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Unformatted text preview: CS180 Algorithms Midterm Examination Student ID: First name: Middle name: Last name: These are not complete solutions but only the major ideas behind solutions. 1 Problem 1. [15%] Solve the following recurrences. You should only give the solutions in Θnotation and write one line of explanation for each. For simplicity, assume that T ( n ) = 1 for all n ≤ 2. 1. T ( n ) = T ( n 2) + 2 2. T ( n ) = 3 T ( n/ 3) + n 3. T ( n ) = 4 T ( n/ 3) + n 2 4. T ( n ) = T ( √ n ) + 1 5. T ( n ) = 2 T ( √ n ) (Hint: Use the previous one) Solution 1. T ( n ) = Θ( n ). It is similar to T ( n ) = T ( n 1) + 1. 2. T ( n ) = Θ( n log n ). Master Theorem. 3. T ( n ) = Θ( n 2 ). Master Theorem. 4. T ( n ) = log log n . One of the homework problems. 5. T ( n ) = Θ(log n ). Observe that log T ( n ) = log T ( √ n ) + 1 therefore using the previous one: log T ( n ) = log log n which gives the result. 2 Problem 2. [15%] What is the running time in Θnotation (as a function of n ) of the following code? Give a 3line explanation. for x=1 to n do begin y=x; while y>1 do y=y/2; end Solution The while loop takes time log x (it halves x repeatedly). Therefore the run ning time is n X x =1 log x = log(1 · 2 ··· n ) = log( n !) = Θ( n log n ) ....
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 Spring '09
 Naver
 Algorithms, Array, Analysis of algorithms, Fibonacci number, notation

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