1. Introduction to Algorithms Jiafen Liu Sept. 2013

2. Today’s task Develop more asymptotic notations How to solve recurrences

3. Θ-notation Math: Θ(g(n)) = { f(n) : there exist positive constants c 1, c 2, and n 0 such that 0 ≤ c 1 g(n) ≤ f (n) ≤ c 2 g(n) for all n ≥ n 0 } Engineering: –Drop low-order terms; –ignore leading constants.

4. O-notation Another asymptotic symbol used to indicate upper bounds. Math: Ex : 2n 2 = O(n 3 ) (c= 1, n 0 = 2) “=” means “one-way” equality

5. Set definition of O-notation Math: Looks better? EX : 2n 2 ∈ O(n 3 ) O-notation corresponds roughly to “less than or equal to”.

6. Usage of O-notation Macro substitution: A set in a formula represents an anonymous function in the set, if O-notation only occurs on the right hand of formula. Ex: f(n) = n 3 + O(n 2 ) Means f(n) = n 3 + h(n) for some h(n) ∈ O(n 2 ), here we can think of h(n) as error term.

7. Usage of O-notation If O-notation occurs on the left hand of formula, such as n 2 + O(n) = O(n 2 ), which means for any f(n) ∈ O(n): there exists some h(n) ∈ O(n 2 ), makes n 2 + f(n) = h(n) O-notation is an upper-bound notation. –It makes no sense to say f(n) is at least O(n 2 ).

8. Ω-notation How can we express a lower bound? Ex:

9. Θ-notation O-notation is like ≤ Ω-notation is like ≥. And Θ-notation is like = Now we can give another definition of Θ- notation We also call Θ-notation as “tight bound”.

10. A Theorem on Asymptotic Notations Theorem 3.1 – For any two functions f(n) and g(n), we say f(n)=Θ(g(n)) if and only if f(n)=O(g(n)) and f(n)=Ω(g(n)). Ex:, how can we prove that?

11. Two More Strict Notations We have just said that: –O-notation and Ω-notation are like ≤and ≥. –o-notation and ω-notation are like. –Difference with O-notation : This inequality must hold for all c instead of just for 1.

12. What does it mean? “for any constant c ” With o-notation, we mean that: no matter what constant we put in front of g(x), f(x) will be still less than cg(x) for sufficiently large n. No matter how small c is. Ex: 2n 2 = o(n 3 ) An counter example: 1/2n 2 = Ω (n 2 ) does not hold for each c. (n 0 = 2/c)

13. Two More Strict Notations We have just said that: –O-notation and Ω-notation are like ≤and ≥. –o-notation and ω-notation are like. –Difference with Ω-notation : This inequality must hold for all c instead of just for 1.

14. Solving recurrences The analysis of merge sort from Lecture 1 required us to solve a recurrence. Lecture 3: Applications of recurrences to divide- and-conquer algorithms. We often omit some details inessential while solving recurrences: –n is supposed to be an integer because the size of input is an integer typically. –Ignore the boundary conditions for convenience.

15. Substitution method The most general method : Substitution method –Guess the form of the solution. –Verify by induction. –Solve for constants. Substitution method is used to determine the upper or lower bounds of recurrence.

16. Example of Substitution EXAMPLE: T(n) = 4T(n/2) + n (n≥1) –(Assume that T(1) = Θ(1) ) Can you guess the time complexity of it? Guess O(n 3 ). (Prove O and Ω separately.) –We will prove T(n) ≤ cn 3 by induction. –First,we assume that T(k) ≤ ck 3 for k < n – T(k) ≤ ck 3 holds while k=n/2 by assumption.

17. Example of Substitution residual desired All we need is this holds as long as c ≥ 2 for n ≥ 1

18. Base Case in Induction We must also handle the initial conditions, that is, ground the induction with base cases. Base: T(n) = Θ(1) for all n < n 0, where n 0 is a suitable constant. For 1 ≤ n< n 0, we have “Θ(1)” ≤ cn 3, if we pick c big enough. Here we are! BUT this bound is not tight !

19. A tighter upper bound? We shall prove that T(n) = O(n 2 ). Assume that T(k) ≤ ck 2 for k < n. Anybody can tell me why? Now we need –n ≥ 0 But it seems impossible for n ≥ 1 residual desired

20. A tighter upper bound! IDEA: Strengthen the inductive hypothesis. Subtract a low-order term. Inductive hypothesis: T(k) ≤ c 1 k 2 – c 2 k for k< n. Now we need (c 2 -1) n ≥0, it holds if c 2 ≥ 1 residual desired

21. For the Base Case We have proved now that for any value of c 1, and provided c 2 ≥ 1. Base: We need T(1) ≤ c 1 – c 2 Assumed T(1) is some constant. We need to choose c 1 to be sufficiently larger than c 2, and c 2 has to be at least 1. To handle the initial conditions, just pick c 1 big enough with respect to c 2.

22. About Substitution method We have worked for upper bounds, and the lower bounds are similar. –Try it yourself. Shortcomings: –We had to know the answer in order to find it, which is a bit of a pain. –It would be nicer to just figure out the answer by some procedure, and that will be the next two techniques.

23. Recursion-tree method A recursion tree models the costs (time) of a recursive execution of an algorithm. The recursion-tree method can be unreliable, just like any method that uses dot,dot,dots (…). The recursion-tree method promotes intuition, however. The recursion tree method is good for generating guesses for the substitution method.

24. Example of recursion tree Solve T(n) = T(n/4) + T(n/2)+ n 2 :

25. Example of recursion tree Solve T(n) = T(n/4) + T(n/2)+ n 2 :

26. Example of recursion tree Solve T(n) = T(n/4) + T(n/2)+ n 2 :

27. Example of recursion tree Solve T(n) = T(n/4) + T(n/2)+ n 2 : ? How many leaves? We just need an upper bound. <n

28. Example of recursion tree Solve T(n) = T(n/4) + T(n/2)+ n 2 : ＜ 2n 2 So T(n) = Θ(n 2 ) ? ? ? ? Recall 1+1/2+1/4+1/8+…

29. The Master Method It looks like an application of the recursion tree method but with more precise. The sad part about the master method is it is pretty restrictive. It only applies to recurrences of the form: T(n) = a T(n/b) + f(n), where a ≥1, b >1, and f(n) is asymptotically positive. aT(n/b) means every problem you recurse on should be of the same size.

30. Three Common Cases

31. Case 1: –

32. Three Common Cases Case 1: – Case 2: –

33. Three Common Cases Case 3: –

34. Examples Ex: T(n) = 4T(n/2) + n –a = 4, b= 2 – and – –

35. Examples Ex: T(n) = 4T(n/2) + n 2 –a = 4, b= 2 – and – –

36. Examples Ex: T(n) = 4T(n/2) + n 3 –a = 4, b= 2 – and – –

37. Homework Read Chapter 3 and 4 to be prepared for applications of recurrences.

38. Proof of Master Method Height = ？ log b n #(leaves) = ?

39. Proof of Master Method Height = ？ log b n #(leaves) = ? CASE1: The weight increases geometrically from the root to the leaves. The leaves hold a constant fraction of the total weight.

40. Proof of Master Method Height = ？ log b n #(leaves) = ? CASE2(k= 0) :The weight is approximately the same on each of the log b n levels.

41. Proof of Master Method Height = ？ log b n #(leaves) = ? CASE3: The weight decreases geometrically from the root to the leaves. The root holds a constant fraction of the total weight.