Which is quickest sorting method

In fact, it is an improvement over the bubble sort algorithm. If you've observed earlier, bubble sort compares adjacent elements for every iteration. But for comb sort, the items are compared and swapped by a large gap value.

The gap value shrinks by a factor of 1. This shrink factor has been empirically calculated to be 1. Given an array, find the most frequent element in it. If there are multiple elements that appear maximum number of times, print any one of them.

Shell sort algorithm is an improvement over the insertion sort algorithm wherein we resort to diminishing partitions to sort our data. In each pass, we reduce the gap size to half of its previous value for each pass throughout the array.

Thus for each iteration, the array elements are compared by the calculated gap value and swapped if necessary. The idea of shell sort is that it permits the exchange of elements located far from each other. In Shell Sort, we make the array N-sorted for a large value of N.

We then keep reducing the value of N until it becomes 1. Given an unsorted array of integers. Write a program to remove duplicates from the unsorted array. You can even strengthen your sorting algorithms by building a similar sorting visualizer, all by yourself.

Follow the step-by-step instructions and add this valuable project to your resume. Now that you've explored the Top 10 sorting algorithms , all that's left is to answer a few basic questions just 3 in fact. This will hardly take a minute. At the end of the day though, the best sorting algorithm comes down to the nature of your input data and who you ask. Bubble sort is widely recognized as the simplest sorting algorithm out there. Its basic idea is to scan through an entire array and compare adjacent elements and swap them if necessary until the list is sorted.

Pretty simple right? We would also love to know the sorting technique that got you excited the most - let us know in the comments below. Learn the fundamentals of Merge Sort with an example. Sharpen your understanding with fun quizzes and activities. Crio Blog Abheetha Pradhan. Learn the details of insertion sort and how you can use it effectively to sort your dataset. Crio Blog Harshita Bansal. Time Complexity Simplified with Easy Examples. Find out what is time complexity. Understand how to analyze time complexities with simple examples.

Crio Blog Sandipan Das. Project-based Backend Developer Program. Enroll and start for free. Get real software development experience. Even more generally, optimality of a sorting algorithm depends intimately upon the assumptions you can make about the kind of lists you're going to be sorting as well as the machine model on which the algorithm will run, which can make even otherwise poor sorting algorithms the best choice; consider bubble sort on machines with a tape for storage.

The stronger your assumptions, the more corners your algorithm can cut. This answer deals only with complexities. Actual running times of implementations of algorithms will depend on a large number of factors which are hard to account for in a single answer.

The answer, as is often the case for such questions, is "it depends". It depends upon things like a how large the integers are, b whether the input array contains integers in a random order or in a nearly-sorted order, c whether you need the sorting algorithm to be stable or not, as well as other factors, d whether the entire list of numbers fits in memory in-memory sort vs external sort , and e the machine you run it on. In practice, the sorting algorithm in your language's standard library will probably be pretty good pretty close to optimal , if you need an in-memory sort.

Therefore, in practice, just use whatever sort function is provided by the standard library, and measure running time. Only if you find that i sorting is a large fraction of the overall running time, and ii the running time is unacceptable, should you bother messing around with the sorting algorithm. If those two conditions do hold, then you can look at the specific aspects of your particular domain and experiment with other fast sorting algorithms. Theoretically, is it possible that there are even faster ones?

So, what's the least complexity for sorting? There are some algorithms that perform sorting in O n , but they all rely on making assumptions about the input, and are not general purpose sorting algorithms.

Basically, complexity is given by the minimum number of comparisons needed for sorting the array log n represents the maximum height of a binary decision tree built when comparing each element of the array.

You can find the formal proof for sorting complexity lower bound here :. The fastest integer sorting algorithm in terms of worst-case I have come across is the one by Andersson et al. I read through the other two answers at the time of writing this and I didn't think either one answered your question appropriately. Other answers considered extraneous ideas about random distributions and space complexity which are probably out of the scope for high school studies.

So here is my take. This is an unbreakable bound. This is unbeatable. As you don't mention any restrictions on hardware and given you're looking for "the fastest", I would say you should pick one of the parallel sorting algorithm based on available hardware and the kind of input you have. In theory e. In practice, for massive input sizes it would be impossible to achieve O log n due to scalability issues.

Here is the pseudo code for Parallel merge sort. Implementation of merge can be same as in normal merge sort:. 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? In case of linked lists the case is different mainly due to difference in memory allocation of arrays and linked lists. Unlike arrays, linked list nodes may not be adjacent in memory.

Unlike array, in linked list, we can insert items in the middle in O 1 extra space and O 1 time. Therefore merge operation of merge sort can be implemented without extra space for linked lists. In arrays, we can do random access as elements are continuous in memory.

Unlike arrays, we can not do random access in linked list. Quick Sort requires a lot of this kind of access. Therefore, the overhead increases for quick sort. Merge sort accesses data sequentially and the need of random access is low. How to optimize QuickSort so that it takes O Log n extra space in worst case?

Skip to content. Change Language. Related Articles. Table of Contents. Save Article. Improve Article. Like Article. Python3 implementation of QuickSort. This Function handles sorting part of quick sort.

Initializing pivot's index to start. This loop runs till start pointer crosses. Increment the start pointer till it finds an. Decrement the end pointer till it finds an.

If start and end have not crossed each other,. Swap pivot element with element on end pointer. Returning end pointer to divide the array into 2. The main function that implements QuickSort. Sort elements before partition. This code is contributed by Adnan Aliakbar.