quicksort

Closed . This question is opinion-based . It is not currently accepting answers. Want to improve this question? Update the question so it can be answered with facts and citations by editing this post . Closed 2 years ago . I consider Merge sort as divide and conquer because, Divide - Array is literally divided into sub arrays without any processing (compare/swap), and the problem sized is halved/Quartered/.... Conquer - merge() those sub arrays by processing (compare/swap) Code gives an impression that it is Divide&Conquer, if(hi <= lo) return; int mid = lo + (hi-lo)/2; //No (compare/swap) on

I'm implementing a parellel quicksort as programming practice, and after I finished, I read the Java tutorial page on Executors, which sound like they could make my code even faster. Unfortunately, I was relying on join()'s to make sure that the program doesn't continue until everything is sorted. Right now I'm using: public static void quicksort(double[] a, int left, int right) { if (right <= left) return; int i = partition(a, left, right); // threads is an AtomicInteger I'm using to make sure I don't // spawn a billion threads. if(threads.get() < 5){ // ThreadSort's run method just calls

This question already has an answer here: Pseudo-quicksort time complexity 6 answers As they say, "true quicksort sorts in-place" . So the standard short Haskell code for quicksort , quicksort :: Ord a => [a] -> [a] quicksort [] = [] quicksort (p:xs) = (quicksort lesser) ++ [p] ++ (quicksort greater) where lesser = filter (< p) xs greater = filter (>= p) xs what algorithm/computational process is it describing, after all? It surely isn't what Tony Hoare devised , lacking its most defining feature, the in-place partitioning algorithm. (the answer might be well known, but not yet here on SO).

Here is a quicksort algorithm for numbers written in Clojure. It is basically the quicksort algorithm found in "The Joy of Clojure" , 2nd edition, page 133. I modified it slightly for (hopefully) better readability, because the original felt a bit too compact: (defn qsort-inner [work] (lazy-seq (loop [loopwork work] (let [[ part & partz ] loopwork ] (if-let [[pivot & valuez] (seq part)] (let [ smaller? #(< % pivot) smz (filter smaller? valuez) lgz (remove smaller? valuez) nxxt (list* smz pivot lgz partz) ] (recur nxxt)) (if-let [[oldpivot & rightpartz] partz] (cons oldpivot (qsort-inner

I am reading some text which claims this regarding the ordering of the two recursive Quicksort calls: ... it is important to call the smaller subproblem first, this in conjunction with tail recursion ensures that the stack depth is log n. I am not at all sure what that means, why should I call Quicksort on the smaller subarray first? Look at quicksort as an implicit binary tree. The pivot is the root, and the left and right subtrees are the partitions you create. Now consider doing a depth first search of this tree. The recursive calls actually correspond to doing a depth first search on the

I have been reading about Quicksort and found that sometimes it' s referred to as "Deterministic Quicksort". Is this an alternate version of the normal Quicksort ? What is the difference between a normal Quicksort and a Deterministic Quicksort ? The ordinary ("deterministic") Quicksort can have very poor behaviour on particular datasets (as an example, an implementation that picks the first unsorted element has O(n^2) time complexity on already-sorted data). Randomized Quicksort (which selects a random pivot, rather than choosing deterministically) is sometimes used to give better expected