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(IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 8, November 2010 A Comparative Study on Kakkot Sort and Other Sorting Methods Rajesh Ramachandran Dr.E.Kirubakaran HOD, Department of Computer Science Sr.DGM(Outsourcing),BHEL,Trichy Naipunnya Institute of Management & Email: e_kiru@yahoo.com Information Technology, Pongam, Kerala Email: ryanrajesh@hotmail.com Abstract: Several efficient algorithms were Introduction developed to cope with the popular task of sorting. Kakkot sort is a new variant of Sorting is any process of arranging items in Quick and Insertion sort. The Kakkot sort some sequence and/or in different sets, and algorithm requires O( n log n ) accordingly, it has two common, yet distinct comparisons for worst case and average meanings: case. Typically, Kakkot Sort is significantly faster in practice than other O ( n log n ) 1. ordering: arranging items of the algorithms , because its inner loop can be same kind, class, nature, etc. in some efficiently implemented on most ordered sequence, architectures . This sorting method requires 2. categorizing: grouping and labeling data movement, but less than that of items with similar properties together insertion sort. This data movement can be (by sorts). reduced by implementing the algorithm using linked list. In this comparative study In computer science and mathematics, a the mathematical results of Kakkot sort Sorting Algorithm is an algorithm that puts were verified experimentally on ten elements of a list in a certain order. The randomly generated unsorted numbers. To most-used orders are numerical order and have some experimental data to sustain this lexicographical order. Efficient sorting is comparison four different sorting methods important to optimizing the use of other were chosen and code was executed and algorithms (such as search and merge execution time was noted to verify and algorithms) that require sorted lists to work analyze the performance. The Kakkot Sort correctly. algorithm performance was found better as compared to other sorting methods. To analyze an algorithm is to determine the amount of resources (such as time and Key words: Complexity, performance of storage) necessary to execute it. Most algorithms, sorting algorithms are designed to work with inputs of arbitrary length. Usually the efficiency or complexity of an algorithm is stated as a function relating the input length to the number of steps (time complexity) or storage locations (space complexity). Algorithm analysis is an important part of a broader computational complexity theory, 150 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 8, November 2010 which provides theoretical estimates for the time complexity, its space complexity is also resources needed by any algorithm which important: This is essentially the number of solves a given computational problem. memory cells which an algorithm needs. A These estimates provide an insight into good algorithm keeps this number as small reasonable directions of search for efficient as possible, too. The space complexity of a algorithms. In theoretical analysis of program (for a given input) is the number of algorithms it is common to estimate their elementary objects that this program needs complexity in the asymptotic sense, i.e., to to store during its execution. This number is estimate the complexity function for computed with respect to the size n of the arbitrarily large input. Big O notation, input data. omega notation and theta notation are used to this end There is often a time-space-tradeoff involved in a problem, that is, it cannot be Time complexity solved with few computing time and low memory consumption. One then has to make Time efficiency estimates depend on what a compromise and to exchange computing we define to be a step. For the analysis to time for memory consumption or vice versa, correspond usefully to the actual execution depending on which algorithm one chooses time, the time required to perform a step and how one parameterizes it. must be guaranteed to be bounded above by a constant. In mathematics, computer In addition to varying complexity, sorting science, and related fields, Big Oh notation algorithms also fall into two basic categories describes the limiting behavior of a function — comparison based and non-comparison when the argument tends towards a based. A comparison based algorithm orders particular value or infinity, usually in terms a sorting array by weighing the value of one of simpler functions. Big O notation allows element against the value of other elements. its users to simplify functions in order to Algorithms such as Quicksort, Mergesort, concentrate on their growth rates: different Heapsort, Bubble sort, and Insertion sort are functions with the same growth rate may be comparison based. Alternatively, a non- represented using the same O notation. comparison based algorithm sorts an array without consideration of pairwise data Although developed as a part of pure elements. Radix sort is a non-comparison mathematics, this notation is now frequently based algorithm that treats the sorting also used in computational complexity elements as numbers represented in a base- theory to describe an algorithm's usage of M number system, and then works with computational resources: the worst case or individual digits of M. average case running time or memory usage of an algorithm is often expressed as a Another factor which influences the function of the length of its input using big performance of sorting method is the O notation. behavior pattern of the input. In computer science, best, worst and average cases of a Space complexity given algorithm express what the resource usage is at least, at most and on average, The better the time complexity of an respectively. Usually the resource being algorithm is, the faster the algorithm will considered is running time, but it could also carry out his work in practice. Apart from be memory or other resources. 151 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 8, November 2010 Kakkot Sort Step1. Read the first two numbers from N, Let K1 & K2 Kakkot Sort is a sorting algorithm that , Step2. Sort K1 and K2 makes O ( n log n ) (Big Oh notation) Step3. Read the next number, Let A comparisons to sort n items. Typically, Step4. Compare A with K2 Kakkot Sort is significantly faster in practice than other O ( n log n ) algorithms , Step5. If A is greater than or equal to K2 because its inner loop can be efficiently then place A right of K2 implemented on most architectures . This else sorting method requires data movement but compare A with K1. less than that of insertion sort. This data If A is less than K1 movement can be reduced by implementing then place A left of K1 the algorithm using linked list. Major else advantage of this sorting method is its Place A immediate right of K1 behavior pattern is same for all cases, ie Step6 . If the list contains any more time complexity of this method is same for elements go to step 3 best, average and worst case Step 7. Now we have 3 Sub list. How it sorts First list with all values less than or equal to K1. From the given set of unsorted numbers, Second with values between take the first two numbers and name it as K1 and K2 key one and key two , ie, K1 and K2. Read Final with values greater than all the remaining numbers one by one. or equal to K2. Compare each number first with K2. If the number is greater than or equal to K2 then Step8. If each list contains more than 1 place the number right of K2 else compare element go to step1 the same number with K1. If the number is greater than K1 then place the number Step 9 End. immediate right of K1 else left of K1.Conitnue the same process for all the Time complexity remaining numbers in the list. Finally we will get three sub lists. One with numbers If there are ‘n’ numbers, then each iteration less than or equal to K1, one with numbers needs maximum 2 * (n-2) comparison and greater than or equal to K2 and the other minimum of n-2 comparison and plus one. with numbers between K1 and K2. Repeat So if we take the average it will be the same process for each sub list. Continue this process till the sub list contains zero =(2n-4+n-2)/2 + 1 elements or one element. =(3n-6)/2+1 = 3n/2 – 2 In the average case each list would have 3 sub lists and number of iteration will be Algorithm 3x=n Kakkot Sort(N:Array of Numbers, K1 ,K2 , taking logarithm on both side we get A:integers,) 152 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 8, November 2010 x log 3= log n x= log n / log 3 Consider the following randomly generated x= log n/ 0.4771 ten unsorted numbers Ignoring the constant we can write x = log n 1,60,33,3,35,21,53,19,70,94 List 1 That is there will be log n iterations and each require 3n/2 – 2 comparisons. So the First two numbers are 1 and 60 time complexity of Kakkot Sort in average and sort it . Here K1 is 1 and K2 is 60 case is 3n/2 – 2 * log n. When we represent Now the total comparison is one. in Big Oh notation constants can be ignored, Read the remaining numbers one by one so we get O(n log n). Read 33, since 33 is less than K2 and greater If the list is already in sorted order, then two than K1 it need two comparison . Now the comparison will be required for each total comparison is increased to 3. number ,so total no of comparison required Read 3, total comparison is now 5 for each iteration will be (n-2)+1, i.e. n-1 Read 35, total comparison is now 7 and number of iteration will be n-1+n-3+n- Read 21, total comparison is now 9 6+…..+1 Read 53, total comparison now is 11 This can be written as Read 19, total comparison now is 13 1+3+5+…..n-3+n-1. Read 70, total comparison now is 14 Read 94 total comparison now is 15 Sum of this series is Now the list will be S= N/2*(2a +(N-1)*d) 1, 3,35,21,53,19 ,60,70,94 Where N is the number of terms in the series Here we have 3 sublist ‘a’ is first term The first one with zero elements ‘d’ is the difference Second list is , 3,35, 35,21,53,19 To get Nth term, the equation is a+(N-1) d Third list is 70,94 And here Nth term is n- 1, so Now do the same process second and third 1+(N-1)*2=n-1 list 2N=n Second list N= n/2 Read first two numbers, and sort S=N/2(2*1+(N-1)*2) We have K1 =3 and K2= 35 S=N/2(2+2N-2) Now total comparison is 16 S=N/2(2N) Read 21, total comparison now is 18 Sum = N2 Read 53,total comparison now is 19 Substitute value for N we get Read 19,total comparison now is 21 (n/2)**2 Now the list will be 3,19,21,35,53 This is equal to one forth of n2. So Kakkot Now only one list with more than one Sort requires only one forth of Quick sort element, ie 19 and 21 comparison in worst case. This is almost Read the first two numbers and sort equal to average case time complexity. So Here K1=19 and K2 =21 we can say that time complexity of Kakkot Now the total comparison is 22 sort is similar in all the cases. Now regarding the sublist 3 we have two numbers 70 and 94 Now let me manually calculate the number Read the numbers and sort of comparison that Kakkot sort take. Now the total number of comparison is 23 153 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 8, November 2010 Kakkot Sort and Bubble Sort So Using Kakkot sort , to sort the given ten randomly generated numbers require only Bubble sort is a straightforward and 23 comparisons. simplistic method of sorting data that is used in computer science education. The Kakkot Sort and Qucick Sort algorithm starts at the beginning of the data set. It compares the first two elements, and if Time complexity of Quick sort is O(n log n) the first is greater than the second, then it in the case of average case and O(n2) in the swaps them. It continues doing this for each worst case behavior. From this it is clear that pair of adjacent elements to the end of the Kakkot sort is better than quick sort. While data set. It then starts again with the first two sorting Quick sort does not require any data elements, repeating until no swaps have movement where as Kakkot sort needs data occurred on the last pass. This algorithm is movement when the item is less than first highly inefficient, and is rarely used[citation key element and greater than second key needed][dubious – discuss], except as a element. But this data movement can be simplistic example. For example, if we have avoided by implementing the algorithm 100 elements then the total number of using linked list. comparisons will be 10000. A slightly better To sort the above ten numbers in the List 1 , variant, cocktail sort, works by inverting the Quick sort requires 29 comparisons ordering criteria and the pass direction on alternating passes. The modified Bubble sort Kakkot Sort and Heap Sort will stop 1 shorter each time through the loop, so the total number of comparisons for Heapsort is a much more efficient version of 100 elements will be 4950. selection sort. It also works by determining the largest (or smallest) element of the list, Bubble sort average case and worst case are placing that at the end (or beginning) of the both O(n²) list, then continuing with the rest of the list, but accomplishes this task efficiently by For the above unsorted numbers in the List 1 using a data structure called a heap, a special Bubble sort requires 45 comparisons. type of binary tree. Once the data list has been made into a heap, the root node is guaranteed to be the largest(or smallest) Kakkot Sort and Insertion Sort element. When it is removed and placed at the end of the list, the heap is rearranged so Insertion sort is a simple sorting algorithm the largest element remaining moves to the that is relatively efficient for small lists and root. Using the heap, finding the next largest mostly-sorted lists, and often is used as part element takes O(log n) time, instead of O(n) of more sophisticated algorithms. It works for a linear scan as in simple selection sort. by taking elements from the list one by one This allows Heapsort to run in O(n log n) and inserting them in their correct position time, and this is also the worst case into a new sorted list. In arrays, the new list complexity. and the remaining elements can share the With the same set of unsorted numbers in array's space, but insertion is expensive, the List 1, Heap sort requires 30 requiring shifting all following elements comparisons over by one. Shell sort (see below) is a 154 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 8, November 2010 variant of insertion sort that is more efficient [6] Sartaj Sahni, “Data Structures for larger lists. Algorithms and Applications in C++”, Insertion sort requires 38 comparisons to University Press, 2nd Ed.,2005 sort the above ten randomly generated numbers in the List 1. [7] Yedidyah Langsam,Moshe J Augenstein, Aaron M Tanenbaum “Data Structures using C and C++”, Prentice Hall India, 2nd Ed. 2005 Conclusion [8] Alfred V Aho, John E Hopcroft, Jeffrey From the above examples it is clear that D Ullman,”Data Structures and Kakkot Sort time complexity is better than Algorithms”, Pearson Education,2nd other sorting methods. Even though Kakkot Ed.,2006 sort requires data movement of items when the item is less than the key K2 and greater [9] Sara Baase, Allen Van Gelder, than the key K1, this data movement can be “Computer Algorithms Introduction to reduced by implementing the algorithm Design and Analysis, Pearson Education, 3rd using linked list. Ed. ,2006 References: [10] Mark Allen Weiss “Data Structures and Algorithm analysis in C++ “, Pearson Education, 3rd Ed., 2007 [1] Aaron M Tanenbaum, Moshe J Augenstein, “Data Structures using [11] Michael T Goodrich, Roberto C”,Prentice Hall International Tamassia, “Algorithm Design Foundations, Inc.,Emglewood Cliffs,NJ,1986 Analysis and Internet Examples”, John Wiley and Sons Inc.,2007 [2] Robert L Cruse, “ Data Structure and Program Design”, Prentice Hall India 3rd [12] Seymour Lipschutz, GAV Pai , “ Data ed.,1999 Structures”, Tata McGraw Hill,2007 [3] Robert Kruse, C L Tondo, Bruse Leung [13] Robert Lafore,” Data Structures and “Data Structures and Program design in Algorithms in Java”, Waite Group Inc., C”, Pearson Education,2nd Ed.,2002 2007 [4] Alfred V Aho, John E Hopcroft, Jeffrey [14] Rajesh Ramachandran, Dr.E. D Ullman, “ The Design and Analysis of Kirubakaran, “Kakkot Sort – A New Sorting Computer Alogorithms”, Pearson Education Method”, International Journal of Computer , 2003 Science, Systems Engineering and Information Technology, ISSN 0974-5807 [5] Thomas H Cormen, Charles E Leiserson, Vol. 2 No. 2 pp209-213,2010 Ronald L Rivest, Clifford Stein, “Introduction to Algorithms” Prentice Hall of India Pvt.Ltd., 2nd Ed. , 2004 155 http://sites.google.com/site/ijcsis/ ISSN 1947-5500

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