1. CSC 380: Design and Analysis of Algorithms
Dr. Curry Guinn

2. Quick Info Dr. Curry Guinn CIS 2015 guinnc@uncw.edu
Office Hours: MWF: 10:00am-11:00m and by appointment

3. Today
Divide-and-Conquer
Mergesort
Quicksort

4. Divide-and-Conquer The most-well known algorithm design strategy:
Divide instance of problem into two or more smaller instances
Solve smaller instances recursively
Obtain solution to original (larger) instance by combining these solutions
A. Levitin “Introduction to the Design & Analysis of Algorithms,” 3rd ed., Ch. 5 ©2012 Pearson Education, Inc. Upper Saddle River, NJ. All Rights Reserved.

5. Divide-and-Conquer Technique (cont.)
a problem of size n
subproblem 1
of size n/2
subproblem 2
of size n/2
a solution to
subproblem 1
a solution to
subproblem 2
a solution to
the original problem
A. Levitin “Introduction to the Design & Analysis of Algorithms,” 3rd ed., Ch. 5 ©2012 Pearson Education, Inc. Upper Saddle River, NJ. All Rights Reserved.

6. Divide-and-Conquer Examples
Sorting: mergesort and quicksort
Binary tree traversals
Multiplication of large integers
Matrix multiplication: Strassen’s algorithm
Closest-pair and convex-hull algorithms
A. Levitin “Introduction to the Design & Analysis of Algorithms,” 3rd ed., Ch. 5 ©2012 Pearson Education, Inc. Upper Saddle River, NJ. All Rights Reserved.

7. General Divide-and-Conquer Recurrence
T(n) = aT(n/b) + f (n) where f(n)  (nd), d  0
Master Theorem: If a < bd, T(n)  (nd)
If a = bd, T(n)  (nd log n)
If a > bd, T(n)  (nlog b a )
Note: The same results hold with O instead of .
Examples: T(n) = 4T(n/2) + n  T(n)  ?
T(n) = 4T(n/2) + n2  T(n)  ?
T(n) = 4T(n/2) + n3  T(n)  ?
A. Levitin “Introduction to the Design & Analysis of Algorithms,” 3rd ed., Ch. 5 ©2012 Pearson Education, Inc. Upper Saddle River, NJ. All Rights Reserved.

8. Mergesort
Split array A[0..n-1] in two about equal halves and make copies of each half in arrays B and C
Sort arrays B and C recursively
Merge sorted arrays B and C into array A as follows:
Repeat the following until no elements remain in one of the arrays:
compare the first elements in the remaining unprocessed portions of the arrays
copy the smaller of the two into A, while incrementing the index indicating the unprocessed portion of that array
Once all elements in one of the arrays are processed, copy the remaining unprocessed elements from the other array into A.
A. Levitin “Introduction to the Design & Analysis of Algorithms,” 3rd ed., Ch. 5 ©2012 Pearson Education, Inc. Upper Saddle River, NJ. All Rights Reserved.

9. Pseudocode of Mergesort
A. Levitin “Introduction to the Design & Analysis of Algorithms,” 3rd ed., Ch. 5 ©2012 Pearson Education, Inc. Upper Saddle River, NJ. All Rights Reserved.

10. Pseudocode of Merge
A. Levitin “Introduction to the Design & Analysis of Algorithms,” 3rd ed., Ch. 5 ©2012 Pearson Education, Inc. Upper Saddle River, NJ. All Rights Reserved.

11. Mergesort Example
Go over this example in detail, then do another example of merging, something like:
( )
(3 4 6)
A. Levitin “Introduction to the Design & Analysis of Algorithms,” 3rd ed., Ch. 5 ©2012 Pearson Education, Inc. Upper Saddle River, NJ. All Rights Reserved.

12. How to merge? Input: two sorted array A and B
Output: an output sorted array C
Three counters: Actr, Bctr, and Cctr
initially set to the beginning of their respective arrays
(1)   The smaller of A[Actr] and B[Bctr] is copied to the next entry in C, and the appropriate counters are advanced
(2)   When either input list is exhausted, the remainder of the other list is copied to C

13. Example: Merge

14. Example: Merge... Running time analysis: Space requirement:
Clearly, merge takes O(m1 + m2) where m1 and m2 are
the sizes of the two sublists.
Space requirement:
merging two sorted lists requires linear extra memory
additional work to copy to the temporary array and back

15. Analysis of Mergesort All cases have same efficiency: Θ(n log n)
Number of comparisons in the worst case is close to theoretical minimum for comparison-based sorting:
log2 n! ≈ n log2 n n
Space requirement: Θ(n) (not in-place)
Can be implemented without recursion (bottom-up)
even if not analyzing in detail, show the recurrence for mergesort in worst case:
T(n) = 2 T(n/2) + (n-1)
worst case comparisons for merge
A. Levitin “Introduction to the Design & Analysis of Algorithms,” 3rd ed., Ch. 5 ©2012 Pearson Education, Inc. Upper Saddle River, NJ. All Rights Reserved.

16. Quicksort
Select a pivot (partitioning element) – here, the first element
Rearrange the list so that all the elements in the first s positions are smaller than or equal to the pivot and all the elements in the remaining n-s positions are larger than or equal to the pivot (see next slide for an algorithm)
Exchange the pivot with the last element in the first (i.e., ) subarray — the pivot is now in its final position
Sort the two subarrays recursively p
A[i]p
A[i]p
A. Levitin “Introduction to the Design & Analysis of Algorithms,” 3rd ed., Ch. 5 ©2012 Pearson Education, Inc. Upper Saddle River, NJ. All Rights Reserved.

17. Hoare’s Partitioning Algorithm
A. Levitin “Introduction to the Design & Analysis of Algorithms,” 3rd ed., Ch. 5 ©2012 Pearson Education, Inc. Upper Saddle River, NJ. All Rights Reserved.

18. Quicksort Example
A. Levitin “Introduction to the Design & Analysis of Algorithms,” 3rd ed., Ch. 5 ©2012 Pearson Education, Inc. Upper Saddle River, NJ. All Rights Reserved.

19. Picking the Pivot Use the first element as pivot
If the input is random, ok
If the input is presorted (or in reverse order)
All the elements go into S2 (or S1)
This happens consistently throughout the recursive calls
Results in O(n2) behavior
Choose the pivot randomly
Generally safe
Random number generation can be expensive

20. Picking the Pivot Use the median of the array
Partitioning always cuts the array into roughly half
An optimal quicksort (O(N log N))
However, hard to find the exact median
e.g., sort an array to pick the value in the middle

21. Pivot: median of three We can use median of three
Take the median of three elements
The element at first
The element at last
The element at the middle index (first + last)//2

22. Analysis of Quicksort Best case: split in the middle — Θ(n log n)
Worst case: sorted array! — Θ(n2)
Average case: random arrays — Θ(n log n)
Improvements:
better pivot selection: median of three partitioning
switch to insertion sort on small subfiles
elimination of recursion
These combine to 20-25% improvement
Considered the method of choice for internal sorting of large files (n ≥ 10000)
A. Levitin “Introduction to the Design & Analysis of Algorithms,” 3rd ed., Ch. 5 ©2012 Pearson Education, Inc. Upper Saddle River, NJ. All Rights Reserved.

23. For Next Class, Monday Do your homework in your other classes!
I’ll assign the next Homework on Monday
(But part of it will involve implementing the book’s algorithms for mergesort and quicksort if you want to get a jump on it.)