CS 211: Sorting with Merges
CS 211: Sorting with Merges
Chris Kauffman
Week 14-2
Front Matter
Deliverables
Topics
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P7 Now up today, due next
week Mon
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P7 Overview
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Review Sorting
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Lab 14: Review and
Evaluations
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Merge Sort
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Attend Lab 14: +1 Day Late
Token (!!)
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Reading: BJP: Ch 13.4 on
Merge Sort
Schedule
4/27
5/4
Mon
Tue
Wed
Thu
Mon
W14
W15
5/4
5/5
5/6
5/7
5/11
12-1:15pm
3-4:15pm
2-4:30pm
1:30-4:15pm
10:30-1:15pm
Sorting, Comparators
Review
Last day for Sec 2 (Review)
Makeup day for Sec 4 (Review)
Special review office hours
Sec 4 (T/R) Final Exam
Sec 2 (M/W) Final Exam
P7 Quick Overview
key
5341
5346
5318
4100
4451
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Generic Maps: key/value association
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value
Chris Kauffman
Mark Snyder
Richard Carver
Main Office
Sean Luke
Any kind of key K, any kind of value V
put(key,val) association into a map
get(key) value associated with given key
contains(key) check if an association exists
AbstractListMM implements MiniMap<K,V>
+-- SimpleListMM : indexOf(key) uses linear search
+-- FastGetListMM : indexOf(key) uses binary search
P7 Tips on Comparable and Comparator
In FastGetListMM, must deal with a Map that has either
Comparable keys or a Comparator
Exercise
Write a function sortEm()
public static <T> void sortEm(ArrayList<T> a,
Comparator<T> optional)
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Wrapper around Collections.sort(a, cmp) and
Collections.sort(a)
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If optional is not null, use it to sort the a
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Otherwise check if a can be caste to
ArrayList<Comparable> and use the other version of
Collections.sort(..)
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If optional is null and the elements of a are not
Comparable, throw a RuntimeException
This is why you must get 60% on the final exam
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P5: https:
//www.freelancer.com/jobs/Java/java-project-7467939/
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P5: https:
//www.freelancer.com/jobs/Java/java-project-7438281/
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P7: https://www.freelancer.com/jobs/Java/java-proj/
Quick Review
Comparing
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What’s one way a java class can be made to work with
Arrays.sort(..) and Collections.sort(..)?
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What’s a different way?
Searching
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Describe one way to search for data in an array; include any
assumptions necessary for this process to work
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Describe a fundamentally different way
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Compare these two approaches according to their worst case
runtime complexity
Quick Review
Sorting
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Describe how one sorting algorithm works
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Give give the worst-case runtime complexity of that sorting
algorithm
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What is the worst-case runtime complexity of the best sorting
algorithms?
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Given an example of one sorting algorithm that achieves this
complexity
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What sorting algorithm does Collections.sort(..) use
anyway?
Runtime of Selection sort
public static void selectionSort(int[] a) {
for (int i = 0; i < a.length - 1; i++) {
int smallest = i;
for (int j = i + 1; j < a.length; j++) {
if (a[j] < a[smallest]) {
smallest = j;
}
}
swap(a, i, smallest);
}
}
public static void swap(int[] a, int i, int j) {
int temp = a[i]; a[i] = a[j]; a[j] = temp;
}
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Each inner loop iteration does a constant amount of work
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Fetch array elements, compare numbers, set variables
Good approximation of runtime: total inner loop iterations
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For array size 10? size 50? size N?
Asymptotic Speeds To Remember
Input Data
Array of size N
Search
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Linear search: O(N)
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Binary search: O(log N)
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Hash tables: O(1)
Sort
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Selection sort: O(N 2 )
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Insertion sort: O(N 2 )
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Merge sort: O(N log N)
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Quick sort: O(N log N)
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Radix sort: O(N) via cheating
Merge Sort
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Fast sorting algorithm: O(N log N)
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Exploits recursion
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Principles are simple but implementation is non-trivial
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Variants have different properties: out-of place vs in-place
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Good culmination of CS 211 last two weeks of material
Exercise: Merge
public static void merge(int[] result, int[] a, int[] b)
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a and b are sorted arrays, may not be same size
Copy elements from a and b into result so that result is
sorted
Assume: result.length = =a.length + b.length
Target Runtime Complexity: O(N) where N is
result.length
Example
int [] a = {1, 6, 7, 9}
int [] b = {0, 2, 3, 4, 8}
int [] result = new int[a.length+b.length];
merge(result, a, b);
print(result)
// [0, 1, 2, 3, 4, 6, 7, 8, 9]
Merge Answers
Textbook
CK preferred
public static
public static
void merge(int[] result,
void merge(int[] result,
int[] a, int[] b) {
int[] a, int[] b) {
int i1 = 0;
int ai=0, bi=0;
int i2 = 0;
for(int ri=0; ri<result.length; ri++){
for(int i = 0; i < result.length; i++){
if(ai >= a.length){
if(i2 >= b.length ||
result[ri] = b[bi];
(i1 < a.length && a[i1] <= b[i2])){
bi++;
result[i] = a[i1];
}
i1++;
else if(bi >= b.length){
}
result[ri] = a[ai];
else {
ai++;
result[i] = b[i2];
}
i2++;
else if(a[ai]<=b[bi]){
}
result[ri] = a[ai];
}
ai++;
}
}
else{
result[ri] = b[bi];
bi++;
}
}
Recursive Application
public static void mergeSort(int[] a) {
if (a.length <= 1) {
return;
}
int[] left = Arrays.copyOfRange(a, 0, a.length/2);
int[] right = Arrays.copyOfRange(a, (a.length/2), a.length);
mergeSort(left);
mergeSort(right);
merge(a, left, right);
}
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An array if 1 element is sorted
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If bigger, chop array into two halves
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Recursively sort left and right halves
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Merge the sorted results into the original array
Demonstrate
public static void mergeSort(int[] a) {
if (a.length <= 1) {
return;
}
int[] left = Arrays.copyOfRange(a, 0, a.length/2);
int[] right = Arrays.copyOfRange(a, (a.length/2), a.length);
mergeSort(left);
mergeSort(right);
merge(a, left, right);
}
Show Execution for
int [] a = {14, 32, 67, 76, 23, 41, 58, 85};
mergeSort(list);
mergeSortDebug() in CKMergeSort.java is useful to see this
Computational Complexity
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What is the complexity of mergeSort()
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Could you prove it?
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What’s one huge disadvantage of this version of merge sort?
In-Place Merge Sort
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Making copies of arrays takes time and memory
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Current version is an out-of-place merge sort
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For and array of size 1,000,000, need left and right arrays
which total another 1,000,000 units of memory - BAD!
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Prefer an in-place version to save memory
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Cost: Implementation complexity
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Examine: In-place merge sort ported from C++ STL
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