chapter2 - Chapter 2 Solutions January 31, 2007 2 2.2....

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Chapter 2– Solutions January 31, 2007
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2 2.2. Consider b d log b n e . 2.3. a) T ( n ) = 3 T ± n 2 ² + cn = ··· = 3 k T ± n 2 k ² + cn k - 1 X i =0 ³ 3 2 ´ i = = 3 k T ± n 2 k ² + 2 cn µ ³ 3 2 ´ k - 1 ! For k = log 2 n , T ( n 2 k ) = T (1) = d = O (1). Then: T ( n ) = dn log 2 3 + 2 cn ³ n log 2 3 n - ´ = Θ( n log 2 3 ) as predicted by the Master theorem. b) T ( n ) = T ( n - 1) + c = ··· = T ( n - k ) + kc . For k = n , T ( n ) = T (0) + nc = Θ( n ). 2.4. a) This is a case of the Master theorem with a = 5 ,b = 2 ,d = 1. As a > b d , the running time is O ( n log b a ) = O ( n log 2 5 ) = O ( n 2 . 33 ). b) T ( n ) = 2 T ( n - 1)+ C , for some constant C . T ( n ) can then be expanded to C n - 1 i =0 2 i +2 n T (0) = O (2 n ). c) This is a case of the Master theorem with a = 9 ,b = 3 ,d = 2. As a = b d , the running time is O ( n d log n ) = O ( n 2 log n ). 2.5. a) T ( n ) = 2 T ( n/ 3) + 1 = Θ( n log 3 2 ) by the Master theorem. b) T ( n ) = 5 T ( n/ 4) + n = Θ( n log 4 5 ) by the Master theorem. c) T ( n ) = 7 T ( n/ 7) + n = Θ( n log 7 n ) by the Master theorem. d) T ( n ) = 9 T ( n/ 3) + n 2 = Θ( n 2 log 3 n ) by the Master theorem. e) T ( n ) = 8 T ( n/ 2) + n 3 = Θ( n 3 log 2 n ) by the Master theorem. f) T ( n ) = 49 T ( n/ 25) + n 3 / 2 log n = Θ( n 3 / 2 log n ). Apply the same reasoning of the proof of the Master Theorem. The contribution of level i of the recursion is ³ 49 25 3 / 2 ´ i n 3 / 2 log ± n 25 3 / 2 ² = ³ 49 125 ´ i O ( n 3 / 2 log n ) Because the corresponding geometric series is dominated by the contribution of the first level, we obtain T ( n ) = O ( n 3 / 2 log n ). But, T ( n ) is clearly Ω( n 3 / 2 log n ). Hence, T ( n ) = Θ( n 3 / 2 log n ). g) T ( n ) = T ( n - 1) + 2 = Θ( n ). h) T ( n ) = T ( n - 1) + n c = n i =0 i c + T (0) = Θ( n c +1 ). i) T ( n ) = T ( n - 1) + c n = n i =0 c i + T (0) = c n +1 - 1 c - 1 + T (0) = Θ( c n ). j) T ( n ) = 2 T ( n - 1) + 1 = n - 1 i =0 2 i + 2 n T (0) = Θ(2 n ). k) T ( n ) = T ( n ) + 1 = k i =0 1 + T ( b ), where k Z such that n 1 2 k is a small constant b , i.e. the size of the base case. This implies k = Θ(log log n ) and T ( n ) = Θ(log log n ). 2.6. The corresponding polynomial is 1 t 0 ( 1 + x + x 2 + ··· + x t 0 ) .
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3 2.7. For n 6 = 0 and ω = e 2 π n : n - 1 X i =0 ω i = 1 - ω n 1 - ω = 0 n - 1 Y i =0 ω i = ω n - 1 i =0 i = ω n ( n - 1) 2 The latter is 1 if n is odd and - 1 if n is even. 2.8. a) The appropriate value of ω is i . We have FFT(1 , 0 , 0 , 0) = (1 , 1 , 1 , 1) and FFT(1 / 4 , 1 / 4 , 1 / 4 , 1 / 4) = (1 , 0 , 0 , 0). b) FFT(1 , 0 , 1 , - 1) = (1 ,i, 3 , - i ). 2.9. a) Use ω = i . The FFT of x + 1 is FFT(1 , 1 , 0 , 0) = (2 ,i + 1 , 0 ,i - 1). The FFT of x 2 + 1 is FFT(1 , 0 , 1 , 0) = (2 , 0 , 2 , 0). Hence, the FFT of their product is (4 , 0 , 0 , 0), corresponding to the polynomial 1 + x + x 2 + x 3 .
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chapter2 - Chapter 2 Solutions January 31, 2007 2 2.2....

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