Here, we will find out is it really quick or by the name only? Sometimes Quicksort is called partition exchange sort. It's an efficient sorting algorithm and serving as a systematic method for placing the elements of an array in order wise. It's internally used by a lot of operating systems. In Linux systems, the sort command under the hood is nothing but quicksort. In , Quicksort developed by Tony Hoare but published after two years. It can sort items of any type for which defined as a less-than relation.
Quicksort can operate in-place on an array, requiring small additional amounts of memory to perform the sorting. It is very similar to selection sort, but it's worse in worst case performance. So, it rarely uses in worst case O n2. The quicksort algorithm is a particularly interesting one and this algorithm breaks down a larger problem into smaller, subproblems. It continues to choose a pivot point and break down the collection into single-element lists, before combing them back together to form one sorted list.
So, this algorithm functions work faster than others. Quicksort is also a cache friendly sorting algorithm as it has good locality of reference when used for arrays. It almost doesn't do unnecessary element swaps.
Swap is time-consuming. With quicksort, you don't swap what is already ordered. If your data is completely ordered, you swap almost nothing. A good reason why quicksort is so fast in practice compared to most other O n log n algorithms such as Heapsort, is because it is relatively cache-efficient. Its running time is actually O n B log nB , where B is the block size.
Heapsort, on the other hand, doesn't have any such speedup: it's not at all accessing memory cache-efficiently. Most practical implementations of quicksort use randomized version.
Writing code in comment? Please use ide. Load Comments. What's New. Most popular in Difference Between. Most visited in Sorting. We use cookies to ensure you have the best browsing experience on our website. These are not the same. If you look at the other answers, you will find that Mergesort might run faster - but Quicksort has the advantage that it uses negligible space for things other than the array to be sorted.
That means I can call it without fear of running out of memory, no matter how large the array is. That is not quite true for Mergesort, if the data can be close in size to the available data. And if virtual memory is available, Mergesort can take a lot longer due to paging.
In practice you wouldn't just use a O n log n algorithm, you would check if you can do things faster in special cases. If your array is mostly ascending or mostly descending with few exceptions, it can be sorted in linear time. If you go a bit further: If your array consists of an initial part that is mostly sorted, a middle part, and an end part that is mostly sorted, and the middle part is substantially smaller than the whole array, then we can sort the middle part with Quicksort or Mergesort, and combine the result with the sorted initial or end parts.
However, this doesn't affect the question, because the middle part can still be sorted with Quicksort or Mergesort. Another question is: Can the work be multi-threaded? The partitioning is hard to divide between two threads. Once the array is partitioned, each half can easily be handled by a separate thread.
If you have many threads, being able to use only one for the initial partitioning is painful. With Mergesort, we can also do many smaller merges using many threads. We have the advantage that a single merge can also be run in parallel by one thread producing the lower half of the sorted array, and another thread working from the high values down, producing the higher half of the sorted array.
I think this gives a large advantage to Mergesort if many threads are available. Sign up to join this community. The best answers are voted up and rise to the top. Stack Overflow for Teams — Collaborate and share knowledge with a private group. Create a free Team What is Teams? Learn more. Why is quicksort better than other sorting algorithms in practice? Ask Question. Asked 9 years, 8 months ago. Active 1 year, 3 months ago.
Viewed k times. Improve this question. Janoma Janoma 5, 3 3 gold badges 17 17 silver badges 21 21 bronze badges. I have not seen both. For instance, most formal analysis count only comparisons. I ran some tests some time ago with random data and a naive implementation of quick sort and merge sort.
Both algorithms performed pretty well for small data sets up to items but after that merge sort turned out to be much better. This seems to contradict the general assumption that quick sort is so good and I still haven't found an explanation for it.
The only idea I could come up with is that normally the term quick sort is used for more complex algorithms like intro sort, and that the naive implementation of quick sort with random pivot is not that good. Show 13 more comments. Active Oldest Votes. There are two ways out: Fix some machine model. Note also that the algorithms relate differently for small inputs: [ source ] Analyse abstract basic operations.
Other input distributions As noted above, average cases are always with respect to some input distribution, so one might consider ones other than random permutations. Improve this answer. Sebastian Sebastian 4, 2 2 gold badges 17 17 silver badges 14 14 bronze badges. Therefore, I would argue 2. I suppose you're assuming that machine-dependant is also implementation-dependant, right?
But yes, the implementation would factor in as well. Real world machines are so complicated that the results from type 2 can not be feasibly translated to type 1.
Compare that to type 1: plotting experimental running times takes 5 minutes of work. That is why we do formal analysis, after all. Show 10 more comments. Quicksort is usually faster than Mergesort This comparison is completely about constant factors if we consider the typical case. Use the sort that suits your needs In conclusion: no sorting algorithm is always optimal.
Alex ten Brink Alex ten Brink 8, 2 2 gold badges 32 32 silver badges 62 62 bronze badges. A colleague of mine currently analyses Quicksort implementations under different input distributions. Some of them break down for many duplicates, for instance.
Imagine Newton saying, "Butterflies fly up, apples fall down: there's no theory behind this, apples just happen to fall. Some algorithm families suffers from a lack of full formalisation; hashing functions are an example case.
Show 2 more comments. I'll accept your answer a bit later to give others a chance to comment on this algorithm too. Yes, it works as Henrik has said, but it's a very wasteful way to do a quicksort to have the pivot sorted again. The reason you don't generally see quicksorts done this way is that it isn't as efficient. Resorting the pivot will generate more stack calls as well as extra unnecessary comparisons and probably swaps too.
Justin Peel - actually, the first implementation seems to be much faster than the wiki one, even with random pivots. But that discussion is for another question :. Show 2 more comments. Blastfurnace From Wikipedia I've emphasized the part that I think addresses your question directly : This is the in-place partition algorithm. Could that be what you were missing?
Dan Tao Dan Tao k 52 52 gold badges silver badges bronze badges. No, I don't see what that has to do with my question.
That describes the algorithm that I said definitely works, I was asking why the first one also works. Plus, the first one does not find the final position of the pivot element, nor does it temporarily move anything.
You are getting confused between the index of the item and the iten value Look at your header int partition int a[], int p, int r ; Now if we changed the data type on the array a to some weird data type you will see the problem int partition Otherdatatype a[], int p, int r ; You call the function from within your main with partition a, 1, a[0] ; See the problem a[0] is the value of the entry in a[0] not an index value.
Array a runs from a[0] Mxyk Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password.
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