1. Divide-and-Conquer Dr. B C Dhara Department of Information Technology Jadavpur University

2. Divide-and-Conquer Divide-and conquer is a general algorithm design paradigm: Divide-and conquer is a general algorithm design paradigm: Breaking the problem into several sub-problems that are similar to the original problem but smaller in size, Breaking the problem into several sub-problems that are similar to the original problem but smaller in size, (successively and independently), and then Conquer the subproblems by solving them recursively (successively and independently), and then Combine these solutions to subproblems to create a solution to the original problem. Combine these solutions to subproblems to create a solution to the original problem. The base case for the recursion are subproblems of constant size The base case for the recursion are subproblems of constant size Analysis can be done using recurrence equations Analysis can be done using recurrence equations It is a top-down technique for designing algorithms

3. Binary search Binary Search is an extremely well-known instance of divide-and-conquer paradigm. Binary Search is an extremely well-known instance of divide-and-conquer paradigm. Given an ordered (increasing) array of n elements (a[1,…,n]), the basic idea of binary search is that for a given element we "probe" the middle element of the array. Given an ordered (increasing) array of n elements (a[1,…,n]), the basic idea of binary search is that for a given element we "probe" the middle element of the array. We continue in either the lower or upper segment of the array, depending on the outcome of the probe until we reached the required (given) element. We continue in either the lower or upper segment of the array, depending on the outcome of the probe until we reached the required (given) element.

4. Binary search Divide: middle = (low+high)/2 Divide: middle = (low+high)/2 Conquer: Conquer: if(low>high) return 0; if (a[middle]==key) return key; else if(a[middle]>key) search(a, low, middle-1); else search(a,middle+1,high); Combine: none Combine: none

5. Binary search T(n) = T(n/2) +  (1) T(n) = T(n/2) +  (1)

6. Merge sort The merge sort algorithm closely follows the divide-and-conquer paradigm. Intuitively, it operates as follows. Divide: Divide the n-element sequence to be sorted into two subsequences of n/2 elements each. Conquer: Sort the two subsequences recursively using merge sort. Combine: Merge the two sorted subsequences to produce the sorted answer.

7. Merge sort We note that the recursion “bottoms out” when the sequence to be sorted has length 1, in which case there is no work to be done, since every sequence of length 1 is already in sorted order.

8. Merge sort Input: An array A and indices p and r Output: An sorted array A MERGE-SORT(A, p, r) 1. if p < r 2. then q = (p + r) / 2 3. MERGE-SORT(A, p, q) 4. MERGE-SORT(A, q+1, r) 5. MERGE(A, p, q, r)

9. Merge sort MERGE(A, p, q, r) 1. i = p, j = q + 1, n = r - p + 1 2. for k = 1 to n // for 1 3. if [ (A[i] r)] and (i ≤ q) 4. B[k] = A[i] 5. i = i +1 6. else 7. B[k] = A[j] 8. j = j +1 9. for k = 0 to n – 1 10. A[p + k] = B[k]  (n) Complexity of merge sort is T(n)= T(  n/2  ) + T(  n/2  ) +  (n)

10. Quick sort Divide: partition the data set into two subsets respect to the pivot element Divide: partition the data set into two subsets respect to the pivot element Conquer: recursively use the quick sort to solve the subsets Conquer: recursively use the quick sort to solve the subsets Combine: none, as it is inplace sorting. Combine: none, as it is inplace sorting.

11. Quick sort Quicksort(A,p,r) if p  r then return; if p  r then return; q = partition(A,p,r); q = partition(A,p,r); Quicksort(A,p,q-1); Quicksort(A,p,q-1); Quicksort(A,q+1,r); Quicksort(A,q+1,r); Partition(a,first,last) pivot =a[first] up=first; down=last; while (up <down) { while(pivot >=a[up] && up <=last) up++; while( pivot < a[down]) down--; if(up<down) swap(a, up, down); } swap(a, first, down); return down; Complexity of quick sort is T(n) = T(i) + T(n-i-1) + O(n)

12. Counting inversions problem Let L = {x 1, x 2, x 3,…, x n } a collection of distinct integers between 1 and n. Let L = {x 1, x 2, x 3,…, x n } a collection of distinct integers between 1 and n. The number of pairs (i,j), 1 ≤ i x j The number of pairs (i,j), 1 ≤ i x j If counter is (n-1) + (n-2) + … + 1 = n(n-1)/2, then the given data is in ascending order. If counter is (n-1) + (n-2) + … + 1 = n(n-1)/2, then the given data is in ascending order. If counter value is 0, the given set is in descending order. If counter value is 0, the given set is in descending order.

13. Counting inversions problem 3 5 2 1 7 9 8 4 6 3 5 2 1 7 9 8 4 6 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 Number of crossing point is the answer We can apply the merge sort like method to solve the problem

14. Counting inversions problem Divide: Partition the list L into two lists A and B with elements n/2 Divide: Partition the list L into two lists A and B with elements n/2 Conquer: Conquer: Recursively count the number of inversions in A Recursively count the number of inversions in A Recursively count the number of inversions in B Recursively count the number of inversions in B Combine: Count the number of inversions involving one element in A and one element in B (use merge procedure and increase the counter appropriately) Combine: Count the number of inversions involving one element in A and one element in B (use merge procedure and increase the counter appropriately)

15. Counting inversions problem sort-and-count(L) If |L}==1, return 0; Else A  first  n/2  elements A  first  n/2  elements B  remaining  n/2  elements B  remaining  n/2  elements (ca, A)= sort-and-count(A) (ca, A)= sort-and-count(A) (cb, B)= sort-and-count(B) (cb, B)= sort-and-count(B) (cm, L) = merge-and-count(A,B) (cm, L) = merge-and-count(A,B) return ca + cb + cm with sorted list L return ca + cb + cm with sorted list L Running time of the method is T(n) ≤2T(  n/2  ) + O(n)

16. Counting inversions problem merge-and-count (A,B) 1. Maintain a current pointer for each list and initially point to the first element 2. Maintain a variable count initialized to 0 3. While both lists are nonempty 1. Let ai and bj are the current elements 2. Append the smaller of the two to the output list 3. If bj is smaller, increment the count by number of elements in A from current position to last 4. Advance the current pointer of smaller element list 4. Append the rest of the non-empty list to the output 5. Return count and the merged list.

17. Notes In ‘sort-and-count’ process In ‘sort-and-count’ process The lists A and B inputed to the ‘merge-and-count’ function are sorted. The lists A and B inputed to the ‘merge-and-count’ function are sorted. 342586719 342586719342586719342586719342586719342586719 Counter =0

18. Example 342586719 Counter =2 234586719234586719234586719 234586719

19. Example 234586719 234586719234586719234586719 234586719

20. Example 234586719 234586719 234581679 Counter =4 1 Counter =9 12345 123456 Counter =10 1234567 Counter =11 12345678 123456789

21. Closest pair of points A set P of n points in the plane, find the pair of points in P that are closest to each other. A set P of n points in the plane, find the pair of points in P that are closest to each other. The simplest solution of the problem, consider all pair and find the distance of each pair and then find the closets one. The simplest solution of the problem, consider all pair and find the distance of each pair and then find the closets one. Complexity is O(n 2 ) Complexity is O(n 2 ) Another solution, using divide-and-conquer approach with complexity O(n log n). Another solution, using divide-and-conquer approach with complexity O(n log n).

22. Closest pair of points In 1D In 1D Without any loss assume points are on the x-axis Without any loss assume points are on the x-axis Shortest distance is the one between adjacent pair of points. Shortest distance is the one between adjacent pair of points. Sort the given points and compute the distance for n-1 adjacent pairs Sort the given points and compute the distance for n-1 adjacent pairs Complexity: O(n log n) + O(n) = O(n log n) Complexity: O(n log n) + O(n) = O(n log n)

23. Closest pair of points 1D 1D Divide and conquer approach after the sorting: Divide equally in left half and right half Closest pair could be: Closest pair in left half: distance  l Closest pair in right half: distance  r Closest pair that span the left and right halves and are at most min(  l,  r ) apart (only one such pair?) Closest pair that span the left and right halves and are at most min(  l,  r ) apart (only one such pair?)

24. Closest pair of points Extend the concept of 1D for 2D Extend the concept of 1D for 2D Let the given point set P = {p 1, p 2,…,p n }with p i =(x i, y i ) Let the given point set P = {p 1, p 2,…,p n }with p i =(x i, y i ) If |P|≤3, use brute-force method If |P|≤3, use brute-force method X  sort(P) respect to x value (presort) X  sort(P) respect to x value (presort) Y  sort(P) respect to y value (presort) Y  sort(P) respect to y value (presort)

25. Closest pair of points Divide: Divide: partition, respect to a vertical line L, P into P L and P R with  n/2  and  n/2  points partition, respect to a vertical line L, P into P L and P R with  n/2  and  n/2  points X L  X(P L ); Y L  Y(P L ); X L  X(P L ); Y L  Y(P L ); X R  X(P R ); Y R  Y(P R ); X R  X(P R ); Y R  Y(P R ); Conquer: Conquer:  L = Closesrpair(P L, X L, Y L )  L = Closesrpair(P L, X L, Y L )  R = Closesrpair(P R, X R, Y R )  R = Closesrpair(P R, X R, Y R )  = min(  L,  R )  = min(  L,  R )

26. Closest pair of points Combine: Combine: closest pair is in Left set, or  closest pair is in Left set, or  closest pair is in Right set, or  closest pair is in Right set, or  closest pair formed by one point from each of the sets closest pair formed by one point from each of the sets if minimum distance is , then we reach to the solution if minimum distance is , then we reach to the solution else (i.e., less than  ), then one point (p L ) in P L and other point (p R ) is in P R and both must reside within  unit from L. else (i.e., less than  ), then one point (p L ) in P L and other point (p R ) is in P R and both must reside within  unit from L.

27. Closest pair of points Combine (contd…) Combine (contd…) Consider vertical strip (S) of width 2  centered at L // Ignore the points outside the strip Now, Y’  strip(Y), sorted on y value For each point p in S compute the distance with next 5 points (in Y’) compute the distance with next 5 points (in Y’) if distance is less update the minimum distance and track the closest pair. if distance is less update the minimum distance and track the closest pair.

28. Closest pair of points All points in P L are at least  unit apart, there can be at most 4 point in  x  with distance  Similarly, in other half, so total 4+4-2 = 6 points  x 2  Closest pair within  x 2 

29. Closest pair of points Running time Running time Presort (respect to x, y value): O(n log n) Each recursive call required the sorted array in x and y for the sub points. This can be done by O(n). How? size(X L )=n/2 size(X R )=n/2, t=0; r=0; size(X L )=n/2 size(X R )=n/2, t=0; r=0; for i = 1 to n for i = 1 to n if X[i]  P L X L [t]  X[i]; t++ if X[i]  P L X L [t]  X[i]; t++ if X[i]  P R X R [t]  X[i]; r++ if X[i]  P R X R [t]  X[i]; r++

30. Closest pair of points Running time Running time In combine step, for each point within the strip compare with next 5 points O(n) O(n) Complexity is: T(n) + O(n log n), where T(n) + O(n log n), where T(n) = 2T(n/2) + O(n) T(n) = 2T(n/2) + O(n)

31. Integer multiplication Instance: two n-digits number X and Y (decimal number or binary number) Instance: two n-digits number X and Y (decimal number or binary number) Output: XY, a number with at most 2n digits, for both the cases. Output: XY, a number with at most 2n digits, for both the cases. In school method, number of operations is O(n 2 ), n is the number of digits. School method

32. Integer multiplication We consider 2 decimal, non- negative integers We consider 2 decimal, non- negative integers Input: Input: X = x n-1 x n-2 x n-3 … x 2 x 1 x 0 X = x n-1 x n-2 x n-3 … x 2 x 1 x 0 Y = y n-1 y n-2 y n-3 … y 2 y 1 y 0 Y = y n-1 y n-2 y n-3 … y 2 y 1 y 0 Output: Output: Z = z 2n-2 z 2n-3 z 2n-4 … z 2 z 1 z 0 Z = z 2n-2 z 2n-3 z 2n-4 … z 2 z 1 z 0

33. Integer multiplication

34. Let Let U=a*c U=a*c V=b*d V=b*d W=(a+b)*(c+d) = a*c + a*d + b*c + b*d W=(a+b)*(c+d) = a*c + a*d + b*c + b*d Z = U*10 n + (W-U-V)*10 n/2 + V Z = U*10 n + (W-U-V)*10 n/2 + V We need three multiplications and number of digits is n/2 We need three multiplications and number of digits is n/2

35. Integer multiplication integerMultiplication(X, Y) U= integerMultiplication(a, c) V= integerMultiplication(b, d) W= integerMultiplication((a+b),(c+d)) W= integerMultiplication((a+b),(c+d)) return (U.10 n + (W-U-V)*10 n/2 + V ) return (U.10 n + (W-U-V)*10 n/2 + V )

36. Integer multiplication Complexity: Complexity: T(n) = 3T(n/2) + O(n) The same algorithm can be used for two binary numbers multiplication The same algorithm can be used for two binary numbers multiplication We have to replace 10 by 2 We have to replace 10 by 2

37. Matrix multiplication We consider n x n matrices We consider n x n matrices Using brut-force method,  (n 3 ) Using brut-force method,  (n 3 ) a, b, c, d, e, f, g, h, r, s, t and u are n/2 x n/2 matrices r = ae + bf s= ag + bh t = ce + df u = cg + dh Eight n/2 x n/2 multiplications and four additions of size n/2 x n/2 Eight n/2 x n/2 multiplications and four additions of size n/2 x n/2 T(n) = 8T(n/2) +  (n 2 ) T(n) = 8T(n/2) +  (n 2 )

38. Matrix multiplication Compute 14 n/2 x n/2 matrices A1,B1,A2,…,A7,B7 Compute 14 n/2 x n/2 matrices A1,B1,A2,…,A7,B7 Pi = AiBi Pi = AiBi Then compute r, s, t, u by simple matrix addition/subtraction with different combination of Pi Seven n/2 x n/2 multiplications and some additions of size n/2 x n/2 Seven n/2 x n/2 multiplications and some additions of size n/2 x n/2 T(n) = 7T(n/2) +  (n 2 ) T(n) = 7T(n/2) +  (n 2 )

39. Strassen’s method r = ae + bf r = ae + bf s= ag + bh s= ag + bh t = ce + df t = ce + df u = cg + dh u = cg + dh P5=(a+d).(e+h) = ae + ah + de + dh P6 = (b-d).(f+h) = bf + bh –df –dh P7 = (a-c).(e+g) = ae + ag –ce –cg r = P5 + P4 –P2 +P6 = ae + bf u = P5 + P1 – P3 – P7 = cg + dh P1 = ag –ah = a.(g-h) P1 = ag –ah = a.(g-h) P2 = bh+ ah = (a+b).h P2 = bh+ ah = (a+b).h s = P1 + P2 s = P1 + P2 P3 = ce + de = (c+d).e P3 = ce + de = (c+d).e P4 = df -de = d.(f-e) P4 = df -de = d.(f-e) t = P3 + P4 t = P3 + P4

40. Analysis of divide-and-conquer algorithms Let the input problem of size n divides into subproblems of size n 1, n 2, n 3,…, n r. Let the input problem of size n divides into subproblems of size n 1, n 2, n 3,…, n r. The running time of the problem is T(n) The running time of the problem is T(n) Let t d (n) and t c (n) represents the time complexity of the divide step and combine step respectively. Then, Let t d (n) and t c (n) represents the time complexity of the divide step and combine step respectively. Then, T(n) = t d (n) +  T(n i ) + t c (n) when n> certain value

41. Analysis of divide-and-conquer algorithms Usually (not always) the subproblems are of same size, say, n/b and then Usually (not always) the subproblems are of same size, say, n/b and then T(n) = r.T(n/b)+ f(n) when n> certain value and f(n)= t d (n) + t c (n) T(n) = r.T(n/b)+ f(n) when n> certain value and f(n)= t d (n) + t c (n) If not of same size, sometimes we write the expression as (depending on the requirement) If not of same size, sometimes we write the expression as (depending on the requirement) T(n) ≤ r.T(n/b)+ f(n) n>c T(n) ≤ r.T(n/b)+ f(n) n>c

42. Analysis of divide-and-conquer algorithms Binary search: T(n) = T(n/2) +  (1) Binary search: T(n) = T(n/2) +  (1) Merge sort:  (n) Merge sort: T(n)= T(  n/2  ) + T(  n/2  ) +  (n) Quick sort: T(n) = T(i) + T(n-i-1) + O(n) Quick sort: T(n) = T(i) + T(n-i-1) + O(n) Counting inver prob: T(n) ≤2T(n/2) + O(n) Counting inver prob: T(n) ≤2T(n/2) + O(n) Closest pair: T(n) + O(n log n), where T(n) = 2T(n/2) + O(n) Closest pair: T(n) + O(n log n), where T(n) = 2T(n/2) + O(n) Integer multiplication: T(n) = 3T(n/2) + O(n) Integer multiplication: T(n) = 3T(n/2) + O(n) Matrix multiplication: Matrix multiplication: T(n) = 8T(n/2) +  (n 2 ) T(n) = 8T(n/2) +  (n 2 ) T(n) = 7T(n/2) +  (n 2 ) T(n) = 7T(n/2) +  (n 2 )

43. Solution of recurrence relation Three approaches: Three approaches: Substitution method Substitution method Iteration method Iteration method Master method Master method

44. Substitution method We ‘guess’ that a certain function is a solution (or an upper or lower bound on the solution) to the recurrence relation, and we verify that this correct by induction We ‘guess’ that a certain function is a solution (or an upper or lower bound on the solution) to the recurrence relation, and we verify that this correct by induction Consider T(n) = 2T(  n/2  ) + O(n), let T(n)=O(n log n) Consider T(n) = 2T(  n/2  ) + O(n), let T(n)=O(n log n)

45. Substitution method Making a good guess Making a good guess Avoiding pitfalls Avoiding pitfalls T(n) = 2T(n/2) + n, let T(n) = cn for c>0 T(n) = 2T(n/2) + n, let T(n) = cn for c>0 T(n) = 2.c.n/2 + n = cn +n = O(n), a wrong sol. T(n) = 2.c.n/2 + n = cn +n = O(n), a wrong sol. The initial assumption was wrong The initial assumption was wrong

46. Substitution method Changing variable: Changing variable: Consider m = lg n,i.e, n = 2 m Consider m = lg n,i.e, n = 2 m T(2 m ) = 2T(2 m/2 ) + m T(2 m ) = 2T(2 m/2 ) + m S(m) = 2S(m/2) + m S(m) = 2S(m/2) + m S(m)= O(m lg m) S(m)= O(m lg m) T(n) = O(lg n lg lg n) T(n) = O(lg n lg lg n)

47. Iteration method Repeatedly expand the recurrence relation using the same given form for the recurrence terms on the right side, until we reach th base the case, for which we can substitute the value given for the base case. Repeatedly expand the recurrence relation using the same given form for the recurrence terms on the right side, until we reach th base the case, for which we can substitute the value given for the base case. Finally, we have to apply the summation of the values, to find the bound Finally, we have to apply the summation of the values, to find the bound

48. Iteration method T(n) = 3T(  n/4  ) + n T(n) = 3T(  n/4  ) + n

49. Recursion tree A recursion tree is a way to visualize what happens when a recursion is iterated. A recursion tree is a way to visualize what happens when a recursion is iterated. E.g., T(n) = 2T(n/2) + n 2 E.g., T(n) = 2T(n/2) + n 2

50. The master method Let a  1 and b >1, f(n) is monotonically non- negative function of n, and T(n) = aT(n/b) + f(n) is a recurrence relation defined on the non-negative integers. Then T(n) can be bounded asymptotically as follows, Let a  1 and b >1, f(n) is monotonically non- negative function of n, and T(n) = aT(n/b) + f(n) is a recurrence relation defined on the non-negative integers. Then T(n) can be bounded asymptotically as follows,

51. Analysis of divide-and-conquer algorithms Binary search: T(n) = T(n/2) +  (1) [O(log n)] Binary search: T(n) = T(n/2) +  (1) [O(log n)] Merge sort:  (n) [rule 1] Merge sort: T(n)= T(  n/2  ) + T(  n/2  ) +  (n) [rule 1] Quick sort: T(n) = T(i); + T(n-i-1) + O(n) ????? Quick sort: T(n) = T(i); + T(n-i-1) + O(n) ????? Counting inver prob: T(n) ≤2T(n/2) + O(n) [R1: O(n log n] Counting inver prob: T(n) ≤2T(n/2) + O(n) [R1: O(n log n] Closest pair: T(n) + O(n log n), where T(n) = 2T(n/2) + O(n) [R1: O(n log n)] Closest pair: T(n) + O(n log n), where T(n) = 2T(n/2) + O(n) [R1: O(n log n)] Integer multiplication: T(n) = 3T(n/2) + O(n) [R1] Integer multiplication: T(n) = 3T(n/2) + O(n) [R1] Matrix multiplication: Matrix multiplication: T(n) = 8T(n/2) +  (n 2 ) [R1:  (n 2 ) ] T(n) = 8T(n/2) +  (n 2 ) [R1:  (n 2 ) ] T(n) = 7T(n/2) +  (n 2 ) [R1:  (n 2 ) ] T(n) = 7T(n/2) +  (n 2 ) [R1:  (n 2 ) ]

52. Master theorem T(n) =3T(n/4) + n lg n T(n) =3T(n/4) + n lg n a=3 and b=4, for  =0.2, Now test the regularity condition, af(n/b) ≤ cf(n) for some 0 <c <1 3(n/4) lg (n/4) ≤ (3/4) n lg n c=3/4. 3(n/4) lg (n/4) ≤ (3/4) n lg n c=3/4. By base 3, solution is T(n)=  (n lg n)

53. Master theorem T(n) =2T(n/2) + n lg n T(n) =2T(n/2) + n lg n a=2 and b=2, for any ,

54. Approximation by integrals

55. Let f(x) is a monotonically increasing function and then the sum can be expressed as Let f(x) is a monotonically increasing function and then the sum can be expressed as Similarly, if f(x) monotonically decreasing function then Similarly, if f(x) monotonically decreasing function then

59. Consider f(k) = 1/k, for k>0 Consider f(k) = 1/k, for k>0

60. Average case quick sort Assumptions: Assumptions: All elements are distinct All elements are distinct Number of permutation depends on order of the input data set. We assume the data set as {1,2,…,n}. Number of permutation depends on order of the input data set. We assume the data set as {1,2,…,n}. All permutation is equally likely All permutation is equally likely Let input list is any random permutation of {1, 2, …, n} and pivot element is ‘s’ Let input list is any random permutation of {1, 2, …, n} and pivot element is ‘s’

61. Average case quick sort After partition let the situation is as After partition let the situation is as (i 1, i 2, …, i s-1, s, j s+1, …., j n,) (i 1, i 2, …, i s-1, s, j s+1, …., j n,) (i 1, i 2, …, i s-1 ) is a random permutation of {1, 2, …, s-1} (i 1, i 2, …, i s-1 ) is a random permutation of {1, 2, …, s-1} (j s+1, j s+2, …, j n ) is a random permutation of {s+1, s+2, …, n} (j s+1, j s+2, …, j n ) is a random permutation of {s+1, s+2, …, n}

62. Average case quick sort Q a (n) = avg. number of comparison done by ‘Partition( )’ on an input which is a random permutation of {1, 2, …, n}, then Q a (n) = avg. number of comparison done by ‘Partition( )’ on an input which is a random permutation of {1, 2, …, n}, then Q a (0) = Q a (1) = 0 Q a (0) = Q a (1) = 0 Q a (2) = 3 Q a (2) = 3 Any element can be a pivot element, prob (s: s is pivot)=1/n, 1≤ s ≤ n Any element can be a pivot element, prob (s: s is pivot)=1/n, 1≤ s ≤ n

63. Average case quick sort