DATA STRUCTURE
&
ALGORITHMS
ASSIGNMENT NO. 2
SUBMITTED BY:
USAMA PERVAIZ
BEEF17M012
SUBMITTED TO:
ENGR. NOUMAN AHMAD TARIQ
ACADEMIC YEAR: 2017 -21
DEPARTMENT OF ELECTRICAL ENGINEERING
COLLEGE OF ENGINEERING AND TECHNOLOGY
UNIVERSITY OF SARGODHA
RADIX SORT ALGORITHM
Radix sort is a sorting technique that sorts the elements by first grouping the individual
digits of same place value. Then, sort the elements according to their increasing/decreasing
order.
Suppose, we have an array of 8 elements. First, we will sort elements based on the value of
the unit place. Then, we will sort elements based on the value of the tenth place. This process
goes on until the last significant place.
Let the initial array be [121, 432, 564, 23, 1, 45, 788]. It is sorted according to radix sort as
shown in the figure below.
Please go through the counting sort before reading this article because counting sort is used
as an intermediate sort in radix sort.
How Radix Sort Works?
1. Find the largest element in the array, i.e. max. Let X be the number of digits in max. X is
calculated because we have to go through all the significant places of all elements.
In this array [121, 432, 564, 23, 1, 45, 788], we have the largest number 788. It has 3 digits.
Therefore, the loop should go up to hundreds place (3 times).
2. Now, go through each significant place one by one.
Use any stable sorting technique to sort the digits at each significant place. We have used
counting sort for this.
Sort the elements based on the unit place digits (X=0).
3. Now, sort the elements based on digits at tens place.
4. Finally, sort the elements based on the digits at hundreds place.
Radix Sort Algorithm
radixSort(array)
d <- maximum number of digits in the largest element
create d buckets of size 0-9
for i <- 0 to d
sort the elements according to ith place digits using countingSort
countingSort(array, d)
max <- find largest element among dth place elements
initialize count array with all zeros
for j <- 0 to size
find the total count of each unique digit in dth place of elements and
store the count at jth index in count array
for i <- 1 to max
find the cumulative sum and store it in count array itself
for j <- size down to 1
restore the elements to array
decrease count of each element restored by 1
Complexity
Since radix sort is a non-comparative algorithm, it has advantages over comparative sorting
algorithms.
For the radix sort that uses counting sort as an intermediate stable sort, the time complexity
is O(d(n+k)).
Radix Sort Applications
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Radix sort is implemented in
DC3 algorithm (Kärkkäinen-Sanders-Burkhardt) while making suffix array.
places where there are numbers in large range.
BUCKET SORT ALGORITHM
Bucket Sort is a sorting technique that sorts the elements by first dividing the elements into
several groups called buckets. The elements inside each bucket are sorted using any of the
suitable sorting algorithms or recursively calling the same algorithm.
A number of buckets are created. Each bucket is filled with a specific range of elements. The
elements inside the bucket are sorted using any other algorithm. Finally, the elements of the
bucket are gathered to get the sorted array.
The process of bucket sort can be understood as scatter-gather approach. The elements are
first scattered into buckets then the elements of buckets are sorted. Finally, the elements are
gathered in order.
How Bucket Sort Works?
1. Suppose, the input array is:
Create an array of size 10. Each slot of this array is used as a bucket for storing elements.
2. Insert elements into the buckets from the array. The elements are inserted according to the
range of the bucket.
In our example code, we have buckets each of ranges from 0 to 1, 1 to 2, 2 to 3,...... (n-1) to
n.
Suppose, an input element is .23 is taken. It is multiplied by size = 10 (ie. .23*10=2.3). Then,
it is converted into an integer (ie. 2.3≈2). Finally, .23 is inserted into bucket-2.
Similarly, .25 is also inserted into the same bucket. Everytime, the floor value of thr floating
point number is taken.
In a similar way, other elements are inserted into their respective buckets.
3. The elements of each bucket are sorted using any of the stable sorting algorithms. Here, we
have used quicksort (inbuilt function).
4. The elements from each bucket are gathered.
It is done by iterating through the bucket and inserting an individual element into the original
array in each cycle. The element from the bucket is erased once it is copied into the original
array.
Bucket Sort Algorithm
1. bucketSort()
2. create N buckets each of which can hold a range of values
3. for all the buckets
4.
initialize each bucket with 0 values
5. for all the buckets
6.
put elements into buckets matching the range
7. for all the buckets
8.
sort elements in each bucket
9. gather elements from each bucket
10. end bucketSort
Complexity
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Worst Case Complexity: O(n2)
When there are elements of close range in the array, they are likely to be placed in the same
bucket. This may result in some buckets having more number of elements than others.
It makes the complexity depend on the sorting algorithm used to sort the elements of the
bucket.
The copmlexity becomes even worse when the elements are in reverse order. If insertion sort
is used to sort elements of the bucket, then the time complexity becomes O(n2).
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Best Case Complexity: O(n+k)
It occurs when the elements are uniformly distributed in the buckets with a nearly equal
number of elements in each bucket.
The complexity becomes even better if the elements inside the buckets are already sorted.
If insertion sort is used to sort elements of a bucket then the overall complexity in the best
case will be linear ie. O(n+k). O(n) is the complexity for making the buckets and O(k) is the
complexity for sorting the elements of the bucket using algorithm having linear time
complexity at best case.
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Average Case Complexity: O(n)
It occurs when the elements are distributed randomly in the array. Even if the elements are
not distributed uniformly, bucket sort runs in linear time. It holds true until the sum of the
squares of the bucket sizes is linear in the total number of elements.
Bucket Sort Applications
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Bucket sort is used when:
input is uniformly distributed over a range.
there are floating point values
SHELL SORT ALGORITHM
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Shell sort is an algorithm that first sorts the elements far apart from each other and
successively reduces the interval between the elements to be sorted. It is a generalized
version of insertion sort.
In shell sort, elements at a specific interval are sorted. The interval between the elements is
gradually decreased based on the sequence used. the performance of the shell sort depends on
the type of sequence used for a given input array.
Some of the optimal sequences used are:
Shell’s original sequence: N/2 , N/4 , …, 1
Knuth’s increments: 1, 4, 13, …, (3k – 1) / 2
Sedgewick’s increments: 1, 8, 23, 77, 281, 1073, 4193, 16577...4j+1+ 3·2j+ 1.
Hibbard’s increments: 1, 3, 7, 15, 31, 63, 127, 255, 511…
Papernov & Stasevich increment: 1, 3, 5, 9, 17, 33, 65,...
Pratt: 1, 2, 3, 4, 6, 9, 8, 12, 18, 27, 16, 24, 36, 54, 81....
How Shell Sort Works?
1. Suppose, we need to sort the following array.
2. We are using the shell’s original sequence (N/2, N/4, ...1) as intervals in our algorithm.
In the first loop, if the array size is N = 8 then, the elements lying at the interval of N/2 =
4 are compared and swapped if they are not in order.
a. The 0th element is compared with the 4th element.
b. If the 0th element is greater than the 4th one then, the 4th element is first stored
in temp variable and the 0th element (ie. greater element) is stored in the 4th position and the
element stored in temp is stored in the 0th position.
3. In the second loop, an interval of N/4 = 8/4 = 2 is taken and again the elements lying at these
intervals are sorted.
You might get confused at this point.
The elements at 4th and 2nd position are compared. The elements at 2nd and 0th position are
also compared. All the elements in the array lying at the current interval are compared.
4. The same process goes on for remaining elements.
5. Finally, when the interval is N/8 = 8/8 =1 then the array elements lying at the interval of 1 are
sorted. The array is now completely sorted.
Shell Sort Algorithm
1. shellSort(array, size)
2. for interval i <- size/2n down to 1
3.
for each interval "i" in array
4.
sort all the elements at interval "i"
5. end shellSort
Complexity
Shell sort is unstable sorting algorithm because this algorithm does not examine the elements
lying in between the intervals.
Time Complexity
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Worst Case Complexity: less than or equal to O(n2)
Worst case complexity for shell sort is always less than or equal to O(n2).
According to Poonen Theorem, worst case complexity for shell sort is Θ(NlogN)2/(log
logN)2) or Θ(NlogN)2/log logN) or Θ(N(logN)2) or something in between.
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Best Case Complexity: O(n*log n)
When the array is already sorted, the total number of comparison for each interval (or
increment) is equal to the size of the array.
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Average Case Complexity: O(n*log n)
It is around O(n1.25).
The complexity depends on the interval chosen. The above complexities differ for different
increment sequence chosen. Best increment sequence is unknown.
Space Complexity:
The space complexity for shell sort is O(1).
Shell Sort Applications
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Shell sort is used when:
calling a stack is overhead. uClibc library uses this sort.
recursion exceeds a limit. bzip2 compressor uses it.
Insertion sort does not perform well when the close elements are far apart. Shell sort helps in
reducing the distance between the close elements. Thus, there will be less number of
swappings to be performed.
COUNTING SORT ALGORITHM
Counting sort is a sorting algorithm that sorts the elements of an array by counting the
number of occurrences of each unique element in the array. The count is stored in an
auxiliary array and the sorting is done by mapping the count as an index of the auxiliary
array.
How Counting Sort Works?
1. Find out the maximum element (let it be max) from the given array.
2. Initialize an array of length max+1 with all elements 0. This array is used for storing the
count of the elements in the array.
3. Store the count of each element at their respective index in count array
For example: If the count of element “4” occurs 2 times then 2 is stored in the 4th position in
the count array. If element “5” is not present in the array, then 0 is stored in 5th position.
4. Store cumulative sum of the elements of the count array.
It helps in placing the elements into the correct index.
If there are x elements less than y, its position should be at x-1.
For example: In the array below, the count of 4 is 6. It denotes that there are 5 elements
smaller than 4. Thus, the position of 4 in the sorted array is 5th.
5. Find the index of each element of the original array in count array. This gives the cumulative
count. Place the element at the index calculated.
6. After placing each element at its correct position, decrease the its count by one.
Counting Sort Algorithm
1. countingSort(array, size)
2. max <- find largest element in array
3. initialize count array with all zeros
4. for j <- 0 to size
5.
find the total count of each unique element and
6.
store the count at jth index in count array
7. for i <- 1 to max
8.
find the cumulative sum and store it in count array itself
9. for j <- size down to 1
10. restore the elements to array
11.
decrease count of each element restored by 1
Time Complexities:
There are mainly four main loops. (Finding the greatest value can be done outside the
function.)
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for-loop
time of counting
1st
O(max)
2nd
O(size)
3rd
O(max)
4th
O(size)
Overall complexity = O(max)+O(size)+O(max)+O(size) = O(max+size)
Worst Case Complexity: O(n+k)
Best Case Complexity: O(n+k)
Average Case Complexity: O(n+k)
In all the above cases, the complexity is same because no matter how the elements are placed
in the array, the algorithm goes through n+k times.
There is no comparison between any elements so, it is better than comparison based sorting
techniques. But, it is bad if the integers are very large because the array of that size should be
made.
Space Complexity:
The space complexity of Counting Sort is O(max). Larger the range of elements, larger is the
space complexity.
Counting Sort Applications
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Counting sort is used when:
the are smaller integers of multiple counts.
linear complexity is the need.