1. Nattee Niparnan

2. Recall  Complexity Analysis  Comparison of Two Algos  Big O  Simplification  From source code  Recursive

3. Today Topic  Divide and Conquer

4. Algorithm Design Technique Divide and Conquer

6. Induction

7. Induction Example

8. The Inductive Step  The inductive step  Assume that it is true for (n-1)  Check the case (n)  1 + 2 + 3 +… + n = (1 + 2 + 3 + … + (n-1) ) + n  = (n-1)(n) / 2 + n  = n((n-1)/2 + 1)  = n(n/2-1/2 + 1)  = n(n/2 + 1/2)  = n(n+1)/2

9. The Inductive Step  The inductive step  Assume that it is true for (n-1)  Check the case (n)  1 + 2 + 3 +… + n = (1 + 2 + 3 + … + (n-1) ) + n  = (n-1)(n) / 2 + n  = n((n-1)/2 + 1)  = n(n/2-1/2 + 1)  = n(n/2 + 1/2)  = n(n+1)/2 By using (n-1)

10. The Induction  By using the result of the smaller case  We can proof the larger case

11. Key of Divide and Conquer

12. Steps in D&C

13. Code Example ResultType DandC(Problem p) { if (p is trivial) { solve p directly return the result } else { divide p into p 1,p 2,...,p n for (i = 1 to n) r i = DandC(p i ) combine r 1,r 2,...,r n into r return r }

14. Code Example ResultType DandC(Problem p) { if (p is trivial) { solve p directly t s return the result } else { divide p into p 1,p 2,...,p n t d for (i = 1 to n) t r r i = DandC(p i ) combine r 1,r 2,...,r n into r t c return r }

15. Examples  Let’s see some examples

16. Merge Sort

17. The Sorting Problem  Given a sequence of numbers  A = [a 1,a 2,a 3,…,a n ]  Sort the sequence from min to max

18. The Question  What if we know the solution of the smaller problem?  What is the smaller problem?  Try the same problem  sorting  What if we know the result of the sorting of some elements?

19. The Question  How to divide?  Let’s try sorting of the smaller array  Divide exactly at the half of the array  How to conquer?  Merge the result directly

20. Idea  Simplest dividing  Divide array into two array of half size  Laborious conquer  Merge the result

21. Divide

24. Merge

26. Analysis  T(n) = 2T(n/2) + O(n)  Master method  T(n) = O(n lg n)

27. Quick Sort

28. The Sorting Problem (again)  Given a sequence of numbers  A = [a 1,a 2,a 3,…,a n ]  Sort the sequence from min to max

29. Problem of Merge Sort  Need the use of external memory for merging  Are there any other way such that conquering is not that hard?

30. The Question  How to divide?  Try doing the same thing as the merge sort  Add that every element in the first half is less than the second half  Can we do that?  How to conquer?  The sorted result should be easier to merge?

31. Idea  Laborious dividing  Divide array into two arrays  Add the requirement of value of the array  Simplest conquer  Simply connect the result

32. Is dividing scheme possible?  Can we manage to have two subproblem of equal size  That satisfy our need?  Any trade off?

33. The Median

34. Simplified Division

35. partitioning First k element Other n- k element

36. Solve by Recursion sorting

37. Do nothing!

38. Analysis  T(n) = T(k) + T(n – k) + Θ (n)  There can be several cases  Up to which K that we chosen

39. Analysis : Worst Case  K always is 1 st What should be our T(N) ? What is the time complexity

40. Analysis : Worst Case  K always is 1 st T(n) = T(1) + T(n – 1) + Θ (n) = T(n – 1) + Θ (n) = Σ Θ (i) = Θ (n 2 )

41. Analysis : Best Case  K always is the median What should be our T(N) ? What is the time complexity

42. Analysis : Best Case  K always is the median T(n) = 2T(n/2) + Θ (n) = Θ (n log n) The same as the merge sort (without the need of external memory)

43. Fixing the worst case  When will the worst case happen?  When we always selects the smallest element as a pivot  Depends on pivot selection strategy  If we select the first element as a pivot  What if the data is sorted?

44. Fixing the worst case  Select wrong pivot leads to worst case.  There will exist some input such that for any strategy of “deterministic” pivot selection leads to worst case.

45. Fixing the worst case  Use “non-deterministic” pivot selection  i.e., randomized selection  Pick a random element as a pivot  It is unlikely that every selection leads to worst case  We can hope that, on average,  it is O(n log n)

46. Exponential

47. The Problem  Computing x n mod k  Given x, n and k

48. Naïve method res = x mod k; for (i = 2 to n) do { res = res * x; res = res mod k; } Θ (n)

49. The Question  What if we knows the solution to the smaller problem?  Smaller problem  smaller N  x n ?? from x (n-e) ?  Let’s try x (n/2)

50. The Question  How to divide?  x n = x n/2 * x n/2 (n is even)  x n = x n/2 * x n/2 * x (n is odd)  How to conquer?  Compute x n/2 mod k  Square and times with x, if needed,  mod k afterward

51. Analysis  T(n) = T(n/2) + Θ (1)  = O(log n)

52. Example 2 92 mod 10  2 92 = 2 46 × 2 46  2 46 = 2 23 × 2 23  2 23 = 2 11 × 2 11 × 2  2 11 = 2 5 × 2 5 × 2  2 5 = 2 2 × 2 2 × 2  2 2 = 2 1 × 2 1 = 4×4 mod 10 = 6 = 8×8 mod 10 = 4 = 8×8×2 mod 10 = 8 = 2×2×2 mod 10 = 8 = 4×4 × 2 mod 10 = 2 = 2×2 mod 10 = 4

53. Maximum Sum of Subsequence

54. The MSS Problem  Given a sequence of numbers  A = [a 1,a 2,a 3,…,a n ]  There are n members  Find a subsequence s = [a i,a i+1,a i+2,…,a k ]  Such that the sum of the element in s is maximum

55. Example 4-35-226-2 Sum = 11

56. Naïve approach  Try all possible sequences  How many sequence?  How much time does it use to compute the sum?

57. Naïve approach

58. The DC Approach  What if we know the solution of the smaller problem  What if we know MSS of the first half and the second half?

59. The Question  How to divide?  By half of the member  How to conquer?  Does the result of the subproblems can be used to compute the solution?  Let’s see

60. Combining the result from sub MSS 4-35-2 26-2

61. Combining the result from sub MSS 4-35-226-2

62. Combining the result from sub MSS 4-35-226-2 Sum = 6Sum = 8 Shall this be our answer?

63. Combining the result from sub MSS 4-35-2 26-2 Do we consider all possibilities?

64. Combining the result from sub MSS 4-35-2 26-2 Considered all pairs in each half But not the pair including member from both half There are (n/2) 2 additiona l pairs

65. Compute max of cross over  Consider this  The max sequence from the green part is always the same, regardless of the pink part  The sequence always start at the left border 4-35-2 26-2

66. Compute max of cross over  Similarly  The max sequence from the pink part is always the same, regardless of the green part  The sequence always start at the right border 4-35-2 26-2

67. Compute max of cross over  Just find the marginal max from each part 4-35-226-2

68. Compute max of cross over  Just find the marginal max from each part 4-35-226-2

69. Compute max of cross over  Just find the marginal max from each part 4-35-226-2

70. Compute max of cross over  Just find the marginal max from each part 4-35-226-2 Less than previous

71. Compute max of cross over  Just find the marginal max from each part 4-35-226-2

72. Compute max of cross over  Just find the marginal max from each part 4-35-226-2

73. Compute max of cross over  Just find the marginal max from each part 4-35-226-2

74. Compute max of cross over  Just find the marginal max from each part 4-35-226-2 This is max

75. Compute max of cross over  Just find the marginal max from each part 4-35-226-2 Total = 11 takes Θ (n/2)

76. Combine  Max from the three problem  The left half  The right half  The cross over half

77. Analysis  T(n) = 2 T(n/2) + Θ (n)  = Θ (n log n)  a = 2, b = 2, c = log 2 2=1, f(n) = O(n)  n c =n 2 = Θ (n)  f(n) = Θ (n c )this is case 1 of the master’s method  Hence, T(n) = Θ (n log n) Left and right parts Cross over part

78. Closest Pair

79. The Problem  Given  N points in 2D  (x 1,y 1 ), (x 2,y 2 ), …,(x n,y n )  Produce  A pair of points from the given set  (x a,y a ), (x b,y b ) 1 <= a,b <= n  Such that the distance between the points is minimal

80. Input Example

81. Output Example

82. The Naïve Approach

83. DC approach  What if we know the solution of the smaller problem  What if we know Closest Pair of half of the points?  Which half?

84. Divide by X axis Find closest pair of the left side Find closest pair of the right side

85. Conquer  Like the MSS problem  Solutions of the subproblems do not cover every possible pair of points  Missing the pairs that “span” over the boundary

86. Divide by X axis Find closest pair of the left side Find closest pair of the right side

87. Conquer

88. Find Closest Spanning Pair Should we consider this one? Why?

89. Find Closest Spanning Pair

90. Possible Spanning Pair Consider pairs only in this strip One point from the left side Another point from the right side Any point outside the strip, if paired, its distance will be more than b

91. Point in Strips

92. Point in Strips is O(N)

93. The Solution

94. Spanning Pair to be considered

95. Question is still remains  How many pair to be checked? A point to check

96. Implementation Detail

97. Naïve approach

98. Better Approach

99. Analysis  T(n) = 2 T(n/2) + 4O(n) + O(n)  = Θ (n log n)  a = 2, b = 2, c = log 2 2=1, f(n) = O(n)  n c =n 2 = Θ (n)  f(n) = Θ (n c )this is case 1 of the master’s method  Hence, T(n) = Θ (n log n) Left and right parts Point in strip Divide the list

100. Matrix Multiplication

101. The Problem  Given two square matrix  A n x n and B n x n  A  R n x n and B  R n x n  Produce  C = AB

102. Multiplying Matrix c i,j = Σ(a i,k *b k,j )

103. Naïve Method for (i = 1; i <= n;i++) { for (j = 1; i <= n;j++) { sum = 0; for (k = 1;k <= n;k++) { sum += a[i][k] * b[k][j]; } c[i][j] = sum; } O(N 3 )

104. Simple Divide and Conquer  Divide Matrix into Block  Multiply the block

105. Simple Divide and Conquer  What is the complexity?  T(n) = 8T(n/2) + O(n 2 )  Master method gives O(n 3 )  Still the same

106. Strassen Algorithm  We define  Note that each M can be computed by one single multiplication of n/2 * n/2 matrices  The number of addition is 10(n/2) 2

107. Strassen Algorithm  Compute the result  8 more additions of n/2 * n/2 matrices  Hence, total addition is 18 (n/2) 2

108. Analysis  T(n) = 7T(n/2) + O(n 2 )  Using Master’s method  a = 7, b = 2, f(n) = O(n 2 )  c = log 2 7 ≈ 2.807  Hence, f(n) = O(n 2.807 )  this is case 1 of the master method  So, T(n) = O(n 2.807 )