1. Nattee Niparnan Dept. of Computer Engineering, Chulalongkorn University

2. Why Analysis? We need to know the “behavior” of the algorithm Behavior == How well does it perform?

3. Can we? Sure! If we have the algorithm We can implement it We can test it on any input But… Is that what we really want? If you wish to know whether falling from floor 20 of Eng. 4 building would kill you Will you try?

4. Prediction We wish to know the “Behavior” of the algorithm Without actually trying it Back to the suicidal example Can you guess whether you survive jumping from 20 th floor of Eng. 4 building? What about 15 th floor? What about 10 th floor? What about 5 th floor? What about 2 nd floor? Why?

5. Modeling If floor number > 3 then Die Else Survive (maybe?)

6. Modeling What about jumping from Central World’s 20 th floor? What about jumping from Empire State’s 20 th floor? What about jumping from UCL’s 20 th floor? Why?

7. Generalization Knowledge from some particular instances might be applicable to another instance

8. Analysis We need something that can tell us the behavior of the algorithm Generalization Modeling

9. Measurement What we really care? RESOURCE!!! Time (CPU power) Space (amount of RAM)

10. Model Usage of Resource how does an algo use resource?

11. Model Resource Function Time Function of an algorithm A Time Function of an algorithm A Space Function of an algorithm A Space Function of an algorithm A Input ? Time used Space used Size of input

12. Example Inserting a value into a sorted array Input: a sorted array A[1..N] A number X Output A sorted array A[1..N+1] which includes X

13. Algorithm Element Insertion Assume that X = 20 What if A = [1,2,3]? How much time? What if A = [101,102,103]? idx = N; while (idx >= 1 && A[idx] > X) { A[idx + 1] = A[idx]; idx--; } A[idx] = X;

14. Using the Model Time Size of Input best worst average

15. Resource Function Measure resource by “size of input” Why?

16. Conclusion Measurement for algorithm My modeling and generalization For prediction of behavior Measure as a function on the size of input With some simplification Best, avg, worst case

17. Nattee Niparnan

18. What is “better” in our sense? Takes less resource Consider this which one is better? f(x) g(x)

19. Ruler f(x) g(x)

20. What is “better” in our sense? which one is better? Which ruler to use? Performance is now a function, not a single value Can we use single value? Use the ruler where it’s really matter i.e., when N is large What is large N? Infinity? Implication?

21. Comparing by infinite N There is some problem Usually,

22. Separation between Abstraction and Implementation Rate of Growth by changing the size of input, how does the TIME and SPACE requirement change Describe the TIME, SPACE used by function of N, the size of input F(n) = n 3 +2n 2 + 4n + 10

23. Rate of Growth Compare by how f(x) grows when n increase, w.r.t. g(x)

24. Compare by RoG 0 f(x) grows “slowzer” than g(x) ∞ f(x) grows “faster” than g(x) else f(x) grows “similar” to g(x)

25. Growth Rate Comparison 0.5 n 1 log n log 6 n n 0.5 n 3 2 n n! Sometime it is simple Some time it is not

26. l’Hôpital’s Rule Limit of ratio of two functions equal to limit of ratio of their derivative. Under specific condition

27. l’Hôpital’s Rule If then

28. Example of Growth Rate Comparison © การวิเคราะห์และออกแบบอัลกอริทึม, สมชาย ประสิทธิ์จูตระกูล, 2544

29. The problem of this approach What if f(x) cannot be differentiate? Too lazy to diff

30. Compare by Classing Coarse grain comparison Another simplification Work (mostly) well in practice Classing

31. Simplification by classification Grading Analogy ScoreGrade >= 80A 70 <= x < 80B 60 <= x < 70C 50 <= x < 60D < 50F

32. Compare by Classification algo

33. Compare by Classification algo Group A Group C Group B Group F Group D grouping

34. Compare by Classification algo Group A Group C Group B Group F Group D Select a representative

35. Compare by Classification Group by some property Select representative Use representative for comparison If we have the comparison of the representative The rest is to do the classification

36. Complexity Class We define a set of complexity class using rate of growth Here comes the so-called Asymptotic Notation , O, , o,  Classify by asymptotic bound

37. Asymptote Something that bound curve Asymptote Curve

38. Asymptote Remember hyperbola?

39. O-notation O(g(n)) = { f(n) :  positive constants c and n 0, such that  n  n 0, we have 0  f(n)  cg(n) } O(g(n)) = { f(n) :  positive constants c and n 0, such that  n  n 0, we have 0  f(n)  cg(n) } For function g(n), we define O(g(n)), big-O of n, as the set: n0n0 cg(x) f(x) f(x)  O(g(x))

40.  -notation  (g(n)) = { f(n) :  positive constants c and n 0, such that  n  n 0, we have 0  cg(n)  f(n) }  (g(n)) = { f(n) :  positive constants c and n 0, such that  n  n 0, we have 0  cg(n)  f(n) } For function g(n), we define  (g(n)), big-Omega of n, as the set: n0n0 cg(x) f(x) f(x)   (g(x))

41.  -notation  (g(n)) = { f(n) :  positive constants c 1, c 2, and n 0, such that  n  n 0, we have 0  c 1 g(n)  f(n)  c 2 g(n) } For function g(n), we define  (g(n)), big-Theta of n, as the set: n0n0 c 1 g(x) f(x) c 2 g(x) f(x)   (g(x))

42. Example F(n) = 300n + 10 is a member of  (30n) why? let c 1 = 9 let c 2 = 11 let n = 1

43. Another Example F(n) = 300n 2 + 10n is a member of  (10n 2 ) why? let c 1 = 29 let c 2 = 31 let n = 11

44. How to Compute? Remove any constant F(n) = n 3 +2n 2 + 4n + 10 is a member of  (n 3 +n 2 + n) Remove any lower degrees F(n) = n 3 +2n 2 + 4n + 10 is a member of  (n 3 )

45. Relations Between , , O I.e.,  (g(n)) = O(g(n))   (g(n)) In practice, asymptotically tight bounds are obtained from asymptotic upper and lower bounds. For any two functions g(n) and f(n), f(n) =  (g(n)) iff f(n) = O(g(n)) and f(n) =  (g(n)). For any two functions g(n) and f(n), f(n) =  (g(n)) iff f(n) = O(g(n)) and f(n) =  (g(n)).

46. Practical Usage We say that the program has a worst case running time of O(g(n)) We say that the program has a best case running time of  (g(n)) We say that the program has a tight-bound running time of  (g(n))

47. Example Insertion sort takes  (n 2 ) in the worst case Insertion sort takes  (n) in the best case

48. o-notation o(g(n)) = {f(n):  c > 0,  n 0 > 0 such that  n  n 0, we have 0  f(n) < cg(n)}. For a given function g(n), the set little-o:

49. w (g(n)) = {f(n):  c > 0,  n 0 > 0 such that  n  n 0, we have 0  cg(n) < f(n)}.  -notation For a given function g(n), the set little-omega:

50. Remark on Notation An asymptotic group is a set Hence f(n) is a member of an asymptotic group E.g., f(n)  O( n ) Not f(n) = O( n ) But we will see this a lot It’s traditions

51. Comparison of Functions f (n)  g(n)  a  b f (n) = O(g(n))  a  b f (n) =  (g(n))  a  b f (n) =  (g(n))  a = b f (n) = o(g(n))  a < b f (n) =  (g(n))  a > b

52. Lost of Trichotomy Trichotomy given two numbers a,b it must be one of the following a b, a=b For our asymptotic notation given f(n) and g(n) it is possible that f(n) != O(g(n)) and f(n) !=  (g(n)) e.g., n, n 1+sin n

53. Properties Transitivity f(n) =  (g(n)) & g(n) =  (h(n))  f(n) =  (h(n)) f(n) = O(g(n)) & g(n) = O(h(n))  f(n) = O(h(n)) f(n) =  (g(n)) & g(n) =  (h(n))  f(n) =  (h(n)) f(n) = o (g(n)) & g(n) = o (h(n))  f(n) = o (h(n)) f(n) = ω(g(n)) & g(n) = ω(h(n))  f(n) = ω(h(n)) Reflexivity f(n) =  (f(n)) f(n) = O(f(n)) f(n) =  (f(n))

54. Properties Symmetry f(n) =  (g(n)) iff g(n) =  (f(n)) Complementarity f(n) = O(g(n)) iff g(n) =  (f(n)) f(n) = o(g(n)) iff g(n) = ω((f(n))

55. Complexity Graphs log(n)

56. Complexity Graphs log(n) n n log(n)

57. Complexity Graphs n 10 n log(n) n3n3 n2n2

58. Complexity Graphs (log scale) n 10 n 20 n 1.1 n 2n2n 3n3n

59. Common Class of Growth Rate constant : Θ( 1 ) logarithmic : Θ( log n ) polylogarithmic : Θ( log c n ), c ≥ 1 sublinear : Θ( n a ), 0 < a < 1 linear : Θ( n ) quadratic : Θ( n 2 ) polynomial : Θ( n c ), c ≥ 1 exponential : Θ( c n ), c > 1

60. Logarithm Base of log is irrelevant log b n = ( log c n ) / ( log c b ) log 10 n = ( log 2 n ) / ( log 2 10 ) = Θ( log n ) any polynomial function of n does not matter log n 30 = 30 log n = Θ( log n )

61. Conclusion Compare which one is better By comparing their ratio when n approach infinity Compare by complexity class Asymptotic notation What is Property