3.ppt

Chapter 10: Sorting 1
Sorting
Based on Chapter 10 of
Koffmann and Wolfgang

Chapter Outline
• How to use standard sorting methods in the Java API
• How to implement these sorting algorithms:
• Selection sort
• Bubble sort
• Insertion sort
• Shell sort
• Merge sort
• Heapsort
• Quicksort

Chapter Outline (2)
• Understand the performance of these algorithms
• Which to use for small arrays
• Which to use for medium arrays
• Which to use for large arrays

Using Java API Sorting Methods
• Java API provides a class Arrays with several
overloaded sort methods for different array types
• Class Collections provides similar sorting methods
• Sorting methods for arrays of primitive types:
• Based on the Quicksort algorithm
• Method of sorting for arrays of objects (and List):
• Based on Mergesort
• In practice you would tend to use these
• In this class, you will implement some yourself

Java API Sorting Interface
Arrays methods:
public static void sort (int[] a)
public static void sort (Object[] a)
// requires Comparable
public static <T> void sort (T[] a,
Comparator<? super T> comp)
// uses given Comparator
• These also have versions giving a fromIndex/toIndex
range of elements to sort

Java API Sorting Interface (2)
Collections methods:
public static <T extends Comparable<T>>
void sort (List<T> list)
public static <T> void sort (List<T> l,
Comparator<? super T> comp)
• Note that these are generic methods, in effect having
different versions for each type T
• In reality, there is only one code body at run time

Using Java API Sorting Methods
int[] items;
Arrays.sort(items, 0, items.length / 2);
Arrays.sort(items);
public class Person
implements Comparable<Person> { ... }
Person[] people;
Arrays.sort(people);
// uses Person.compareTo
public class ComparePerson
implements Comparator<Person> { ... }
Arrays.sort(people, new ComparePerson());
// uses ComparePerson.compare

Using Java API Sorting Methods (2)
List<Person> plist;
Collections.sort(plist);
// uses Person.compareTo
Collections.sort(plist,
new ComparePerson());
// uses ComparePerson.compare

Conventions of Presentation
• Write algorithms for arrays of Comparable objects
• For convenience, examples show integers
• These would be wrapped as Integer; or
• You can implement separately for int arrays
• Generally use n for the length of the array
• Elements 0 through n-1

Selection Sort
• A relatively easy to understand algorithm
• Sorts an array in passes
• Each pass selects the next smallest element
• At the end of the pass, places it where it belongs
• Efficiency is O(n2), hence called a quadratic sort
• Performs:
• O(n2) comparisons
• O(n) exchanges (swaps)

Selection Sort Algorithm
1. for fill = 0 to n-2 do // steps 2-6 form a pass
2. set posMin to fill
3. for next = fill+1 to n-1 do
4. if item at next < item at posMin
5. set posMin to next
6. Exchange item at posMin with one at fill

Selection Sort Example
35 65 30 60 20 scan 0-4, smallest 20
swap 35 and 20
20 65 30 60 35 scan 1-4, smallest 30
swap 65 and 30
20 30 65 60 35 scan 2-4, smallest 35
swap 65 and 35
20 30 35 60 65 scan 3-4, smallest 60
swap 60 and 60
20 30 35 60 65 done

Selection Sort Code
void sort (T[] a) {
int n = a.length;
for (int fill = 0; fill < n-1; fill++) {
int posMin = fill;
for (int nxt = fill+1; nxt < n; nxt++)
if (a[nxt].compareTo(a[posMin])<0)
posMin = nxt;
T tmp = a[fill];
a[fill] = a[posMin];
a[posMin] = tmp;
}
}

Bubble Sort
• Compares adjacent array elements
• Exchanges their values if they are out of order
• Smaller values bubble up to the top of the array
• Larger values sink to the bottom

Bubble Sort Example

Bubble Sort Algorithm
1. do
2. for each pair of adjacent array elements
3. if values are out of order
4. Exchange the values
5. while the array is not sorted

Bubble Sort Algorithm, Refined
1. do
2. Initialize exchanges to false
3. for each pair of adjacent array elements
4. if values are out of order
5. Exchange the values
6. Set exchanges to true
7. while exchanges

Analysis of Bubble Sort
• Excellent performance in some cases
• But very poor performance in others!
• Works best when array is nearly sorted to begin with
• Worst case number of comparisons: O(n2)
• Worst case number of exchanges: O(n2)
• Best case occurs when the array is already sorted:
• O(n) comparisons
• O(1) exchanges (none actually)

Bubble Sort Code
int pass = 1;
boolean exchanges;
do {
exchanges = false;
for (int i = 0; i < a.length-pass; i++)
if (a[i].compareTo(a[i+1]) > 0) {
T tmp = a[i];
a[i] = a[i+1];
a[i+1] = tmp;
exchanges = true;
}
pass++;
} while (exchanges);

Insertion Sort
• Based on technique of card players to arrange a hand
• Player keeps cards picked up so far in sorted order
• When the player picks up a new card
• Makes room for the new card
• Then inserts it in its proper place

Insertion Sort Algorithm
• For each element from 2nd (nextPos = 1) to last:
• Insert element at nextPos where it belongs
• Increases sorted subarray size by 1
• To make room:
• Hold nextPos value in a variable
• Shuffle elements to the right until gap at right place

Insertion Sort Example

Insertion Sort Code
void sort (T[] a) {
for (int nextPos = 1;
nextPos < a.length;
nextPos++) {
insert(a, nextPos);
}
}

Insertion Sort Code (2)
private static <T extends Comparable<T>>
void insert (T[] a, int nextPos) {
T nextVal = a[nextPos];
while
(nextPos > 0 &&
nextVal.compareTo(a[nextPos-1]) < 0){
a[nextPos] = a[nextPos-1];
nextPos--;
}
a[nextPos] = nextVal;
}

Analysis of Insertion Sort
• Maximum number of comparisons: O(n2)
• In the best case, number of comparisons: O(n)
• # shifts for an insertion = # comparisons - 1
• When new value smallest so far, # comparisons
• A shift in insertion sort moves only one item
• Bubble or selection sort exchange: 3 assignments

Comparison of Quadratic Sorts
• None good for large arrays!

Shell Sort: A Better Insertion Sort
• Shell sort is a variant of insertion sort
• It is named after Donald Shell
• Average performance: O(n3/2) or better
• Divide and conquer approach to insertion sort
• Sort many smaller subarrays using insertion sort
• Sort progressively larger arrays
• Finally sort the entire array
• These arrays are elements separated by a gap
• Start with large gap
• Decrease the gap on each “pass”

Shell Sort: The Varying Gap
Before and after sorting with gap = 7
Before and after sorting with gap = 3

Analysis of Shell Sort
• Intuition:
Reduces work by moving elements farther earlier
• Its general analysis is an open research problem
• Performance depends on sequence of gap values
• For sequence 2k, performance is O(n2)
• Hibbard’s sequence (2k-1), performance is O(n3/2)
• We start with n/2 and repeatedly divide by 2.2
• Empirical results show this is O(n5/4) or O(n7/6)
• No theoretical basis (proof) that this holds

Shell Sort Algorithm
1. Set gap to n/2
2. while gap > 0
3. for each element from gap to end, by gap
4. Insert element in its gap-separated sub-array
5. if gap is 2, set it to 1
6. otherwise set it to gap / 2.2

Shell Sort Algorithm: Inner Loop
3.1 set nextPos to position of element to insert
3.2 set nextVal to value of that element
3.3 while nextPos > gap and
element at nextPos-gap is > nextVal
3.4 Shift element at nextPos-gap to nextPos
3.5 Decrement nextPos by gap
3.6 Insert nextVal at nextPos

Shell Sort Code
public static <T extends <Comparable<T>>
void sort (T[] a) {
int gap = a.length / 2;
while (gap > 0) {
for (int nextPos = gap;
nextPos < a.length; nextPos++)
insert(a, nextPos, gap);
if (gap == 2)
gap = 1;
else
gap = (int)(gap / 2.2);
}
}

Shell Sort Code (2)
void insert
(T[] a, int NextPos, int gap) {
T val = a[nextPos];
while ((nextPos >= gap) &&
(val.compareTo(a[nextPos-gap])<0)) {
a[nextPos] = a[nextPos-gap];
nextPos -= gap;
}
a[nextPos] = val;
}

Merge Sort
• A merge is a common data processing operation:
• Performed on two sequences of data
• Items in both sequences use same compareTo
• Both sequences in ordered of this compareTo
• Goal: Combine the two sorted sequences in one
larger sorted sequence
• Merge sort merges longer and longer sequences

Merge Algorithm (Two Sequences)
Merging two sequences:
1. Access the first item from both sequences
2. While neither sequence is finished
1. Compare the current items of both
2. Copy smaller current item to the output
3. Access next item from that input sequence
3. Copy any remaining from first sequence to output
4. Copy any remaining from second to output

Picture of Merge

Analysis of Merge
• Two input sequences, total length n elements
• Must move each element to the output
• Merge time is O(n)
• Must store both input and output sequences
• An array cannot be merged in place
• Additional space needed: O(n)

Merge Sort Algorithm
Overview:
• Split array into two halves
• Sort the left half (recursively)
• Sort the right half (recursively)
• Merge the two sorted halves

Merge Sort Algorithm (2)
Detailed algorithm:
• if tSize  1, return (no sorting required)
• set hSize to tSize / 2
• Allocate LTab of size hSize
• Allocate RTab of size tSize – hSize
• Copy elements 0 .. hSize – 1 to LTab
• Copy elements hSize .. tSize – 1 to RTab
• Sort LTab recursively
• Sort RTab recursively
• Merge LTab and RTab into a

Merge Sort Example

Merge Sort Analysis
• Splitting/copying n elements to subarrays: O(n)
• Merging back into original array: O(n)
• Recursive calls: 2, each of size n/2
• Their total non-recursive work: O(n)
• Next level: 4 calls, each of size n/4
• Non-recursive work again O(n)
• Size sequence: n, n/2, n/4, ..., 1
• Number of levels = log n
• Total work: O(n log n)

Merge Sort Code
void sort (T[] a) {
if (a.length <= 1) return;
int hSize = a.length / 2;
T[] lTab = (T[])new Comparable[hSize];
T[] rTab =
(T[])new Comparable[a.length-hSize];
System.arraycopy(a, 0, lTab, 0, hSize);
System.arraycopy(a, hSize, rTab, 0,
a.length-hSize);
sort(lTab); sort(rTab);
merge(a, lTab, rTab);
}

Merge Sort Code (2)
void merge (T[] a, T[] l, T[] r) {
int i = 0; // indexes l
int j = 0; // indexes r
int k = 0; // indexes a
while (i < l.length && j < r.length)
if (l[i].compareTo(r[j]) < 0)
a[k++] = l[i++];
else
a[k++] = r[j++];
while (i < l.length) a[k++] = l[i++];
while (j < r.length) a[k++] = r[j++];
}

Heapsort
• Merge sort time is O(n log n)
• But requires (temporarily) n extra storage items
• Heapsort
• Works in place: no additional storage
• Offers same O(n log n) performance
• Idea (not quite in-place):
• Insert each element into a priority queue
• Repeatedly remove from priority queue to array
• Array slots go from 0 to n-1

Heapsort Picture

Heapsort Picture (2)

Algorithm for In-Place Heapsort
• Build heap starting from unsorted array
• While the heap is not empty
• Remove the first item from the heap:
• Swap it with the last item
• Restore the heap property

Heapsort Code
void sort (T[] a) {
buildHp(a);
shrinkHp(a);
}
private static ... void buildHp (T[] a) {
for (int n = 2; n <= a.length; n++) {
int chld = n-1; // add item and reheap
int prnt = (chld-1) / 2;
while (prnt >= 0 &&
a[prnt].compareTo(a[chld])<0) {
swap(a, prnt, chld);
chld = prnt; prnt = (chld-1)/2
} } }

Heapsort Code (2)
private static ... void shrinkHp (T[] a) {
int n = a.length;
for (int n = a.length-1; n > 0; --n) {
swap(a, 0, n); // max -> next posn
int prnt = 0;
while (true) {
int lc = 2 * prnt + 1;
if (lc >= n) break;
int rc = lc + 1;
int maxc = lc;
if (rc < n &&
a[lc].compareTo(a[rc]) < 0)
maxc = rc;
....

Heapsort Code (3)
if (a[prnt].compareTo(a[maxc])<0) {
swap(a, prnt, maxc);
prnt = maxc;
} else {
break;
}
}
}
}
private static ... void swap
(T[] a, int i, int j) {
T tmp = a[i]; a[i] = a[j]; a[j] = tmp;
}

Heapsort Analysis
• Insertion cost is log i for heap of size i
• Total insertion cost = log(n)+log(n-1)+...+log(1)
• This is O(n log n)
• Removal cost is also log i for heap of size i
• Total removal cost = O(n log n)
• Total cost is O(n log n)

Quicksort
• Developed in 1962 by C. A. R. Hoare
• Given a pivot value:
• Rearranges array into two parts:
• Left part  pivot value
• Right part > pivot value
• Average case for Quicksort is O(n log n)
• Worst case is O(n2)

Quicksort Example

Algorithm for Quicksort
first and last are end points of region to sort
• if first < last
• Partition using pivot, which ends in pivIndex
• Apply Quicksort recursively to left subarray
• Apply Quicksort recursively to right subarray
Performance: O(n log n) provide pivIndex not always
too close to the end
Performance O(n2) when pivIndex always near end

Quicksort Code
void sort (T[] a) {
qSort(a, 0, a.length-1);
}
void qSort (T[] a, int fst, int lst) {
if (fst < lst) {
int pivIndex = partition(a, fst, lst);
qSort(a, fst, pivIndex-1);
qSort(a, pivIndex+1, lst);
}
}

Algorithm for Partitioning
1. Set pivot value to a[fst]
2. Set up to fst and down to lst
3. do
4. Increment up until a[up] > pivot or up = lst
5. Decrement down until a[down] <= pivot or
down = fst
6. if up < down, swap a[up] and a[down]
7. while up is to the left of down
8. swap a[fst] and a[down]
9. return down as pivIndex

Trace of Algorithm for Partitioning

Partitioning Code
int partition
(T[] a, int fst, int lst) {
T pivot = a[fst];
int u = fst;
int d = lst;
do {
while ((u < lst) &&
(pivot.compareTo(a[u]) >= 0))
u++;
while (pivot.compareTo(a[d]) < 0)
d++;
if (u < d) swap(a, u, d);
} while (u < d);

Partitioning Code (2)
swap(a, fst, d);
return d;
}

Revised Partitioning Algorithm
• Quicksort is O(n2) when each split gives 1 empty array
• This happens when the array is already sorted
• Solution approach: pick better pivot values
• Use three “marker” elements: first, middle, last
• Let pivot be one whose value is between the others

Testing Sortiing Algorithms
• Need to use a variety of test cases
• Small and large arrays
• Arrays in random order
• Arrays that are already sorted (and reverse order)
• Arrays with duplicate values
• Compare performance on each type of array

The Dutch National Flag Problem
• Variety of partitioning algorithms have been published
• One that partitions an array into three segments was
introduced by Edsger W. Dijkstra
• Problem: partition a disordered three-color flag into
three contiguous segments
• Segments represent < = > the pivot value

The Dutch National Flag Problem

Chapter Summary
• Three quadratic sorting algorithms:
• Selection sort, bubble sort, insertion sort
• Shell sort: good performance for up to 5000 elements
• Quicksort: average-case O(n log n)
• If the pivot is picked poorly, get worst case: O(n2)
• Merge sort and heapsort: guaranteed O(n log n)
• Merge sort: space overhead is O(n)
• Java API has good implementations

Chapter Summary (2)

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