( left < right ) SWAP(a,left,right); } /* right is final position for the pivot */ a[low] = a[right]; a[right] = pivot_item; return right; }
Move the markers until they cross over
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low highpivot: 23
left right
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Quicksort - Partitionint partition( int *a, int low, int high ) { int left, right; int pivot_item; pivot_item = a[low]; pivot = left = low; right = high;
while ( left < right ) { /* Move left while item < pivot */ while( a[left] <= pivot_item ) left++; /* Move right while item > pivot */ while( a[right] >= pivot_item ) right--; if ( left < right ) SWAP(a,left,right); } /* right is final position for the pivot */ a[low] = a[right]; a[right] = pivot_item; return right; }
Move the left pointer whileit points to items <= pivot
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low highpivot: 23
left right Move right similarly
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Quicksort - Partition
int partition( int *a, int low, int high ) { int left, right; int pivot_item; pivot_item = a[low]; pivot = left = low; right = high;
while ( left < right ) { /* Move left while item < pivot */
while( a[left] <= pivot_item ) left++; /* Move right while item > pivot */
while( a[right] >= pivot_item ) right--; if ( left < right ) SWAP(a,left,right); } /* right is final position for the pivot */ a[low] = a[right]; a[right] = pivot_item; return right; }
Swap the two itemson the wrong side of the pivot
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low high
pivot: 23
left right
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Quicksort - Partitionint partition( int *a, int low, int high ) { int left, right; int pivot_item; pivot_item = a[low]; pivot = left = low; right = high;
while ( left < right ) { /* Move left while item < pivot */
while( a[left] <= pivot_item ) left++; /* Move right while item > pivot */
while( a[right] >= pivot_item ) right--; if ( left < right ) SWAP(a,left,right); } /* right is final position for the pivot */ a[low] = a[right]; a[right] = pivot_item; return right; }
left and right have swapped over,
so stop
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low highpivot: 23
leftright
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Quicksort - Partition
int partition( int *a, int low, int high ) { int left, right; int pivot_item; pivot_item = a[low]; pivot = left = low; right = high;
while ( left < right ) { /* Move left while item < pivot */
while( a[left] <= pivot_item ) left++; /* Move right while item > pivot */
while( a[right] >= pivot_item ) right--; if ( left < right ) SWAP(a,left,right); } /* right is final position for the pivot */ a[low] = a[right]; a[right] = pivot_item; return right; }
Finally, swap the pivotand right
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low highpivot: 23
leftright
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Quicksort - Partition
int partition( int *a, int low, int high ) { int left, right; int pivot_item; pivot_item = a[low]; pivot = left = low; right = high;
while ( left < right ) { /* Move left while item < pivot */
while( a[left] <= pivot_item ) left++; /* Move right while item > pivot */
while( a[right] >= pivot_item ) right--; if ( left < right ) SWAP(a,left,right); } /* right is final position for the pivot */ a[low] = a[right]; a[right] = pivot_item; return right; }
Return the positionof the pivot
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low high
pivot: 23
right
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Quicksort - Conquerpivot
18 12 15 23 42 38 36 29 27
pivot: 23
Recursivelysort left half Recursively
sort right half
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Why study Heapsort?It is a well-known, traditional sorting
algorithm you will be expected to knowHeapsort is always O(n log n)
Quicksort is usually O(n log n) but in the worst case slows to O(n2)
Quicksort is generally faster, but Heapsort is better in time-critical applications
Heapsort is a really cool algorithm!
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What is a “heap”? Definitions of heap:
1. A large area of memory from which the programmer can allocate blocks as needed, and deallocate them (or allow them to be garbage collected) when no longer needed
2. A balanced, left-justified binary tree in which no node has a value greater than the value in its parent
These two definitions have little in common
Heapsort uses the second definition
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Balanced binary treesRecall:
The depth of a node is its distance from the rootThe depth of a tree is the depth of the deepest node
A binary tree of depth n is balanced if all the nodes at depths 0 through n have two children
Balanced Balanced Not balanced
n-2n-1n
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Left-justified binary treesA balanced binary tree is left-justified
if:all the leaves are at the same depth, orall the leaves at depth n+1 are to the left
of all the nodes at depth n
Left-justified Not left-justified
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Plan of attackFirst, we will learn how to turn a binary tree
into a heapNext, we will learn how to turn a binary tree
back into a heap after it has been changed in a certain way
Finally (this is the cool part) we will see how to use these ideas to sort an array
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The heap propertyA node has the heap property if the value
in the node is as large as or larger than the values in its children
All leaf nodes automatically have the heap property
A binary tree is a heap if all nodes in it have the heap property
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8 3
Blue node has heap property
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8 12
Blue node has heap property
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8 14
Blue node does not have heap property
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shiftUpGiven a node that does not have the heap
property, you can give it the heap property by exchanging its value with the value of the larger child
This is sometimes called shifting upNotice that the child may have lost the heap
property
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8 12
Blue node has heap property
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8 14
Blue node does not have heap property
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Constructing a heap IA tree consisting of a single node is
automatically a heapWe construct a heap by adding nodes one
at a time:Add the node just to the right of the
rightmost node in the deepest levelIf the deepest level is full, start a new level
Examples: Add a new node here
Add a new node here
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Constructing a heap IIEach time we add a node, we may destroy the heap
property of its parent nodeTo fix this, we sift upBut each time we sift up, the value of the topmost
node in the shift may increase, and this may destroy the heap property of its parent node
We repeat the shifting up process, moving up in the tree, until eitherWe reach nodes whose values don’t need to be
swapped (because the parent is still larger than both children), or
We reach the root
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8 8
10
10
8
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8 5
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8 5
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10
12 5
8
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10 5
8
1 2 3
4
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Other children are not affected
The node containing 8 is not affected because its parent gets larger, not smaller
The node containing 5 is not affected because its parent gets larger, not smaller
The node containing 8 is still not affected because, although its parent got smaller, its parent is still greater than it was originally
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8 14
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8 10
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A sample heapHere’s a sample binary tree after it has been
heapified
Notice that heapified does not mean sortedHeapifying does not change the shape of the
binary tree; this binary tree is balanced and left-justified because it started out that way
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15
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Removing the rootNotice that the largest number is now in the rootSuppose we discard the root:
How can we fix the binary tree so it is once again balanced and left-justified?
Solution: remove the rightmost leaf at the deepest level and use it for the new root
19
1418
22
321
14
119
15
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11
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The reHeap method IOur tree is balanced and left-justified, but no longer a heapHowever, only the root lacks the heap property
We can shiftUp() the rootAfter doing this, one and only one of its
children may have lost the heap property
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The reHeap method IINow the left child of the root (still the number
11) lacks the heap property
We can shiftUp() this nodeAfter doing this, one and only one of its
children may have lost the heap property
19
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22
321
14
9
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22
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The reHeap method IIINow the right child of the left child of the root
(still the number 11) lacks the heap property:
We can shiftUp() this nodeAfter doing this, one and only one of its children
may have lost the heap property —but it doesn’t, because it’s a leaf
19
1418
11
321
14
9
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The reHeap method IVOur tree is once again a heap, because every
node in it has the heap property
Once again, the largest (or a largest) value is in the rootWe can repeat this process until the tree becomes emptyThis produces a sequence of values in order largest to
smallest
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SortingWhat do heaps have to do with sorting an array?Here’s the neat part:
Because the binary tree is balanced and left justified, it can be represented as an array
All our operations on binary trees can be represented as operations on arrays
To sort: heapify the array; while the array isn’t empty { remove and replace the root; reheap the new root node;
}
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Mapping into an array
Notice:The left child of index i is at index 2*i+1The right child of index i is at index 2*i+2
Example: the children of node 3 (19) are 7 (18) and 8 (14)
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25 22 17 19 22 14 15 18 14 21 3 9 11
0 1 2 3 4 5 6 7 8 9 10 11 12
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Removing and replacing the rootThe “root” is the first element in the arrayThe “rightmost node at the deepest level” is the last
elementSwap them...
...And pretend that the last element in the array no longer exists—that is, the “last index” is 11 (9)
25 22 17 19 22 14 15 18 14 21 3 9 11
0 1 2 3 4 5 6 7 8 9 10 11 12
11 22 17 19 22 14 15 18 14 21 3 9 25
0 1 2 3 4 5 6 7 8 9 10 11 12
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Reheap and repeatReheap the root node (index 0, containing 11)...
...And again, remove and replace the root nodeRemember, though, that the “last” array index is changedRepeat until the last becomes first, and the array is sorted!
22 22 17 19 21 14 15 18 14 11 3 9 25
0 1 2 3 4 5 6 7 8 9 10 11 12
9 22 17 19 22 14 15 18 14 21 3 22 25
0 1 2 3 4 5 6 7 8 9 10 11 12
11 22 17 19 22 14 15 18 14 21 3 9 25
0 1 2 3 4 5 6 7 8 9 10 11 12
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Analysis IHere’s how the algorithm starts:
heapify the array;
Heapifying the array: we add each of n nodes Each node has to be shifted up, possibly as
far as the root Since the binary tree is perfectly balanced, sifting
up a single node takes O(log n) timeSince we do this n times, heapifying takes
n*O(log n) time, that is, O(n log n) time
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Analysis IIHere’s the rest of the algorithm:
while the array isn’t empty { remove and replace the root; reheap the new root node;
}
We do the while loop n times (actually, n-1 times), because we remove one of the n nodes each time
Removing and replacing the root takes O(1) time
Therefore, the total time is n times however long it takes the reheap method
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Analysis IIITo reheap the root node, we have to follow
one path from the root to a leaf node (and we might stop before we reach a leaf)
The binary tree is perfectly balancedTherefore, this path is O(log n) long
And we only do O(1) operations at each nodeTherefore, reheaping takes O(log n) times
Since we reheap inside a while loop that we do n times, the total time for the while loop is n*O(log n), or O(n log n)
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Analysis IVHere’s the algorithm again:
heapify the array; while the array isn’t empty { remove and replace the root; reheap the new root node;
}
We have seen that heapifying takes O(n log n) time
The while loop takes O(n log n) timeThe total time is therefore O(n log n) + O(n log
n)This is the same as O(n log n) time
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