1. Divide-and-Conquer The most-well known algorithm design strategy:
Divide: divide instance of problem into two or more smaller instances, ideally of about equal size
Conquer: Solve smaller instances recursively
Combine: 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.

2. 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.

3. 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
Binary search: decrease-by-half (or degenerate divide&conq.)
A. Levitin “Introduction to the Design & Analysis of Algorithms,” 3rd ed., Ch. 5 ©2012 Pearson Education, Inc. Upper Saddle River, NJ. All Rights Reserved.

4. 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 )
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.

5. Mergesort Divide into two equal parts
Sort each part using merge-sort (recursion!!!)
Merge two sorted subsequences
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. 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.

7. 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.

8. Merge 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. 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.

10. 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. 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.

12. Analysis of Mergesort Recurrence relation:
C(n) = 2C(n/2) + Cmerge(n) for n > 1, C(1) = 0
All cases have same efficiency: Θ(n log 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.

13. Quick Sort Quicksort(A[l…r]) if l <r s  Partition(A[l…r])
Divide: divide into two parts such that
elements of left part < elements of right part
Conquer: recursively solve for each part separately
Combine: trivial - do not do anything
Quicksort(A[l…r])
if l <r
s  Partition(A[l…r])
Quicksort(A[l…s-1])
Quicksort(A[s+1…r])
//divide
//conquer left
//conquer right
A. Levitin “Introduction to the Design & Analysis of Algorithms,” 3rd ed., Ch. 5 ©2012 Pearson Education, Inc. Upper Saddle River, NJ. All Rights Reserved.

14. 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.

15. 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.

16. Quicksort Example l,i r j 5 3 1 9 8 2 4 7 i
A. Levitin “Introduction to the Design & Analysis of Algorithms,” 3rd ed., Ch. 4 ©2012 Pearson Education, Inc. Upper Saddle River, NJ. All Rights Reserved.

17. Analysis of Quicksort Best case: split in the middle
Cbest (n) = 2Cbest (n/2) + n — Θ(n log n)
Worst case: sorted array! — Θ(n2)
Average case: random arrays — Θ(n log n)
Improvements:
better pivot selection: random or median-of-three
switch to insertion sort on small subarrays
elimination of recursion
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.

18. Binary Tree Algorithms
Binary tree is a divide-and-conquer ready structure!
Ex. 1: Classic traversals (preorder, inorder, postorder)
Algorithm Inorder(T)
if T   a a
Inorder(Tleft) b c b c
print(root of T) d e   d e
Inorder(Tright)    
Efficiency: Θ(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.

19. Binary Tree Algorithms (cont.)
Ex. 2: Computing the height of a binary tree
h(T) = max{h(TL), h(TR)} + 1 if T   and h() = -1
Efficiency: Θ(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.