COP3530Fall1999DiagnosticKey

COP3530Fall1999DiagnosticKey - COP 3530 1. Fall 1999...

Info iconThis preview shows pages 1–3. Sign up to view the full content.

View Full Document Right Arrow Icon
COP 3530 Fall 1999 Diagnostic Exam#1 Key Name:_____________________ 1. The algorithmic (programming) techniques of recursion and iteration can be related to the mathematical proof technique of induction in a manner that allows inductive proofs of correctness and run-time complexity . Show this relationship by proving that the first of the following two code segments correctly computes N 2 , for N 0, and that the second has run time complexity 2 N –1, N 1. In this latter case, we base complexity on the number of recursive calls made. function sq(N : integer) : integer; begin if N<=0 then sq := 0 else sq := 2*N – 1 + sq(N-1) end ; { sq } HINT : Prove S(N): sq(N) = N 2 , N 0 Basis: S(0): sq(0) = 0 by definition of function and fact that 0 <= 0. But, 0 2 = 0 and thus sq(0) = 0 2 IH: Assume, for some N>0, that S(k), for all k <N. IS: Show S(N). sq(N) = 2*N – 1 + sq(N – 1) by definition of function and fact that N>0. But, sq(N-1) = (N – 1) 2 by inductive hypothesis. Hence sq(N)= 2* N – 1 + N 2 – 2*N + 1 = N 2 procedure Move (n:integer; X, Y, Z:char); begin if n = 1 then writeln('Move ', X, ' to ', Y) else begin Move (n-1, X, Z, Y); writeln('Move ', X, ' to ', Y); Move (n-1, Z, Y, X) end end ; { Move } HINT : Prove S(N): T(N) = 2 N –1, N 1, where T(1) = 1; T(N) = 2 * T(N–1) + 1, N>1. Basis: S(1): T(1) = 1 by definition of function and fact that 1 = 1. For this case, there is only the initial call to Move, with no subsequent recursive calls. IH: Assume, for some N>1, that S(k), for all k <N. IS: Show S(N). T(N) = 2*T(N–1) + 1 by definition of function and fact that N>1. For this case we recursively call Move twice, each time with its first parameter set to N–1. But, T(N–1) = 2 N-1 –1 by inductive hypothesis. Hence T(N)= 2*(2 N-1 –1) + 1 = 2 N – 2 + 1 = 2 N – 1
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
C. E. Hughes, UCF Computer Science – 2 – COP 3530 Spring ‘99 2. A dictionary is an ADT that responds to the messages insert (newWord), lookup (oldWord) and delete (oldWord). There are many competing abstract implementations for a dictionary, three of which are a sorted linear list (not a linked list), a balanced binary search tree and a trie . Focusing on the lookup only, I have given informal algorithms and analyses of the costs of looking up a word. Discuss the pros and cons of each abstract implementation. Be sure to specify the complexity of the other operations (insert and delete) for each implementation. lookup sorted linear list Start in the middle of the list. If the word is found, report success. If not, and the new word is less than the one in the middle, ignore the bottom half, focusing on the top half of list only. Otherwise, ignore the top half, focusing on the bottom only. If you run out of words in the list, report failure. The search takes O(logN) operations. balanced binary search tree
Background image of page 2
Image of page 3
This is the end of the preview. Sign up to access the rest of the document.

Page1 / 6

COP3530Fall1999DiagnosticKey - COP 3530 1. Fall 1999...

This preview shows document pages 1 - 3. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online