2.3   Quicksort

Quicksort is popular because it is not difficult to implement, works well for a variety of different kinds of input data, and is substantially faster than any other sorting method in typical applications. It is in-place (uses only a small auxiliary stack), requires time proportional to N log N on the average to sort N items, and has an extremely short inner loop.

The basic algorithm.

Quicksort is a divide-and-conquer method for sorting. It works by partitioning an array into two parts, then sorting the parts independently.

Quicksort overview
The crux of the method is the partitioning process, which rearranges the array to make the following three conditions hold: We achieve a complete sort by partitioning, then recursively applying the method to the subarrays. It is a randomized algorithm, because it randomly shuffles the array before sorting it.


To complete the implementation, we need to implement the partitioning method. We use the following general strategy: First, we arbitrarily choose a[lo] to be the partitioning item—the one that will go into its final position. Next, we scan from the left end of the array until we find an entry that is greater than (or equal to) the partitioning item, and we scan from the right end of the array until we find an entry less than (or equal to) the partitioning item.

Quicksort partitioning overview
The two items that stopped the scans are out of place in the final partitioned array, so we exchange them. When the scan indices cross, all that we need to do to complete the partitioning process is to exchange the partitioning item a[lo] with the rightmost entry of the left subarray (a[j]) and return its index j.

Quicksort partitioning


Quick.java is an implementation of quicksort, using the partitioning method described above.

Quicksort trace

Implementation details.

There are several subtle issues with respect to implementing quicksort that are reflected in this code and worthy of mention.


Quicksort uses ~2 N ln N compares (and one-sixth that many exchanges) on the average to sort an array of length N with distinct keys.


Quicksort uses ~N2/2 compares in the worst case, but random shuffling protects against this case.

The standard deviation of the running time is about .65 N, so the running time tends to the average as N grows and is unlikely to be far from the average. The probability that quicksort will use a quadratic number of compares when sorting a large array on your computer is much less than the probability that your computer will be struck by lightning!


Quicksort was invented in 1960 by C. A. R. Hoare, and it has been studied and refined by many people since that time.


QuickBars.java visualizes quicksort with median-of-3 partitioning and cutoff for small subarrays.

Quicksort visualization

Entropy-optimal sorting.

Arrays with large numbers of duplicate sort keys arise frequently in applications. In such applications, there is potential to reduce the time of the sort from linearithmic to linear.

One straightforward idea is to partition the array into three parts, one each for items with keys smaller than, equal to, and larger than the partitioning item's key. Accomplishing this partitioning was a classical programming exercise popularized by E. W. Dijkstra as the Dutch National Flag problem, because it is like sorting an array with three possible key values, which might correspond to the three colors on the flag.

Dijkstra's solution is based on a single left-to-right pass through the array that maintains a pointer lt such that a[lo..lt-1] is less than v, a pointer gt such that a[gt+1..hi] is greater than v, and a pointer i such that a[lt..i-1] are equal to v, and a[i..gt] are not yet examined.

Quicksort 3-way partitioning overview

Starting with i equal to lo we process a[i] using the 3-way compare given us by the Comparable interface to handle the three possible cases:

Quicksort 3-way partitioning trace

Quick3way.java is an implementation of this method.


Quicksort with 3-way partitioning is entropy-optimal.


Quick3wayBars.java visualizes quicksort with 3-way partitioning.

3-way quicksort visualization


  1. Show, in the style of the trace given with partition(), how that method partitions the array E A S Y Q U E S T I O N.

    Partitioning trace

  2. Show, in the style of the quicksort trace, how quicksort sorts the array E A S Y Q U E S T I O N. (For the purposes of this exercise, ignore the initial shuffle.)

    Quicksort trace

  3. Write a program Sort2distinct.java that sorts an array that is known to contain just two distinct key values.

  4. About how many compares will Quick.sort() make when sorting an array of N items that are all equal?

    Solution. ~ N lg N compares. Each partition will divide the array in half, plus or minus one.

  5. Show, in the style of the trace given with the code, how the entropy-optimal sort first partitions the array B A B A B A B A C A D A B R A.
    3-way Partitioning trace

Creative Problems

  1. Nuts and bolts. (G. J. E. Rawlins). You have a mixed pile of N nuts and N bolts and need to quickly find the corresponding pairs of nuts and bolts. Each nut matches exactly one bolt, and each bolt matches exactly one nut. By fitting a nut and bolt together, you can see which is bigger. But it is not possible to directly compare two nuts or two bolts. Given an efficient method for solving the problem.

    Hint: customize quicksort to the problem. Side note: only a very complicated deterministic O(N log N) algorithm is known for this problem.

  2. Best case. Write a program QuickBest.java that produces a best-case array (with no duplicates) for Quick.sort(): an array of N distinct keys with the property that every partition will produce subarrays that differ in size by at most 1 (the same subarray sizes that would happen for an array of N equal keys). For the purposes of this exercise, ignore the initial shuffle.

    Best-case input for quicksort

  3. Fast three-way partitioning. (J. Bentley and D. McIlroy). Implement an entropy-optimal sort QuickBentleyMcIlroy.java based on keeping equal keys at both the left and right ends of the subarray. Maintain indices p and q such that a[lo..p-1] that a[q+1..hi] are all equal to a[lo], an index i such that a[p..i-1] are all less than a[lo] and an index j such that a[j+1..q] are all greater than a[lo]. Add to the inner partitioning loop code to swap a[i] with a[p] (and increment p) if it is equal to v and to swap a[j] with a[q] (and decrement q) if it is equal to v before the usual comparisons of a[i] and a[j] with v.

    Bentley-McIlroy 3-way partitioning overview
    After the partitioning loop has terminated, add code to swap the equal keys into position.

Web Exercises

  1. QuickKR.java is one of the simplest quicksort implementations, and appears in K+R. Convince yourself that it is correct. How will it perform? All equal keys?
  2. Randomized quicksort. Modify partition() so that it always chooses the partitioning item uniformly at random from the array (instead of shuffling the array initially). Compare the performance against Quick.java.
  3. Antiquicksort. The algorithm for sorting primitive types in Java 6 is a variant of 3-way quicksort developed by Bentley and McIlroy. It is extremely efficient for most inputs that arise in practice, including inputs that are already sorted. However, using a clever technique described by M. D. McIlroy in A Killer Adversary for Quicksort, it is possible to construct pathological inputs that make the system sort run in quadratic time. Even worse, it overflows the function call stack. To see the sorting library in Java 6 break, here are some killer inputs of varying sizes: 10,000, 20,000, 50,000, 100,000, 250,000, 500,000, and 1,000,000. You can test them out using the program IntegerSort.java which takes a command line input N, reads in N integers from standard input, and sorts them using the system sort.
  4. Bad partitioning. How does not stopping on equal keys make quicksort go quadratic when all keys are equal?

    Solution. Here is the result of partitioning AAAAAAAAAAAAAAA when we don't stop on equal keys. It unevenly partitions the array into one subproblem of size 0 and one of size 14.

    Partitioning AAAAAAAAAAAAAAA when we don't stop on equal keys

    Here is the result of partitioning AAAAAAAAAAAAAAA when we do stop on equal keys. It evenly partitions the array into two subproblems of size 7.

    Partitioning AAAAAAAAAAAAAAA when we do stop on equal keys
  5. Comparing an item against itself. Show that our implementation of quicksort can compare an item against itself, i.e., calls less(i, i) for some index i. Modify our implementation so that it never compares an item against itself.
  6. Hoare's original quicksort. Implement a version of Hoare's original quicksort algorithm. It's similar to our two-way partitioning algorithm except that the pivot is not swapped into its final position. Instead, the pivot is left in one of the two subarrays, no element is fixed in its final position, and the two subarrays where the pointers cross are sorted recursively.

    Solution. HoareQuick.java. We note that, while this verison is quite elegant, it does not preserve randomness in the subarrays. According to Sedgewick's PhD thesis, "this bias not only makes analysis of the method virtually impossible, it also slows down the sorting process considerably."

  7. Dual-pivot quicksort. Implement a version of Yaroslavskiy's dual-pivot quicksort.

    Solution. QuickDualPivot.java is an implementation that is very similar to Quick3way.java.

  8. Three-pivot quicksort. Implement a version of three-pivot quicksort ala Kushagra-Ortiz-Qiao-Munro.
  9. Number of compares. Give a family of arrays of length n for which the standard quicksort partitioning algorithm makes (i) n + 1 compares, (ii) n compares, (iii) n - 1 compares, or argue that no such family of arrays exist.

    Solution: ascending order; descending order; none.