CSSE 230
Data Structures and Algorithm Analysis

Homework 8  - 43 points

To Be Turned In

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1. Don’t forget: You should be keeping an individual log about your team project work so that you can write a performance evaluation for each of your teammates. You do not need to turn in this log, but I wanted to remind you while I had your attention.
2. ( 6 points) Weiss Exercise 8.10 [8.10].  Logarithmic form of Stirling's approximation. Simplify as much as you can, then identify the most important term to get a big-theta approximation. Show your work.
3. (15 points).  Weiss Exercise 19.7.  Prove it by strong induction on the number of internal nodes in the tree. The induction assumption applies to the left and right subtrees. This exercise is based on the discussion from pages 644-647 (beginning of section 19.3) and Exercise 19. I restate it in terms of Extended Binary Trees (EBTs), because the definition is a bit more precise than the one in the middle of page 647. An EBT T either consists of one external node, or it consists of an internal node (the root) and two subtrees, T_L and T_R, each of which is an EBT. If T is an EBT, then its internal path length, IPL(T) is the sum of the depths of all of the internal nodes of T. Similarly, its external path length, EPL(T) is the sum of the depths of all of the external nodes of T.
   
   For example, if T is the EBT shown here, then IPL(T) = 0+1+1+2+3 = 7, EPL(T) = 2+2+2+3+4+4 = 17. Can you figure out the relationship between IPL(T) and EPL(T)? (Think about it now)
   
   
   


Answer: EPL(T) = IPL( T) + 2*IN(T). It's easy to see that this formula is true for this tree. Your job is to show that it is true for all extended binary trees. Use strong induction to show that for any EBT T, EPL(T) = IPL( T) + 2*IN(T). All induction is over some integer, in this case it is the nunber of internal nodes.

Be sure that you explain what you are doing. You may introduce any new notation that may be helpful.


4. (12 points) Solve the recurrence relations below. Indicate which strategy you are using and show your work. Big-theta or exact answer required? Big-theta. On any problem where it is appropriate to use the Master Theorem, big-theta is sufficient (you can't get better from the theorem). But if the master theorem doesn't apply for a certain problem (hint, hint), you'll have to use another technique like telescoping, or filling out a table and doing guess and check. And then you'll have an exact answer anyway on your way to getting big-theta. So write the exact answer too as part of your solution.
   1. T(1) = 1, T(N) = 2 T(N/4) + N^0.5
   2. T(1) = 1, T(N) = T(N - 2) + 2, assuming N is odd.
   3. T(1) = 1, T(N) = 3 T(N/2) + 2N
5. (10 points) Use a recurrence relation to show that the running time of the printPreorder method in Figure 18.22  is O(N). You may assume that the tree is perfect (i.e., full, balanced, and size 2^k - 1 for some integer k).