sorting-algorithms | friendly repository made for open source beginners | Learning library

 by   argonautica Java Version: Current License: MIT

kandi X-RAY | sorting-algorithms Summary

kandi X-RAY | sorting-algorithms Summary

sorting-algorithms is a Java library typically used in Tutorial, Learning, Example Codes applications. sorting-algorithms has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However sorting-algorithms build file is not available. You can download it from GitHub.

Sorting algorithms implemented in different languages (for hacktoberfest ). This repository is open to everyone. Feel free to add any sorting algorithms. The instructions for how to contribute to this repo are down below._.

            kandi-support Support

              sorting-algorithms has a low active ecosystem.
              It has 30 star(s) with 112 fork(s). There are 2 watchers for this library.
              It had no major release in the last 6 months.
              There are 2 open issues and 7 have been closed. On average issues are closed in 0 days. There are 25 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of sorting-algorithms is current.

            kandi-Quality Quality

              sorting-algorithms has no bugs reported.

            kandi-Security Security

              sorting-algorithms has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              sorting-algorithms is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              sorting-algorithms releases are not available. You will need to build from source code and install.
              sorting-algorithms has no build file. You will be need to create the build yourself to build the component from source.

            Top functions reviewed by kandi - BETA

            kandi has reviewed sorting-algorithms and discovered the below as its top functions. This is intended to give you an instant insight into sorting-algorithms implemented functionality, and help decide if they suit your requirements.
            • Performs a cyclic sort on an array .
            • Simple quick sort .
            • merge elements from right to right
            • Combines the elements in an array using their natural ordering .
            • letter sort algorithm
            • returns the count sort
            • Used to improve the sorted sort order .
            • Heapify a sub - tree .
            • Recursively insert a key into a tree .
            • Merge sort order .
            Get all kandi verified functions for this library.

            sorting-algorithms Key Features

            No Key Features are available at this moment for sorting-algorithms.

            sorting-algorithms Examples and Code Snippets

            No Code Snippets are available at this moment for sorting-algorithms.

            Community Discussions


            Swift Quicksort Algorithm
            Asked 2020-May-07 at 01:36

            been trying to program the following Quicksort Algorithm in Swift for a while now and cannot work out the issue. [Quicksort as there are around 15,000 actual values in array]. The problem is only the left half of the array is ordered (see pic), and the method is never exited (infinite loop). Following a Java conversion from (tested in Java and does work). Cannot work out the error.



            Answered 2017-Jan-28 at 12:38

            Your computation of piv and pivot_location is wrong. It should be:



            javascript: asynchronous function problems (async await with loop)
            Asked 2020-Jan-05 at 15:31

            I'm having a problem with a asynchronous function in javascript

            My function looks like this:



            Answered 2020-Jan-01 at 21:58

            I see your .map() function doesn't return any promises. You can fix this with



            khan academy: javascript loop taking too long to run
            Asked 2019-Jan-01 at 15:09

            I'm reading through the khan academy course on algorithms. I'm at

            The code challenge is supposed to loop through the array find and return the lowest number in the subarray. I've written code which I think should work, but I'm getting the error in the title. Why?



            Answered 2017-Jun-05 at 19:55

            The second part of your for loop needs to be something that actually tests for something. right now it's an infinite loop because its something that's always true

            This should fix it:



            Insertion Sort is a good choice for "small" data sets. What is "small"?
            Asked 2018-Dec-16 at 17:30

            There are many places I have seen where it talks about how Insertion Sort is good for small data sets. I can't find a number for what "small" is though. My guess is that there is no absolute answer and that it depends on the type of machine the code is being run on.

            However, what factors go into deciding what is the threshold for when Insertion Sort is a good idea? And what are some ballpark figures for "small"? 5? 10? 50? 100?


            Site saying Insertion Sort is good for small data sets:



            Answered 2018-Dec-16 at 17:28

            An attempt at an answer, providing we're talking about the general sorting problem. Insertion sort is on average O(n^2), efficient sorting algorithms are on average O(nlogn). So vaguely speaking if something takes K steps to sort efficiently it will take around (kind of) K^2 steps with insertion sort.

            So if n > K is too slow for your liking with an efficient sort, n > K^0.5 will be too slow for you (roughly) with insertion sort.

            Practically speaking let's say you're happy to sort arrays of size 10^8 with something efficient then you might be happy to sort arrays of size 10^4 with insertion sort.



            Does stable_sort() also preserve the order of unequal elements?
            Asked 2018-Jun-27 at 03:17

            From cppreference, I know that for stable_sort:

            The order of equal elements is guaranteed to be preserved.

            I also read this question What is stability in sorting algorithms and why is it important? and understand the concept and basic usage of stability.

            Here's how I understand stable sort:
            Let's say I have unordered flight departure time and destination.

            First, I sort it by time. It would be like



            Answered 2018-Jun-27 at 02:58

            fox, red, and the are equal for the purposes of the stable_sort. The cppreference link you have says:

            Elements are compared using the given comparison function comp.

            So, yes the fox red the order is fixed for your example, as the stable_sort won't change the relative order of those three (equally short) items.



            Difference in calculating middle index of array?
            Asked 2018-Mar-21 at 23:12

            I have been trying to solve QuickSort and I got thru a scenario where we are selecting pivot element as the middle one.




            Answered 2018-Mar-21 at 23:12

            It is more likely that


            overflows rather than


            For example for lowerIndex == higherIndex == Integer.MAX_VALUE / 2 + 1.

            Edit: Mathematical proof of equivalence of the expressions



            QuickSort and MergeSort performance on Sequential data fit in memory vs Slow to Access Sequential data on disk
            Asked 2017-Dec-26 at 04:26

            The following quote is from "Comparison with other sort algorithms" section from Wikipedia Merge Sort page

            On typical modern architectures, efficient quicksort implementations generally outperform mergesort for sorting RAM-based arrays.[citation needed] On the other hand, merge sort is a stable sort and is more efficient at handling slow-to-access sequential media.

            My questions:

            1. Why does Quicksort outperform Mergesort when the data to be sorted can all fit into memory? If all data needed are cached or in memory wouldn't it be fast for both Quicksort and Mergesort to access?

            2. Why is Mergesort more efficient at handling slow-to-access sequential data (such as from disk in the case where the data to be sorted can't all fit into memory)?

            3. (move from my comments below to here)In an array arr of primitives (data are sequential) of n elements. The pair of elements that has to be read and compared in MergeSort is arr[0] and arr[n/2] (happens in the final merge). Now think the pair of elements that has to be read and compared in QuickSort is arr[1] and arr[n] (happens in the first partition, assume we swap the randomly chosen pivot with the first element). We know data are read in blocks and load into cache, or disk to memory (correct me if I am wrong) then isn't there a better chance for the needed data gets load together in one block when using MergeSort? It just seems to me MergeSort would always have the upperhand because it is likely comparing elements that are closer together. I know this is False (see graph below) because QuickSort is obviously faster...... I know MergeSort is not in place and requires extra memory and that is likely to slow things down. Other than that what pieces am I missing in my analysis?

            images are from Princeton CS MergeSort and QuickSort slides

            My Motive:

            I want to understand these above concepts because they are one of the main reasons of why mergeSort is preferred when sorting LinkedList,or none sequential data and quickSort is preferred when sorting Array, or sequential data. And why mergeSort is used to sort Object in Java and quickSort is used to sort primitive type in java.

            update: Java 7 API actually uses TimSort to sort Object, which is a hybrid of MergeSort and InsertionSort. For primitives Dual-Pivot QuickSort. These changes were implemented starting in Java SE 7. This has to do with the stability of the sorting algorithm. Why does Java's Arrays.sort method use two different sorting algorithms for different types?


            I will appreciate an answer that addresses the following aspects:

            • I know the two sorting algorithms differ in the number of moves, read, and comparisons. If those are that reasons contribute to the behaviors I see listed in my questions (I suspected it) then a thorough explanation of how the steps and process of the sorting algorithm results it having advantages or disadvantages seeking data from disk or memory will be much appreciated.
            • Examples are welcome. I learn better with examples.

            note: if you are reading @rcgldr's answer. check out our conversation in the chat room it has lots of good explanations and details.



            Answered 2017-Dec-23 at 00:01

            The main difference is that merge sort does more moves, but fewer compares than quick sort. Even in the case of sorting an array of native types, quick sort is only around 15% faster, at least when I've tested it on large arrays of pseudo random 64 bit unsigned integers, which should be quick sort's best case, on my system (Intel 3770K 3.5ghz, Windows 7 Pro 64 bit, Visual Studio 2015, sorting 16 million pseudo random 64 bit unsigned integers, 1.32 seconds for quick sort, 1.55 seconds for merge sort, 1.32/1.55 ~= 0.85, so quick sort was about 15% faster than merge sort). My test was with a quick sort that had no checks to avoid worst case O(n^2) time or O(n) space. As checks are added to quick sort to reduce or prevent worst case behavior (like fall back to heap sort if recursion becomes too deep), the speed advantage decreases to less than 10% (which is the difference I get between VS2015's implementation of std::sort (modified quick sort) versus std::stable_sort (modified merge sort).

            If sorting "strings", it's more likely that what is being sorted is an array of pointers (or references) to those strings. This is where merge sort is faster, because the moves involve pointers, while the compares involve a level of indirection and comparison of strings.

            The main reason for choosing quick sort over merge sort is not speed, but space requirement. Merge sort normally uses a second array the same size as the original. Quick sort and top down merge sort also need log(n) stack frames for recursion, and for quick sort limiting stack space to log(n) stack frames is done by only recursing on the smaller partition, and looping back to handle the larger partition.

            In terms of cache issues, most recent processors have 4 or 8 way associative caches. For merge sort, during a merge, the two input runs will end up in 2 of the cache lines, and the one output run in a 3rd cache line. Quick sort scans the data before doing swaps, so the scanned data will be in cache, although in separate lines if the two elements being compared / swapped are located far enough from each other.

            For an external sort, some variation of bottom up merge sort is used. This because merge sort merge operations are sequential (the only random access occurs when starting up a new pair of runs), which is fast in the case of hard drives, or in legacy times, tape drives (a minimum of 3 tapes drives is needed). Each read or write can be for very large blocks of data, reducing average access time per element in the case of a hard drive, since a large number of elements are read or written at a time with each I/O.

            It should also be noted that most merge sorts in libraries are also some variation of bottom up merge sort. Top down merge sort is mostly a teaching environment implementation.

            If sorting an array of native types on a processor with 16 registers, such as an X86 in 64 bit mode, 8 of the registers used as start + end pointers (or references) for 4 runs, then a 4-way merge sort is often about the same or a bit faster than quick sort, assuming a compiler optimizes the pointers or references to be register based. It's a similar trade off, like quick sort, 4-way merge sort does more compares (1.5 x compares), but fewer moves (0.5 x moves) than traditional 2-way merge sort.

            It should be noted that these sorts are cpu bound, not memory bound. I made a multi-threaded version of a bottom up merge sort, and in the case of using 4 threads, the sort was 3 times faster. Link to Windows example code using 4 threads:




            Printing array with Processingjs
            Asked 2017-Jun-06 at 15:11

            I'm reading through the khan academy course on algorithms. I'm at .

            The code is working and I can get it to print to the console but I need to be able to print to the canvas.

            I've tried :



            Answered 2017-Jun-06 at 15:10

            You're still using "println", you need to use 'text' function.

   is the reference on how to use this.


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install sorting-algorithms

            You can download it from GitHub.
            You can use sorting-algorithms like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the sorting-algorithms component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer For Gradle installation, please refer .


            If there's an existing folder named with your chosen language, create a file for your new algorithm inside, calling it by the algorithm name and use the appropriate file extension i.e. bubbleSort.js. If there aren't any algorithms in your choice of language, feel free to start your own folder and place your implementations inside, and don't forget to update the!.
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