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Unformatted text preview: Mathematical Background and Running Time Analysis EECS 2332 Previous Lecture: Mathematical Defnitions T ( N ) = O ( f ( N )) if there are positive constants c and n such that T ( N )<= cf ( N ) when N >= n T ( N ) = ( f ( N )) if there are positive constants c and n such that T ( N )>= cf ( N ) when N >= n T ( N ) = ( f ( N )) iff T ( N ) = O ( f ( N )) and T ( N ) = ( f ( N )) T ( N ) = o ( f ( N )) if for all constants c there exists an n such that T ( N )< cf ( N ) when N > n3 How to Determine the Relative Growth Rate? If lim N>infinity T ( N ) / f ( N ) = 0: T ( N ) = o ( f ( N )) = c != 0: T ( N ) = ( f ( N )) = infinity: f ( N ) = o ( T ( N )) If no convergence <= c : T ( N ) = O ( f ( N )) T ( N ) = N f ( N ) = N if N is odd, or N 2 if N is even >= c : f ( N ) = O ( T ( N )) Otherwise (could be > c and < c for any c ): no relation T ( N ) = N f ( N ) = 1 if N is odd, or N 2 if N is even T ( N ) = o ( f ( N )) if for all constants c there exists an n such that T ( N )< cf ( N ) when N > n . T ( N ) = ( f ( N )) if there are positive constants c and n such that T ( N )<= cf ( N ) when N >= n AND there are positive constants c* and n* such that T ( N )>= cf ( N ) when N >= n* T ( N ) = O ( f ( N )) if there are positive constants c and n such that T ( N )<= cf ( N ) when N >= n4 Logarithm Properties Nonnegative, monotonic function for N 1 Sub linear growth: Basic equivalencies: Corollary: logarithm grows slower than any polynomial! log( N ) = o ( N x ) " log(log( N ) = o (log( N x ) = o ( x log( N )) " log Y = o ( xY ) = o ( Y ) log( X ) = o ( X )5 Relative Growth Rates Examples (which grows faster?) 1000000 versus 0.01*sqrt( N ) log( N ) versus sqrt( N ) N log( N ) versus N 1.001 N 3 versus 10000* N 2 log 2 ( N ) versus 10*log( N 5 ) 2*log 2 ( N ) versus log 3 ( N ) N *2 N versus 3 N 10*log( N 5 ) = 50*log( N ) 3 N = 1.5 N * 2 N N 1.001 = N * N 0.001 6...
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 Spring '08
 Rabinovich

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