# The Quick Sort

**Krishna Kumar Khatri**on

**February 12th, 2003**

### WEBINAR:

On-Demand

Building the Right Environment to Support AI, Machine Learning and Deep Learning

__Environment:__Windows 9x, TC or C's MinGW port of GCC.

### Preface

My previous article on Insertion Sort (you can find it at www.codeguru.com/algorithms/kmethod.html) faced a lot of real hot comments (especially by Peter). This forced me to write about more interesting and comprehensive sorting methods, the result of which is this one. Through this writing I have tried to give in-depth coverage of the entire sort algorithm; I hope Peter wouldn't mind reading it. This article assumes that you really don't know about the iterations, looping, and so forth; hence, it explains these in detail first.

### Iterations

While designing and writing applications for the end user, sometimes it is found that a set of statements are being repeated for a pre-determined time. When such a condition arises, we go for the looping. Some people, however, may argue about the use of the functions then as they too are used repeatedly throughout the life-span of the program (application). The main difference here is that the function is set (block) of statements that peform a cohesive task. For example, we may have a function for reading the input from the user, while another displays the same back to the user. Because these two functions are cohesive, we write functions (hence the modular approach) and through the main function (where the actual execution is taking place) we just call these newly written functions for as many times as we need by the use of the loopings' iterations.

### Recursion

*primitive recursive functions*and

*nonprimitive recursive functions*. Primitive recursive functions are the functions that can normally be solved iteratively (an example is factorial, sum of digits, and so forth), whereas nonprimitive recursive functions can't be solved iteratively (not easily at least) (an example is Ackermann's function). A general algorithm model for any recursive procedure (whether primitive or nonprimitive) contains the following steps:

Prologue >> Save the parameters, local variables, and return address.

Body >> If the base criterion has been reached, then perform the final computation and go to Step 3; otherwise, perform the partial computation and go to Step 1 (initiate a recursive call).

Epilogue >> Restore the most recent saved parameters, local variables, and return address. Go to this return address.

A more detailed discussion of the same is beyond the scope of this article. Have a look at the following code to see a recursive fuction in action:

int fun(long n) { static int sum; sum = 0; if (n >= 10) return sum + (n % 10) + fun(n / 10); else return n; }

### Time Complexity

Whenever an algorithm is developed, several analytical phases are applied to it for the stress test. The most obvious, the correctness, can be done simply by tracing the algorithm, reading the algorithm for logical correctness, implementing the algorithm, and testing it on some data or using mathematical techniques to prove it correct. The other factor that is widely used to determine the performance of an algorithm is the Time complexity.

Time complexity is a factor that determines how much time the algorithm takes to finish. It is almost impossible to calculate the exact time for an algorithm (due to a variety of machines available); however, we can always obtain an estimated time. Usually, this is most easily done by isolating a particular operation, sometimes called an *active operations*, that is central to the algorithm and that is executed essentially as often as any other (for example, inner and outer iterations). The other operation in the algorithm, the assignments, the manipulation of index, and so forth, occur no more often than the body of the inner iterations. These other operations are called collectively *bookkeeping operations* and generally are not counted.

Let us consider, for example, the case of a linear sort, where two loops are required (both run from 0 to N-1) to run N times (each). Thus, together the time complexity of such an algorithm becomes N * N; in other words, N^{2} or O(N^{2}).

### Fundamental Concept of Quick Sort

This sorting method performs very well on larger tables. At each step in the method, the goal is to place a particular record in its final position within the table. In so doing, all records that precede this record have smaller keys, while all records that follow it have larger keys. This technique essentially partitions the table into two subtables (and hence is also called partition-sort). The same process can then be applied to each of these subtables and repeated until all records are placed in their final positions.

Each time a table is partitioned into two smaller subtables, these can be processed in turn by using a recursive approach. A general algorithm based on a recursive approach follows:

- Partition the current table into two subtables.
- Invoke Quicksort to sort the left subtable.
- Invoke Quicksort to sort the right subtable.

### Algorithm

Given a table K of N records, this recursive procedure sorts the table in ascending order. A dummy record with key K[N+1] is assumed where K[I] <= K[N+1] for all 1 <= I <= N. The integer parameters LB and UB denote the lower and upper bounds of the current subtable being processed. I and J are used as index variables to select keys. KEY contains the key value that is being placed in its final position within the sorted table. FLAG is used as a logical variable indicating the end of a processed that places a record in it final position. When FLAG becomes false, the input subtable has been partitioned into two disjointed parts:

QUICK_SORT(K, LB, UB) 1. [Initialize] FLAG <- true 2. [Perform Sort] If LB < UB then I <- LB J <- UB + 1 KEY <- K[LB] Repeat while FLAG I <- I + 1 Repeat while K[I] < KEY (scan the keys from left to right) I <- I + 1 Repeat while K[J] > KEY (scan the keys from right to left) J <- J - 1 If I < J then K[I] <---> K[J] (interchange records) else FLAG <- fales K[LB] <---> K[J] (interchange records) Call QUICK_SORT(K, LB, J - 1) (sort first subtable) Call QUICK_SORT(K, J + 1, UB) (sort second subtable) 3. [Finished] ReturnThe procedure is intially invoked by the statement

`Call QUICK_SORT(K, 1, N)`

.
### Time Complexity Analysis

Now, let us focus on the time complexity of the Quick sort algorithm. The analysis of the procedure `QUICK_SORT`

is given by

T(N) = P(N) + T(J-LB) + T(UB - J)

where P(N), T(J-LB), and T(UB-J) denote the times to partition the given table, sort the left subtable, and sort the right subtable, respectively. Note that the time to partition a table is O(N). The worst case occurs when, at each invocation of the procedure, the current table is partitioned into two subtables with one of them being empty (that is J = LB or J = UB). Such a situation, for example, occurs when the given key set is already sorted. The sorting of the example key set {11, 23, 36, 42} would yield the following sequence of partitions:

11 {23 36 42} 11 23 {36 42} 11 23 36 {42} 11 23 36 42The point to be noted here is that the present method of sorting is no better than the selection sort. The worst case time analysis, assuming J = LB, then becomes

T^{w}= P(N) + T^{w}(0) + T^{w}(N-1) = c*N + T^{w}(N-1) = c*N + c*(N-1) + T^{w}(N-2) = c*N + c*(N-1) + c*(N-2) + T^{w}(N-3) . . . = c * {(N+1)(N)}/2 =O(N^{2})

The worst case can sometimes be avoided by choosing the record for final placement more carefully at each stage. Istead of always choosing K_{LB}, as was done in the previous approach, we could choose a random position in the interval [LB, UB]. Another approach is to take the middle position in the interval, that is [(LB+UB)/2]. Finally, the position could be chosen to be the median of the keys in the interval, although this option is costly.

The best case analysis occurs when the table is always partitioned in half, that is, J = [(LB+UB)/2]. The analysis becomes:

T^{b}= P(N) + 2T^{b}(N/2) = c*N + 2T^{b}(N/2) = c*N + 2c(N/2) + 4T^{b}(N/4) = c*N + 2c(N/2) + 4c(N/4) + 8T^{b}(N/8) . . . = (log_{2}N)*c*N + 2^{log2N}*T^{b}(1) =O(Nlog_{2}N)

The average case analysis (albeit difficult to evaluate) also comes out to be

. I wrote this article after the detailed study of the various sorting methods and my work is influenced by Paul G. Sorenson and Jean Paul Trembley of the Department of Computational Science of University of Saskatchewan, Saskatoon.**O(Nlog _{2}N)**

### Downloads

Download demo project - 5 KbDownload source - 1 Kb

## Comments

## non recursive quicksort algorithm in c++

Posted byAnjali01on10/24/2008 02:22pmhi can anyone please send me the non recursive version of the quicksort program in C++...i badly need it a simple one

Reply## Not working properly

Posted bycegparameshon12/05/2005 10:22amI downloaded the source code, but it is not working properly. Here is the sample output i got: How many keys would you like to enter : 5 Now enter the elements separated by or :
10
020
3
1
4
Sorted table is :
-858993460 1 3 4 10
Program over. Press any key to exit...

## Re: Not working properly

Posted byzipcn046on12/25/2005 05:57amHi: I apologise for being late in replying. Well, it has really been a long time since I have written this article. However, after receiving your complaint-notification I tested the same key-set on my PC and it worked perfectly - yielding correct output. Perhaps, problem is somwhere else than in the algorithm/code. Try removing preceding 0 from the second key you enter and then let me know whether you receive the same errorneous result. Regards, zipcn046.

Reply## QuickSort Partition In Fewest Moves

Posted byDarelRexon08/17/2005 09:57amGo here for a non-recursive, highly optimized quicksort implementation in C. It also includes a step-by-step diagram showing the best way to carry out one partition in the fewest possible moves: http://alienryderflex.com/quicksort

Reply## Re: Non recursive version of QuickSort.

Posted byzipcn046on03/19/2005 12:17am## non recursive version of quicksort

Posted bynanu2010on03/18/2005 06:46pmcan you give me a non recursive version of quicksort that uses a stack. the version should store at the stack indicies[i,j] of subarrays that have not been sorted yet. what is the number of [i,j] pairs, non-sorted sunarrays that can belong to stack simultaneously.

## Re: Non recursive version of QuickSort.

Posted byzipcn046on03/19/2005 12:08am## demo project

Posted byallygatoron03/21/2004 06:38pmthe demo project doesn't work properly! if the user enters 5 tokens, the sorted list eats up the largest token, and always makes 0 the shortest token, no matter whether 0 was entered as a token or not! it needs fixing! does the source mirror this error, or does it still work?

Reply## Some interesting facts...

Posted byLegacyon11/24/2003 12:00amOriginally posted by: RaspberryJam

Dear Krishna,

The comments you seem to unfortunately invite seem more interesting than your articles. (in fact I did just that)

Anyways, it brought back sweet memories of my CSE classes 12 years back.

keep up the good work.

Some interesting thoughts

1. Anthony Hoare (born 1934 in Sri Lanka), who invented QUICKSORT now works for MICROSOFT! So doodlsquats to the guys who said MS knows nothing about writing libraries!!

2. http://research.microsoft.com/~thoare/

;-)

Reply## Moderator !!!

Posted byLegacyon07/29/2003 12:00amOriginally posted by: WellWisher

Knock Knock !!!

Is moderator awake ?

NO

Reply## what's wrong?

Posted byLegacyon06/12/2003 12:00amOriginally posted by: telemark

what the hell is wrong with posting yet-another-quick-sort-implementation? as i saw, it's the only one available on codeguru, and since i first search here if code fragments needed, it helped solving a problem in about 10 minutes while coding it on my own would have taken at least an hour...

Replydid i get smth wrong about the intention of this site?

## Useless

Posted byLegacyon06/09/2003 12:00amOriginally posted by: Dheeraj

Not so helping?????

ReplyLoading, Please Wait ...