04 sorting

Sorting Algorithms

rules of the game
shellsort
mergesort
quicksort
animations
Reference:
Algorithms in Java, Chapters 6-8

1

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.

Shellsort.
•Warmup: easy way to break the N2 barrier.
•Embedded systems.

Mergesort.
•Java sort for objects.
•Perl, Python stable sort.

Quicksort.
•Java sort for primitive types.
•C qsort, Unix, g++, Visual C++, Python.
2

rules of the game
shellsort
mergesort
quicksort
animations

3

Basic terms

Ex: student record in a University.

Sort: rearrange sequence of objects into ascending order.

4

Sample sort client

Goal: Sort any type of data
Example. List the files in the current directory, sorted by file name.

import java.io.File;
public class Files
{
public static void main(String[] args)
{
File directory = new File(args[0]);
File[] files = directory.listFiles();
Insertion.sort(files); % java Files .
for (int i = 0; i < files.length; i++) Insertion.class
Insertion.java
System.out.println(files[i]); InsertionX.class
} InsertionX.java
Selection.class
} Selection.java
Shell.class
Shell.java
ShellX.class
Next: How does sort compare file names?
ShellX.java
index.html

5

Callbacks

Goal. Write robust sorting library method that can sort
any type of data using the data type's natural order.

Callbacks.
• Client passes array of objects to sorting routine.
• Sorting routine calls back object's comparison function as needed.

Implementing callbacks.
• Java: interfaces.
• C: function pointers.
• C++: functors.

6

Callbacks
client
import java.io.File;
public class SortFiles object implementation
{ public class File
public static void main(String[] args) implements Comparable<File>
{ {
File directory = new File(args[0]);
...
File[] files = directory.listFiles();
public int compareTo(File b)
Insertion.sort(files); {
for (int i = 0; i < files.length; i++) ...
System.out.println(files[i]); return -1;
}
...
}
return +1;
...
interface return 0;
built in to Java }
interface Comparable <Item>
}
{
public int compareTo(Item);
sort implementation
}
public static void sort(Comparable[] a)
{
int N = a.length;
for (int i = 0; i < N; i++)
Key point: no reference to File for (int j = i; j > 0; j--)
if (a[j].compareTo(a[j-1]))
exch(a, j, j-1);
else break;
}
7

Callbacks

Goal. Write robust sorting library that can sort any type of data
into sorted order using the data type's natural order.

Callbacks.
• Client passes array of objects to sorting routine.
• Sorting routine calls back object's comparison function as needed.

Implementing callbacks.
• Java: interfaces.
• C: function pointers.
• C++: functors.

Plus: Code reuse for all types of data
Minus: Significant overhead in inner loop

This course:
• enables focus on algorithm implementation
• use same code for experiments, real-world data 8

Interface specification for sorting

Comparable interface.
Must implement method compareTo() so that v.compareTo(w)returns:
• a negative integer if v is less than w
• a positive integer if v is greater than w
• zero if v is equal to w

Consistency.
Implementation must ensure a total order.
• if (a < b) and (b < c), then (a < c).
• either (a < b) or (b < a) or (a = b).

Built-in comparable types. String, Double, Integer, Date, File.
User-defined comparable types. Implement the Comparable interface.

9

Implementing the Comparable interface: example 1

Date data type (simplified version of built-in Java code)

public class Date implements Comparable<Date>
{
private int month, day, year; only compare dates
to other dates
public Date(int m, int d, int y)
{
month = m;
day = d;
year = y;
}

public int compareTo(Date b)
{
Date a = this;
if (a.year < b.year ) return -1;
if (a.year > b.year ) return +1;
if (a.month < b.month) return -1;
if (a.month > b.month) return +1;
if (a.day < b.day ) return -1;
if (a.day > b.day ) return +1;
return 0;
}
}
10

Implementing the Comparable interface: example 2

Domain names
• Subdomain: bolle.cs.princeton.edu.
• Reverse subdomain: edu.princeton.cs.bolle.
• Sort by reverse subdomain to group by category. unsorted
ee.princeton.edu
cs.princeton.edu
public class Domain implements Comparable<Domain> princeton.edu
{ cnn.com
private String[] fields; google.com
private int N; apple.com
public Domain(String name) www.cs.princeton.edu
{ bolle.cs.princeton.edu
fields = name.split(".");
N = fields.length;
} sorted
public int compareTo(Domain b)
{ com.apple
com.cnn
Domain a = this; com.google
for (int i = 0; i < Math.min(a.N, b.N); i++) edu.princeton
{ edu.princeton.cs
int c = a.fields[i].compareTo(b.fields[i]); edu.princeton.cs.bolle
if (c < 0) return -1; edu.princeton.cs.www
else if (c > 0) return +1; edu.princeton.ee
}
return a.N - b.N;
}
} details included for the bored... 11

Sample sort clients

File names Random numbers
import java.io.File; public class Experiment
public class Files {
{ public static void main(String[] args)
public static void main(String[] args) {
{ int N = Integer.parseInt(args[0]);
File directory = new File(args[0]); Double[] a = new Double[N];
for (int i = 0; i < N; i++)
File[] files = directory.listFiles()
a[i] = Math.random();
Insertion.sort(files); Selection.sort(a);
for (int i = 0; i < files.length; i++) for (int i = 0; i < N; i++)
System.out.println(files[i]); System.out.println(a[i]);
} }
}
}
% java Files . % java Experiment 10
Insertion.class 0.08614716385210452
Insertion.java 0.09054270895414829
InsertionX.class 0.10708746304898642
InsertionX.java 0.21166190071646818
Selection.class 0.363292849257276
Selection.java 0.460954145685913
Shell.class 0.5340026311350087
Shell.java 0.7216129793703496
0.9003500354411443
0.9293994908845686

Several Java library data types implement Comparable
You can implement Comparable for your own types
12

Two useful abstractions

Helper functions. Refer to data only through two operations.

• less. Is v less than w ?

private static boolean less(Comparable v, Comparable w)
{
return (v.compareTo(w) < 0);
}

• exchange. Swap object in array at index i with the one at index j.

private static void exch(Comparable[] a, int i, int j)
{
Comparable t = a[i];
a[i] = a[j];
a[j] = t;
}

13

Sample sort implementations

selection sort public class Selection
{
{
int N = a.length;
for (int i = 0; i < N; i++)
{
int min = i;
for (int j = i+1; j < N; j++)
if (less(a, j, min)) min = j;
exch(a, i, min);
}
}
...
}

insertion sort public class Insertion
{
{
int N = a.length;
for (int i = 1; i < N; i++)
for (int j = i; j > 0; j--)
if (less(a[j], a[j-1]))
exch(a, j, j-1);
else break;
}
...
}
14

Why use less() and exch() ?

Switch to faster implementation for primitive types
private static boolean less(double v, double w)
{
return v < w;
}

Instrument for experimentation and animation

private static boolean less(double v, double
w)
{
cnt++;
return v < w;

Translate to other languages

...
for (int i = 1; i < a.length; i++) Good code in C, C++,
if (less(a[i], a[i-1])) JavaScript, Ruby....
return false;
return true;}
15

Properties of elementary sorts (review)

Selection sort Insertion sort

a[i] a[i]
i min 0 1 2 3 4 5 6 7 8 9 10 i j 0 1 2 3 4 5 6 7 8 9 10
S O R T E X A M P L E S O R T E X A M P L E
0 6 S O R T E X A M P L E 1 0 O S R T E X A M P L E
1 4 A O R T E X S M P L E 2 1 O R S T E X A M P L E
2 10 A E R T O X S M P L E 3 3 O R S T E X A M P L E
3 9 A E E T O X S M P L R 4 0 E O R S T X A M P L E
4 7 A E E L O X S M P T R 5 5 E O R S T X A M P L E
5 7 A E E L M X S O P T R 6 0 A E O R S T X M P L E
6 8 A E E L M O S X P T R 7 2 A E M O R S T X P L E
7 10 A E E L M O P X S T R 8 4 A E M O P R S T X L E
8 8 A E E L M O P R S T X 9 2 A E L M O P R S T X E
9 9 A E E L M O P R S T X 10 2 A E E L M O P R S T X
10 10 A E E L M O P R S T X
A E E L M O P R S T X A E E L M O P R S T X

Running time: Quadratic (~c N2) Running time: Quadratic (~c N2)
Exception: expensive exchanges Exception: input nearly in order
(could be linear) (could be linear)

Bottom line: both are quadratic (too slow) for large randomly ordered files
16

rules of the game
shellsort
mergesort
quicksort
animations

17

Visual representation of insertion sort
left of pointer is in sorted order right of pointer is untouched

a[i]

i
Reason it is slow: data movement 18

Shellsort

Idea: move elements more than one position at a time
by h-sorting the file for a decreasing sequence of values of h

input S O R T E X A M P L E 1-sort A E L E O P M S X R T
7-sort
A E L E O P M S X R T
M O R T E X A S P L E
A E E L O P M S X R T
M O R T E X A S P L E
M O L T E X A S P R E
M O L E E X A S P R T
A E E L M O P S X R T
3-sort
E O L M E X A S P R T
E E L M O X A S P R T
A E E L M O P R S X T
E E L M O X A S P R T
A E E L M O P R S T X
A E L E O X M S P R T
A E E L M O P R S T X
A E L E O X M S P R T
result A E E L M O P R S T X

a 3-sorted file is A E L E O P M S X R T
3 interleaved sorted files A E M R
E O S T
L P X
19

Shellsort

Idea: move elements more than one position at a time
by h-sorting the file for a decreasing sequence of values of h

Use insertion sort, modified to h-sort

magic increment
sequence

big increments: public static void sort(double[] a)
small subfiles {
int N = a.length;
small increments: int[] incs = { 1391376, 463792, 198768, 86961,
subfiles nearly in order 33936, 13776, 4592, 1968, 861,
336, 112, 48, 21, 7, 3, 1 };
for (int k = 0; k < incs.length; k++)
method of choice for both {
int h = incs[k];
small subfiles
for (int i = h; i < N; i++)
subfiles nearly in order for (int j = i; j >= h; j-= h)
if (less(a[j], a[j-h]))
insertion sort!
exch(a, j, j-h);
else break;
}
}

20

Visual representation of shellsort
big increment

small increment

Bottom line: substantially faster! 21

Analysis of shellsort

Model has not yet been discovered (!)

N comparisons N1.289 2.5 N lg N

5,000 93 58 106

10,000 209 143 230

20,000 467 349 495

40,000 1022 855 1059

80,000 2266 2089 2257

measured in thousands

22

Why are we interested in shellsort?

Example of simple idea leading to substantial performance gains

Useful in practice
• fast unless file size is huge
• tiny, fixed footprint for code (used in embedded systems)
• hardware sort prototype

Simple algorithm, nontrivial performance, interesting questions
• asymptotic growth rate?
• best sequence of increments?
• average case performance?

Your first open problem in algorithmics (see Section 6.8):
Find a better increment sequence
mail rs@cs.princeton.edu

Lesson: some good algorithms are still waiting discovery 23

rules of the game
shellsort
mergesort
quicksort
animations

24

Mergesort (von Neumann, 1945(!))

Basic plan: plan

• Divide array into two halves. M E R G E S O R T E X A M P L E

• Recursively sort each half. E E G M O R R S T E X A M P L E

•
E E G M O R R S A E E L M P T X
Merge two halves. A E E E E G L M M O P R R S T X

trace
a[i]
lo hi 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
M E R G E S O R T E X A M P L E
0 1 E M R G E S O R T E X A M P L E
2 3 E M G R E S O R T E X A M P L E
0 3 E G M R E S O R T E X A M P L E
4 7 E G M R E O R S T E X A M P L E
0 7 E E G M O R R S T E X A M P L E
8 9 E E G M O R R S E T X A M P L E
10 11 E E G M O R R S E T A X M P L E
8 11 E E G M O R R S A E T X M P L E
12 13 E E G M O R R S A E T X M P L E
14 15 E E G M O R R S A E T X M P E L
12 15 E E G M O R R S A E T X E L M P
8 15 E E G M O R R S A E E L M P T X
0 15 A E E E E G L M M O P R R S T X
25

Merging

Merging. Combine two pre-sorted lists into a sorted whole.

How to merge efficiently? Use an auxiliary array.
l i m j r
aux[] A G L O R H I M S T
k
a[] A G H I L M

private static void merge(Comparable[] a,
Comparable[] aux, int l, int m, int r)
{
copy for (int k = l; k < r; k++) aux[k] = a[k];
int i = l, j = m;
for (int k = l; k < r; k++)
if (i >= m) a[k] = aux[j++]; see book for a trick
merge else if (j >= r) a[k] = aux[i++]; to eliminate these
else if (less(aux[j], aux[i])) a[k] = aux[j++];
else a[k] = aux[i++];

}

26

Mergesort: Java implementation of recursive sort

public class Merge
{
private static void sort(Comparable[] a,
Comparable[] aux, int lo, int hi)
{
if (hi <= lo + 1) return;
int m = lo + (hi - lo) / 2;
sort(a, aux, lo, m);
sort(a, aux, m, hi);
merge(a, aux, lo, m, hi);
}

{
Comparable[] aux = new Comparable[a.length];
sort(a, aux, 0, a.length);
}
}

lo m hi

10 11 12 13 14 15 16 17 18 19
27

Mergesort analysis: Memory

Q. How much memory does mergesort require?
A. Too much!
• Original input array = N.
• Auxiliary array for merging = N.
• Local variables: constant.
• Function call stack: log2 N [stay tuned].
• Total = 2N + O(log N).
cannot “fill the memory and sort”

Q. How much memory do other sorting algorithms require?
• N + O(1) for insertion sort and selection sort.
• In-place = N + O(log N).

Challenge for the bored. In-place merge. [Kronrud, 1969]

28

Mergesort analysis

Def. T(N) number of array stores to mergesort an input of size N
= T(N/2) + T(N/2) + N

left half right half merge

Mergesort recurrence T(N) = 2 T(N/2) + N
for N > 1, with T(1) = 0

• not quite right for odd N
• same recurrence holds for many algorithms
• same for any input of size N
• comparison count slightly smaller because of array ends
lg N log2 N
Solution of Mergesort recurrence T(N) ~ N lg N

• true for all N
• easy to prove when N is a power of 2
29

Mergesort recurrence: Proof 1 (by recursion tree)

T(N) = 2 T(N/2) + N
(assume that N is a power of 2)
for N > 1, with T(1) = 0

T(N)
+

T(N/2) T(N/2) N = N

T(N/4) T(N/4) T(N/4) T(N/4) 2(N/2) = N
...

lg N
T(N / 2k) 2k(N/2k) = N

...

T(2) T(2) T(2) T(2) T(2) T(2) T(2) T(2) N/2 (2) = N

N lg N
T(N) = N lg N
30

Mergesort recurrence: Proof 2 (by telescoping)

T(N) = 2 T(N/2) + N
for N > 1, with T(1) = 0

Pf. T(N) = 2 T(N/2) + N given

T(N)/N = 2 T(N/2)/N + 1 divide both sides by N

= T(N/2)/(N/2) + 1 algebra

= T(N/4)/(N/4) + 1 + 1 telescope (apply to first term)

= T(N/8)/(N/8) + 1 + 1 + 1 telescope again

...

= T(N/N)/(N/N) + 1 + 1 +. . .+ 1 stop telescoping, T(1) = 0

= lg N

T(N) = N lg N
31

Mergesort recurrence: Proof 3 (by induction)

T(N) = 2 T(N/2) + N
for N > 1, with T(1) = 0

Claim. If T(N) satisfies this recurrence, then T(N) = N lg N.
Pf. [by induction on N]
• Base case: N = 1.
• Inductive hypothesis: T(N) = N lg N
• Goal: show that T(2N) + 2N lg (2N).

T(2N) = 2 T(N) + 2N given
= 2 N lg N + 2 N inductive hypothesis
= 2 N (lg (2N) - 1) + 2N algebra
= 2 N lg (2N) QED

Ex. (for COS 340). Extend to show that T(N) ~ N lg N for general N
32

Bottom-up mergesort

Basic plan:
• Pass through file, merging to double size of sorted subarrays.
• Do so for subarray sizes 1, 2, 4, 8, . . . , N/2, N. proof 4 that mergesort
uses N lgN compares
a[i]
lo hi 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
M E R G E S O R T E X A M P L E
0 1 E M R G E S O R T E X A M P L E
8 9 E M G R E S O R E T X A M P L E
10 11 E M G R E S O R E T A X M P L E
12 13 E M G R E S O R E T A X M P L E
14 15 E M G R E S O R E T A X M P E L
0 3 E G M R E S O R E T A X M P E L
4 7 E G M R E O R S E T A X M P E L
8 11 E E G M O R R S A E T X M P E L
8 15 E E G M O R R S A E E L M P T X
0 15 A E E E E G L M M O P R R S T X

No recursion needed!
33

Bottom-up Mergesort: Java implementation

public class Merge
{
private static void merge(Comparable[] a, Comparable[] aux,
int l, int m, int r)
{
for (int i = l; i < m; i++) aux[i] = a[i];
for (int j = m; j < r; j++) aux[j] = a[m + r - j - 1];
tricky merge int i = l, j = r - 1;
that uses sentinel for (int k = l; k < r; k++)
(see Program 8.2) if (less(aux[j], aux[i])) a[k] = aux[j--];
else a[k] = aux[i++];

}

{
int N = a.length;
Comparable[] aux = new Comparable[N];
for (int m = 1; m < N; m = m+m)
for (int i = 0; i < N-m; i += m+m)
merge(a, aux, i, i+m, Math.min(i+m+m, N));
}
}

Concise industrial-strength code if you have the space 34

Mergesort: Practical Improvements

Use sentinel.
• Two statements in inner loop are array-bounds checking.
• Reverse one subarray so that largest element is sentinel (Program 8.2)

Use insertion sort on small subarrays.
• Mergesort has too much overhead for tiny subarrays.
• Cutoff to insertion sort for 7 elements.

Stop if already sorted.
• Is biggest element in first half smallest element in second half?
• Helps for nearly ordered lists.

Eliminate the copy to the auxiliary array. Save time (but not space) by
switching the role of the input and auxiliary array in each recursive call.

See Program 8.4 (or Java system sort)
35

Sorting Analysis Summary

Running time estimates:
• Home pc executes 108 comparisons/second.
• Supercomputer executes 1012 comparisons/second.

Insertion Sort (N2) Mergesort (N log N)
computer thousand million billion thousand million billion
home instant 2.8 hours 317 years instant 1 sec 18 min
super instant 1 second 1.6 weeks instant instant instant

Lesson. Good algorithms are better than supercomputers.

Good enough?

18 minutes might be too long for some applications

36

rules of the game
shellsort
mergesort
quicksort
animations

37

Quicksort (Hoare, 1959)

Basic plan.
• Shuffle the array.
• Partition so that, for some i
element a[i] is in place
no larger element to the left of i
no smaller element to the right of i
• Sort each piece recursively. Sir Charles Antony Richard Hoare
1980 Turing Award

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

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

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

sort left part A C E E I K L P U T M Q R X O S

sort right part A C E E I K L M O P Q R S T U X

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

result
38

Quicksort: Java code for recursive sort

public class Quick
{

{
StdRandom.shuffle(a);
sort(a, 0, a.length - 1);
}

private static void sort(Comparable[] a, int l, int r)
{
if (r <= l) return;
int m = partition(a, l, r);
sort(a, l, m-1);
sort(a, m+1, r);
}
}

39

Quicksort trace

a[i]
input l r i 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Q U I C K S O R T E X A M P L E
randomize E R A T E S L P U I M Q C X O K
partition 0 15 5 E C A I E K L P U T M Q R X O S
0 4 2 A C E I E K L P U T M Q R X O S
0 1 1 A C E I E K L P U T M Q R X O S
0 0 A C E I E K L P U T M Q R X O S
3 4 3 A C E E I K L P U T M Q R X O S
4 4 A C E E I K L P U T M Q R X O S
6 15 12 A C E E I K L P O R M Q S X U T
6 11 10 A C E E I K L P O M Q R S X U T
6 9 7 A C E E I K L M O P Q R S X U T
no partition for 6 6 A C E E I K L M O P Q R S X U T
subfiles of size 1 8 9 9 A C E E I K L M O P Q R S X U T
8 8 A C E E I K L M O P Q R S X U T
11 11 A C E E I K L M O P Q R S X U T
13 15 13 A C E E I K L M O P Q R S T U X
14 15 15 A C E E I K L M O P Q R S T U X
14 14 A C E E I K L M O P Q R S T U X
A C E E I K L M O P Q R S T U X

array contents after each recursive sort
40

Quicksort partitioning

Basic plan:
• scan from left for an item that belongs on the right
• scan from right for item item that belongs on the left
• exchange
• continue until pointers cross

a[i]

i j r 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

-1 15 15 E R A T E S L P U I M Q C X O K

scans 1 12 15 E R A T E S L P U I M Q C X O K

exchange 1 12 15 E C A T E S L P U I M Q R X O K

3 9 15 E C A T E S L P U I M Q R X O K

3 9 15 E C A I E S L P U T M Q R X O K

5 5 15 E C A I E S L P U T M Q R X O K

5 5 15 E C A I E K L P U T M Q R X O S

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

array contents before and after each exchange
41

Quicksort: Java code for partitioning

private static int partition(Comparable[] a, int l, int r)
{
int i = l - 1;
int j = r; v
while(true)
{ i j

find item on left to swap
while (less(a[++i], a[r]))
if (i == r) break;

while (less(a[r], a[--j])) find item on right to swap
if (j == l) break;
<= v >= v v

if (i >= j) break; check if pointers cross
i j

exch(a, i, j); swap
}

exch(a, i, r); swap with partitioning item
<= v v >= v
return i; return index of item now known to be in place
} i

42

Quicksort Implementation details

Partitioning in-place. Using a spare array makes partitioning easier,
but is not worth the cost.

Terminating the loop. Testing whether the pointers cross is a bit
trickier than it might seem.

Staying in bounds. The (i == r) test is redundant, but the (j == l)
test is not.

Preserving randomness. Shuffling is key for performance guarantee.

Equal keys. When duplicates are present, it is (counter-intuitively)
best to stop on elements equal to partitioning element.

43

Quicksort: Average-case analysis

Theorem. The average number of comparisons CN to quicksort a
random file of N elements is about 2N ln N.

• The precise recurrence satisfies C 0 = C1 = 0 and for N 2:
CN = N + 1 + ((C0 + CN-1) + . . . + (Ck-1 + CN-k) + . . . + (CN-1 + C1)) / N

partition left right partitioning
probability
= N + 1 + 2 (C0 . . . + Ck-1 + . . . + CN-1) / N

• Multiply both sides by N
NCN = N(N + 1) + 2 (C0 . . . + Ck-1 + . . . + CN-1)

• Subtract the same formula for N-1:
NCN - (N - 1)CN-1 = N(N + 1) - (N - 1)N + 2 CN-1

• Simplify:
NCN = (N + 1)CN-1 + 2N
44

Quicksort: Average Case

NCN = (N + 1)CN-1 + 2N

• Divide both sides by N(N+1) to get a telescoping sum:
CN / (N + 1) = CN-1 / N + 2 / (N + 1)

= CN-2 / (N - 1) + 2/N + 2/(N + 1)

= CN-3 / (N - 2) + 2/(N - 1) + 2/N + 2/(N + 1)

= 2 ( 1 + 1/2 + 1/3 + . . . + 1/N + 1/(N + 1) )

• Approximate the exact answer by an integral:
CN 2(N + 1)( 1 + 1/2 + 1/3 + . . . + 1/N )
N

= 2(N + 1) HN 2(N + 1) dx/x
1

• Finally, the desired result:
CN 2(N + 1) ln N 1.39 N lg N
45

Quicksort: Summary of performance characteristics

Worst case. Number of comparisons is quadratic.
•N + (N-1) + (N-2) + … + 1 N2 / 2.
•More likely that your computer is struck by lightning.

Average case. Number of comparisons is ~ 1.39 N lg N.
• 39% more comparisons than mergesort.
• but faster than mergesort in practice because of lower cost of
other high-frequency operations.

Random shuffle
• probabilistic guarantee against worst case
• basis for math model that can be validated with experiments

Caveat emptor. Many textbook implementations go quadratic if input:
• Is sorted.
• Is reverse sorted.
• Has many duplicates (even if randomized)! [stay tuned]
46

Sorting analysis summary

Running time estimates:
• Home pc executes 108 comparisons/second.
• Supercomputer executes 1012 comparisons/second.
Insertion Sort (N2) Mergesort (N log N)
computer thousand million billion thousand million billion
home instant 2.8 hours 317 years instant 1 sec 18 min
super instant 1 second 1.6 weeks instant instant instant

Quicksort (N log N)
thousand million billion
instant 0.3 sec 6 min
instant instant instant

Lesson 1. Good algorithms are better than supercomputers.
Lesson 2. Great algorithms are better than good ones.

47

Quicksort: Practical improvements

Median of sample.
• Best choice of pivot element = median.
• But how to compute the median?
• Estimate true median by taking median of sample.

Insertion sort small files.
• Even quicksort has too much overhead for tiny files.
• Can delay insertion sort until end.

Optimize parameters. 12/7 N log N comparisons

• Median-of-3 random elements.
• Cutoff to insertion sort for 10 elements.

Non-recursive version.
• Use explicit stack. guarantees O(log N) stack size

• Always sort smaller half first.

All validated with refined math models and experiments 48

rules of the game
shellsort
mergesort
quicksort
animations

49

Mergesort animation merge in progress
input
done untouched

merge in progress
auxiliary array
output 50

Bottom-up mergesort animation merge in progress
input
this pass last pass

merge in progress
auxiliary array
output 51

Quicksort animation

i

first partition

v

second partition

done j
52

04 sorting

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04 sorting