CS300-03_Algorithm_Analysis

# CS300-03_Algorithm_Analysis - 3. How to Measure...

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11 3. How to Measure Complexities How good is our measure of work done to compare algorithms ? How precisely can we compare two algorithms using our measure of work ? Measure of work # of passes of a loop # of basic operations Time complexity c 1 (measure of work)

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22 For solving a problem P , suppose that two algorithms A 1 and A 2 need 106 n and 5 n basic operations, respectively, i.e. basic operations A 1 106 n A 2 5 n Which one is better ? What are their time complexities ? A 1 c1∙ 106 n O( n ) A 2 c2∙ 5 n O( n ) Does this mean that A 1( A 2) is as good as A 2( A 1) ? Well, …… 1 Both # of basic operations (loops) and time complexity are imprecise !!!
Now, suppose that algorithms A 1 and A 2 need the following amount of time: time complexity A 1 106 n O( n ) 10 n T ( n )

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44 N = {0, 1, 2, …} N + = {1, 2, 3, …} R = the set of real numbers R + = the set of positive real numbers R * = R + {0} f : N R * and g : N R * g is: 1 ( f ): g grows at least as fast as f . 1 ( f ): g grows as the same rate as f . O( f ): g grows no faster than f .
55 Definition : Let f : N R * . O( f ) is the set of functions, g : N R * such that for some c R + and some n 0 N , g ( n ) c f ( n ) for all n n 0. O( f ) is usually called “big oh of f ”, “oh of f ”, or “order of f ”. Note: In other books, g ( n ) = O( f ( n )) if and only if there exist two positive constants c and n 0 such that | g ( n )| c | f ( n )| for all n n 0 Under the assumption that f : N R * and g: N R *, two definitions have a minor difference. How to check : n 2, 105 n 2 - n , n 2 + 1010, 103 n 2 + n - 1 O( n 2) Is 1010 n | O( n 2) ? What is it ?

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66 Definition : Let f : N R * . ( f ) is the set of functions, g : N R * such that for some c R + and some n 0 N , g ( n ) c 1 f ( n ) for all n n 0. 1 ( f ) is usually called “big omega of f ” or “omega of f ”. Note: In other books, g ( n ) = ( f ( n )) if and only if there exist two positive constants c and n 0 such that | g ( n )| c 1 | f ( n )| for all n n 0 How to check : 105 n , n 2, n 2 + 10 n + 106 ( n ). Are they also (log n ) ?
77 Definition : Let f : N R * . ( f ) = O( f ) ( f ).

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## This note was uploaded on 10/07/2010 for the course CS 300 taught by Professor Shin during the Spring '10 term at Korea Advanced Institute of Science and Technology.

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CS300-03_Algorithm_Analysis - 3. How to Measure...

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