Quicksort

COS 265 - Data Structures & Algorithms

two classic sorting algorithms

Critical components in the world's computational infrastructure

Full scientific understanding of their properties has enabled us to develop them into practical system sorts
Quicksort honored as one of Top 10 Algorithms of 20th century in science and engineering

Merge sort: last lecture

Quicksort: this lecture

[ Quicksort Algorithm Tees ]

quicksort

Basic plan

Shuffle the array
Partition so that, for some j
- entry a[j] is in place
- no larger entry to the left of j
- no smaller entry to the right of j
Sort each subarray recursively

input:                   Q U I C K S O R T E X A M P L E

shuffle:                 K R A T E L E P U I M Q C X O S
partition item:          ^---------v
partition:             E C A I E  |K|  L P U T M Q R X O S
                      ( all<=K )  |K|  (     all >= K     )
quicksort left:   ==>  A C E E I  |K|  . . . . . . . . . .
quicksort right:       . . . . .  |K|  L M O P Q R S T U X  <==

result:                  A C E E I K L M O P Q R S T U X

sir tony hoare

Sir Tony Hoare, 1980 Turing Award

Invented quicksort to translate Russian into English (but couldn't explain his algorithm or implement it!)
Learned Algol 60 (and recursion)
Implemented quicksort

[ wikipedia: Tony Hoare ]

sir tony hoare

“
There are two ways of constructing a software design: One way is to make it so simple that there are obviously no deficiencies, and the other way is to make it so complicated that there are no obvious deficiencies. The first method is far more difficult.
”

“
I call it my billion-dollar mistake. It was the invention of the null reference in 1965... This has led to innumerable errors, vulnerabilities, and system crashes, which have probably caused a billion dollars of pain and damage in the last forty years.
”

bob sedgewick

Refined and popularized quicksort
Analyzed many versions of quicksort

quicksort partitioning demo

Repeat until i and j pointers cross

Scan i from left to right so long as a[i] < a[lo]
Scan j from right to left so long as a[j] > a[lo]
Exchange a[i] with a[j]

When pointers cross

Exchange a[lo] with a[j]

quicksort partitioning demo

Partitioned!

quicksort: java code for partitioning

private static int partition(Comparable[] a, int lo, int hi) {
    int i = lo, j = hi + 1;
    while(true) {
        while(less(a[++i], a[lo]))      // find item on left to swap
            if(i == hi) break;
        while(less(a[lo], a[--j]))      // find item on right to swap
            if(j == lo) break;
        if(i >= j) break;               // check if pointers cross
        exch(a, i, j);                  // swap
    }
    exch(a, lo, j);                 // swap with partition item
    return j;       // return index of item now know to be in place
}

quiz: compares to partition

[ live view, card view ]

Q. How many compares (in the worst case) to partition an array of length \(N\)?

\(\sim \frac{1}{4} N\)
\(\sim \frac{1}{2} N\)
\(\sim N\)
\(\sim N \lg N\)

quicksort: java implementation

public class Quick {
    private static int partition(Comparable[] a, int lo, int hi) {
        /* as before */
    }

    public static void sort(Comparable[] a) {
        // shuffle needed for performance guarantee (stay tuned...)
        StdRandom.shuffle(a);
        sort(a, 0, a.length - 1);
    }

    private static void sort(Comparable[] a, int lo, int hi) {
        if(hi <= lo) return;
        int j = partition(a, lo, hi);
        sort(a, lo, j-1);
        sort(a, j+1, hi);
    }
}

quicksort trace

lo  j hi  0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5
          Q U I C K S O R T E X A M P L E  <- initial values
          K R A T E L E P U I M Q C X O S  <- random shuffle
 0  5 15  E C A I E|K|L P U T M Q R X O S  <- K is pivot
 0  3  4  E C A|E|I . . . . . . . . . . .  <- E is pivot
 0  2  2  A C|E|. . . . . . . . . . . . .  <- E is pivot
 0  0  1 |A|C . . . . . . . . . . . . . .  <- A is pivot
 1     1  .|C|. . . . . . . . . . . . . .  <- no partition
 4     4  . . . .|I|. . . . . . . . . . .  <- subarrays of sz 1
 6  6 15  . . . . . .|L|P U T M Q R X O S  <- L is pivot
 7  9 15  . . . . . . . M O|P|T Q R X U S  <- P is pivot
 7  7  8  . . . . . . .|M|O . . . . . . .  <- M is pivot
 8     8  . . . . . . . .|O|. . . . . . .  <- no partition
10 13 15  . . . . . . . . . . S Q R|T|U X  <- T
10 12 12  . . . . . . . . . . R Q|S|. . .  <- S
10 11 11  . . . . . . . . . . Q|R|. . . .  <- R
10    10  . . . . . . . . . .|Q|. . . . .  <- no partition
14 14 15  . . . . . . . . . . . . . .|U|X  <- U
15    15  . . . . . . . . . . . . . . .|X| <- no partition
          A C E E I K L M O P Q R S T U X  <- result

quicksort: animation

Key:

Black values are sorted
Gray values are unsorted
Red triangle marks algorithm position (i, j)
Dark gray values denote the current subarray

Note

Data here are not shuffled before sorting, but a random item in subarray is chosen as pivot (equivalent to shuffling).

[ Sorting Algorithm Animations: Quick Sort ]

excerpt of 15 sorting algorithms in 6 minutes

[ Timo Bingmann ]

quicksort: implementation details

Partition in-place

Using an extra array makes partitioning easier (and stable), but is not usually worth the cost

Terminating the loop

Testing whether the pointers cross is trickier than it might seem

Equal keys

When duplicates are present, it is (counter-intuitively) better to stop scans on keys equal to the partitioning item's key (see book)

Preserving randomness

Shuffling is needed for performance guarantee
Equivalent alternative: Pick a random partitioning item in each subarray

quicksort: empirical analysis (1961)

Running time estimates

Algol 60 implementation
National-Elliott 405 computer

sorting \(N\) 6-word items with 1-word keys

quicksort: empirical analysis

Running time estimates:

Home PC executes \(10^8\) compares/second
Supercomputer executes \(10^{12}\) compares/second

	Insertion Sort			Merge Sort			Quicksort
computer	1k	1m	1b	1k	1m	1b	1k	1m	1b
home	instant	2.8hrs	317yrs	instant	1sec	18min	instant	0.6sec	12min
super	instant	1sec	1wk	instant	instant	instant	instant	instant	instant

Lesson 1: Good algorithms are better than supercomputers

Lesson 2: Great algorithms are better than good ones

quicksort: best-case analysis

Best case: Number of compares is \(\sim N \lg N\)

lo  j hi  0 1 2 3 4 5 6 7 8 9 0 1 2 3 4
          A B C D E F G H I J K L M N O  <- initial values
          H A C B F E G D L I K J N M O  <- random shuffle
 0  7 14  D A C B F E G|H|L I K J N M O  <- 14 comparisons
 0  3  6  B A C|D|F E G . . . . . . . .  <-  6 comparisons
 0  1  2  A|B|C . . . . . . . . . . . .  <-  2 comparisons
 0     0 |A|. . . . . . . . . . . . . .
 2     2  . .|C|. . . . . . . . . . . .
 4  5  6  . . . . E|F|G . . . . . . . .  <-  2 comparisons
 4     4  . . . .|E|. . . . . . . . . .
 6     6  . . . . . .|G|. . . . . . . .
 8 11 14  . . . . . . . . J I K|L|N M O  <-  6 comparisons
 8  9 10  . . . . . . . . I|J|K . . . .  <-  2 comparisons
 8     8  . . . . . . . .|I|. . . . . .
10    10  . . . . . . . . . .|K|. . . .
12 13 14  . . . . . . . . . . . . M|N|O  <-  2 comparisons
12    12  . . . . . . . . . . . .|M|. .
14    14  . . . . . . . .   . . . . .|O|
          A B C D E F G H I J K L M N O  <- result,  34 comparisons

quicksort: worst-case analysis

Worst case: Number of compares is \(\sim \frac{1}{2} N^2\)

lo  j hi  0 1 2 3 4 5 6 7 8 9 0 1 2 3 4
          A B C D E F G H I J K L M N O  <- initial values
          A B C D E F G H I J K L M N O  <- random shuffle
 0  0 14 |A|B C D E F G H I J K L M N O  <- 14 comparisons
 1  1 14  .|B|C D E F G H I J K L M N O  <- 13 comparisons
 2  2 14  . .|C|D E F G H I J K L M N O  <- 12 comparisons
 3  3 14  . . .|D|E F G H I J K L M N O  <- 11 comparisons
 4  4 14  . . . .|E|F G H I J K L M N O  <- 10 comparisons
 5  5 14  . . . . .|F|G H I J K L M N O  <-  9 comparisons
 6  6 14  . . . . . .|G|H I J K L M N O  <-  8 comparisons
 7  7 14  . . . . . . .|H|I J K L M N O  <-  7 comparisons
 8  8 14  . . . . . . . .|I|J K L M N O  <-  6 comparisons
 9  9 14  . . . . . . . . .|J|K L M N O  <-  5 comparisons
10 10 14  . . . . . . . . . .|K|L M N O  <-  4 comparisons
11 11 14  . . . . . . . . . . .|L|M N O  <-  3 comparisons
12 12 14  . . . . . . . . . . . .|M|N O  <-  2 comparisons
13 13 14  . . . . . . . . . . . . .|N|O  <-  1 comparisons
14 14 14  . . . . . . . .   . . . . .|O|
          A B C D E F G H I J K L M N O  <- result, 105 comparisons

quicksort: average-case analysis

Proposition: The average number of compares \(C_N\) to quicksort an array of \(N\) distinct keys is \(\sim 2 N \ln N\) (and the number of exchanges is \(\sim\frac{1}{3} N \ln N\))

Pf: \(C_N\) satisfies the recurrence \(C_0 = C_1 = 0\) and for \(N \geq 2\):

\[\scriptsize C_N = \underbrace{(N+1)}_\text{partitioning} + \frac{C_0+C_{N-1}}{N} + \underbrace{\frac{\overbrace{C_1}^\text{left}+\overbrace{C_{N-2}}^\text{right}}{N}}_\text{partitioning probability} + \ldots + \frac{C_{N-1} + C_0}{N}\]

quicksort: average-case analysis

\[\scriptsize C_N = (N+1) + \frac{C_0+C_{N-1}}{N} + \frac{C_1+C_{N-2}}{N} + \ldots + \frac{C_{N-1} + C_0}{N}\]

multiply both sides by \(N\) and collect terms:

\[N C_N = N(N+1) + 2(C_0 + C_1 + \ldots + C_{N-1})\]

subtract from this equation the same equation for \(N-1\):

\[N C_N - (N-1)C_{N-1} = 2N + 2C_{N-1}\]

rearrange terms and divide by \(N(N+1)\):

\[\frac{C_N}{N+1} = \frac{C_{N-1}}{N} + \frac{2}{N+1}\]

quicksort: average-case analysis

repeatedly apply previous step:

\[\begin{array}{rcl} \frac{C_N}{N+1} & = & \frac{C_{N-1}}{N} + \frac{2}{N+1} \\ & = & \frac{C_{N-2}}{N-1} + \frac{2}{N} + \frac{2}{N+1} \\ & = & \frac{C_{N-3}}{N-2} + \frac{2}{N-1} + \frac{2}{N} + \frac{2}{N+1} \\ & = & \frac{2}{3} + \frac{2}{4} + \frac{2}{5} + \ldots + \frac{2}{N+1} \end{array}\]

approximate sum by an integral:

\[\begin{array}{rcl} C_N & = & 2(N+1) \left(\frac{1}{3} + \frac{1}{4} + \frac{1}{5} + \ldots + \frac{1}{N+1}\right) \\ & \sim & 2(N+1) \int_3^{N+1} \frac{1}{x} dx \end{array}\]

quicksort: average-case analysis

finally, the desired result:

\[C_N \sim 2(N+1) \ln N \approx 1.39 N \lg N\]

quicksort: summary of perf characteristics

Quicksort is a (Las Vegas) randomized algorithm

Guaranteed to be correct
Running time depends on random shuffle

Average case: Expected number of compares is \(\sim 1.39 N \lg N\)

39% more compares than merge sort
Faster than merge sort in practice because of less data movement

Best case: Number of compares is \(\sim N \lg N\)

Worst case: Number of compares is \(\sim \frac{1}{2} N^2\) (but more likely that lightning bolt strikes computer during execution!)

quicksort properties

Proposition: Quicksort is an in-place sorting algorithm

Pf:

Partitioning: constant extra space
Depth of recursion: logarithmic extra space (with high probability)
- can guarantee logarithmic depth by recurring on smaller subarray before larger subarray (requires using an explicit stack)

quicksort properties

Proposition: Quicksort is not stable.

Pf (by counterexample):

i j  0  1  2  3
    B1 C1 C2 A1  <- input with partition = B1
1 3 B1 C1 C2 A1  <- found first inversion for partition
1 3 B1 A1 C2 C1  <- swap (oh no!)
  1 A1 B1 C2 C1  <- swap partition in place

quicksort: practical improvements

Insertion sort small subarrays

Even quicksort has too much overhead for tiny subarrays
Cutoff to insertion sort for \(\approx\) 10 items

private static void sort(Comparable[] a, int lo, int hi) {
    if(hi <= lo + CUTOFF - 1) {
        Insertion.sort(a, lo, hi);
        return;
    }
    int j = partition(a, lo, hi);
    sort(a, lo, j-1);
    sort(a, j+1, hi);
}

quicksort: practical improvements

Median of sample

Best choice of pivot item = median
Estimate true median by taking median of sample
Median-of-3 (random) items
- \(\sim 12/7 N \ln N\) compares (14% fewer)
- \(\sim 12/35 N \ln N\) exchanges (3% more)

private static void sort(Comparable[] a, int lo, int hi) {
    if(hi <= lo) return;

    int median = medianOf3(a, lo, lo+(hi-lo)/2, hi);
    swap(a, lo, median); // swap median into lo position

    int j = partition(a, lo, hi);
    sort(a, lo, j-1);
    sort(a, j+1, hi);
}

quicksort: 3-way improvement

When the array has large number of duplicates, partition array into three parts: vals \(< v\), vals \(= v\), and vals \(> v\)

private static void sort(Comparable[] a, int lo, int hi) {
    if(hi <= lo) return;
    int lt = lo, gt = hi;
    Comparable v = a[lo];
    int i = lo + 1;
    while(i <= gt) {
        int cmp = a[i].compareTo(v);
        if(cmp < 0) swap(a, lt++, i++);
        else if(cmp > 0) swap(a, i, gt--);
        else i++;
    }
    // a[lo..lt-1] < v = a[lt..gt] < a[gt+1..hi]
    sort(a, lo, lt - 1);
    sort(a, gt + 1, hi);
}

quicksort 3-way: animation


qs
3-way	random	nearly sorted	reversed	few unique

Key:

Black: sorted. Gray: unsorted. Dark: current subarray.
Red triangle marks algorithm position (i, j)

Note

Data here are not shuffled before sorting, but a random item in subarray is chosen as pivot (equivalent to shuffling).

[ Sorting Algorithm Animations: Quick Sort 3-Way ]

quicksort

selection

Goal: Given an array of \(n\) items, find the \(k^\textit{th}\) smallest item

Examples:

min (\(k=0\))
max (\(k=n-1\))
median (\(k=n/2\))

Applications:

Order statistics
Find the "top \(k\)"

selection

Use theory as a guide

Easy \(n \log n\) upper bound. How?
Easy \(n\) upper bound for \(k=1,2,3\). How?
Easy \(n\) lower bound. Why?

Which is true?

\(n \log n\) lower bound? (is selection as hard as sorting?)
\(n\) upper bound? (is there a linear-time algorithm?)

quick-select

Partition array so that:

Entry a[j] is in place
No larger entry left of j
No smaller entry right of j

Repeat in one subarray, depending on j; finished when j equals k.

public static Comparable select(Comparable[] a, int k) {
    StdRandom.shuffle(a);
    int lo = 0, hi = a.length - 1;
    while (hi > lo) {
        int j = partition(a, lo, hi);
             if (j < k) lo = j + 1;
        else if (j > k) hi = j - 1;
        else            return a[k];
    }
    return a[k];
}

quick-select: mathematical analysis

Proposition: Quick-select takes linear time on average.

Pf sketch

Intuitively, each partitioning step splits array approximately in half: \(n + n/2 + n/4 + ... + 1 \sim 2n\) compares
Formal analysis similar to quicksort analysis yields: \[ \begin{array}{rcl} C_n & = & 2 n + 2 k \ln (n / k) + 2 (n - k) \ln (n / (n - k)) \\ & \leq & (2 + 2 \ln 2) n \end{array} \]
Ex: \((2 + 2 \ln 2) n \approx 3.38n\) compares to find median (\(k = n/2\))

theoretical context for selection

Proposition: Compare-based selection algorithm whose worst-case running time is linear (Blum, Floyd, Pratt, Rivest, Tarjan. 1973)

Remark: Constants are high ⇒ not used in practice

Use theory as a guide

Still worthwhile to seek practical linear-time (worst-case) algorithm
Until one is discovered, use quick-select (if you don't need a full sort)