1. Lecture 7

2. Solution by Substitution Method T(n) = 2 T(n/2) + n Substitute n/2 into the main equation 2T(n/2) = 2(2(T(n/4)) + n/2) = 4T(n/4) + n And T(n) = 4T (n/4) + 2n Again by substituting n/4, we can see 4T(n/4) = 4(2T(n/8)) + (n/4) = 8T(n/8) + n And T(n) = 8T(n/8) + 3n Continuing in this manner, we obtain T(n) = 2 k T(n/2 k ) + k.n Using k = lgn T(n) = nT(1) + nlgn = nlgn + n T(n) = O(nlgn)

3. 3 Overview Divide and Conquer Merge Sort Quick Sort

4. 4 Divide: Pick any element p as the pivot, e.g, the first element Partition the remaining elements into FirstPart, which contains all elements < p SecondPart, which contains all elements ≥ p Recursively sort the FirstPart and SecondPart Combine: no work is necessary since sorting is done in place

5. 5 Quick Sort x < p p p ≤ x Partition FirstPart SecondPart p pivot A: Recursive call x < p p p ≤ x Sorted FirstPart Sorted SecondPart Sorted

6. 6 Quick Sort Quick-Sort(A, left, right) if left ≥ right return else middle ← Partition(A, left, right) Quick-Sort(A, left, middle–1 ) Quick-Sort(A, middle+1, right) end if

7. 7 Partition p p x < pp ≤ x p x < p A: A: A: p

8. 8 Partition ExampleA: 48635172

9. 9 A: 48635172 i=0 j=1

10. 10 Partition ExampleA: j=1 48635172 i=0 8

11. 11 Partition ExampleA: 486351726 i=0 j=2

12. 12 Partition ExampleA: 4 8 635172 i=0 3 83 j=3i=1

13. 13 Partition ExampleA: 43685172i=1 5 j=4

14. 14 Partition ExampleA: 43685172i=1 1 j=5

15. 15 Partition ExampleA: 43685172i=2 16 j=5

16. 16 Partition ExampleA: 438572i=2 16 7 j=6

17. 17 Partition ExampleA: 438572i=2 16 2 2 8i=3j=7

18. 18 Partition ExampleA: 432678i=3 15 j=8

19. 19 Partition ExampleA: 41678i=3 25 4 2 3

20. 20 A: 3 678 1 5 42 x < 4 4 ≤ x pivot in correct position Partition Example

21. 21 Partition(A, left, right) 1.x ← A[left] 2.i ← left 3.for j ← left+1 to right 4.if A[j] < x then 5.i ← i + 1 6.swap(A[i], A[j]) 7.end if 8.end for j 9.swap(A[i], A[left]) 10.return i n = right – left +1 Time: cn for some constant c Space: constant

22. 22 4 8 6 3 5 1 7 2 2 3 1 5 6 7 8 4 Quick-Sort(A, 0, 7) Partition A:

23. 23 2 3 1 5 6 7 8 4 2 13 Quick-Sort(A, 0, 7) Quick-Sort(A, 0, 2) A:, partition

24. 24 2 5 6 7 8 4 1 1 3 Quick-Sort(A, 0, 7) Quick-Sort(A, 0, 0), base case, return

25. 25 2 5 6 7 8 4 1 3 3 Quick-Sort(A, 0, 7) Quick-Sort(A, 1, 1), base case

26. 26 5 6 7 8 4 2 1 3 2 1 3 Quick-Sort(A, 0, 7) Quick-Sort(A, 2, 2), return Quick-Sort(A, 0, 2), return

27. 27 4 2 1 3 5 6 7 86 7 8 5 Quick-Sort(A, 0, 7) Quick-Sort(A, 2, 2), return Quick-Sort(A, 4, 7), partition

28. 28 4 5 6 7 8 7 8 6 6 2 1 3 Quick-Sort(A, 0, 7) Quick-Sort(A, 5, 7), partition

29. 29 4 5 6 7 8 8 7 2 1 3 Quick-Sort(A, 0, 7) Quick-Sort(A, 6, 7), partition

30. 30 4 5 6 7 2 1 3 Quick-Sort(A, 0, 7) Quick-Sort(A, 7, 7) 8, return, base case 8

31. 31 4 5 6 8 7 2 1 3 Quick-Sort(A, 0, 7) Quick-Sort(A, 6, 7), return

32. 32 4 5 2 1 3 Quick-Sort(A, 0, 7) Quick-Sort(A, 5, 7), return 6 8 7

33. 33 4 2 1 3 Quick-Sort(A, 0, 7) Quick-Sort(A, 4, 7), return 5 6 8 7

34. 34 4 2 1 3 Quick-Sort(A, 0, 7), done! 5 6 8 7

35. 35 Quick-Sort: Best Case Even Partition Total time:  (nlogn) cn 2 × cn/2 = cn 4 × c/4 = cn n/3 × 3c = cn log n levels n n/2 n/4 3 3 3

36. 36 cn c(n-1) 3c 2c n n-1 n-2 3 2 c(n-2) Happens only if input is sortd input is reversely sorted Quick-Sort: Worst Case Unbalanced Partition Total time:  (n 2 )

37. 37 Quick-Sort: an Average Case Suppose the split is 1/10 : 9/10 Quick-Sort: an Average Case cn ≤cn n 0.1n 0.9n 0.01n 0.09n Total time:  (nlogn) 0.81n 2 2 log 10 n log 10/9 n ≤cn

38. 38 Quick-Sort Summary Time –Most of the work done in partitioning. –Average case takes  (n log(n)) time. –Worst case takes  (n 2 ) time Space –Sorts in-place, i.e., does not require additional space

39. Recursion Recursion is more than just a programming technique. It has two other uses in computer science and software engineering, namely: as a way of describing, defining, or specifying things. as a way of designing solutions to problems (divide and conquer).

41. In general, we can define the factorial function in the following way:

42. Iterative Definition This is an iterative definition of the factorial function. It is iterative because the definition only contains the algorithm parameters and not the algorithm itself. This will be easier to see after defining the recursive implementation.

43. Recursive Definition We can also define the factorial function in the following way:

44. Iterative vs. Recursive Iterative 1 if n=0 factorial(n) = n x (n-1) x (n-2) x … x 2 x 1 if n>0 Recursive 1if n=0 factorial(n) = n x factorial(n-1)if n>0 Function calls itself Function does NOT call itself

45. Recursion To see how the recursion works, let ’ s break down the factorial function to solve factorial(3)

46. Breakdown Here, we see that we start at the top level, factorial(3), and simplify the problem into 3 x factorial(2). Now, we have a slightly less complicated problem in factorial(2), and we simplify this problem into 2 x factorial(1).

47. Breakdown We continue this process until we are able to reach a problem that has a known solution. In this case, that known solution is factorial(0) = 1. The functions then return in reverse order to complete the solution.

48. Breakdown This known solution is called the base case. Every recursive algorithm must have a base case to simplify to. Otherwise, the algorithm would run forever (or until the computer ran out of memory).

49. Breakdown The other parts of the algorithm, excluding the base case, are known as the general case. For example: 3 x factorial(2)  general case 2 x factorial(1)  general case etc …

50. Iterative Algorithm factorial(n) { i = 1 factN = 1 loop (i <= n) factN = factN * i i = i + 1 end loop return factN } The iterative solution is very straightforward. We simply loop through all the integers between 1 and n and multiply them together.

51. Recursive Algorithm factorial(n) { if (n = 0) return 1 else return n*factorial(n-1) end if } Note how much simpler the code for the recursive version of the algorithm is as compared with the iterative version  we have eliminated the loop and implemented the algorithm with 1 ‘if’ statement.

52. How Recursion Works To truly understand how recursion works we need to first explore how any function call works. When a program calls a subroutine (function) the current function must suspend its processing. The called function then takes over control of the program.

53. How Recursion Works When the function is finished, it needs to return to the function that called it. The calling function then ‘ wakes up ’ and continues processing. One important point in this interaction is that, unless changed through call-by- reference, all local data in the calling module must remain unchanged.

54. How Recursion Works Therefore, when a function is called, some information needs to be saved in order to return the calling module back to its original state (i.e., the state it was in before the call). We need to save information such as the local variables and the spot in the code to return to after the called function is finished.

55. How Recursion Works To do this we use a stack. Before a function is called, all relevant data is stored in a stackframe. This stackframe is then pushed onto the system stack. After the called function is finished, it simply pops the system stack to return to the original state.

56. How Recursion Works By using a stack, we can have functions call other functions which can call other functions, etc. Because the stack is a first-in, last-out data structure, as the stackframes are popped, the data comes out in the correct order.

57. Main disadvantage of programming recursively The main disadvantage of programming recursively is that, while it makes it easier to write simple and elegant programs, it also makes it easier to write inefficient ones. when we use recursion to solve problems we are interested exclusively with correctness, and not at all with efficiency. Consequently, our simple, elegant recursive algorithms may be inherently inefficient.

58. Limitations of Recursion Recursive solutions may involve extensive overhead because they use calls. When a call is made, it takes time to build a stackframe and push it onto the system stack. Conversely, when a return is executed, the stackframe must be popped from the stack and the local variables reset to their previous values – this also takes time.

59. Limitations of Recursion In general, recursive algorithms run slower than their iterative counterparts. Also, every time we make a call, we must use some of the memory resources to make room for the stackframe.

60. Limitations of Recursion Therefore, if the recursion is deep, say, factorial(1000), we may run out of memory. Because of this, it is usually best to develop iterative algorithms when we are working with large numbers.

61. Recursion is based upon calling the same function over and over, whereas iteration simply `jumps back' to the beginning of the loop. A function call is often more expensive than a jump.