SLU MATH135 algorithms

SLU MATH135 algorithms - Complexity of algorithms Discrete...

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Complexity of algorithms Discrete Math MATH 135 David Oury Introduction Big-O/ Ω / Θ Definitions Examples Theorems Exercises Algorithms & complexity Introduction Search Sort Exercises Recursion Exercises Complexity of algorithms Discrete Math MATH 135 David Oury November 3, 2011 Chapter 3, Sections 1, 2, 3 (5 lectures) of Discrete Mathematics and its Applications, by Rosen
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Complexity of algorithms Discrete Math MATH 135 David Oury Introduction Big-O/ Ω / Θ Definitions Examples Theorems Exercises Algorithms & complexity Introduction Search Sort Exercises Recursion Exercises Introduction Goals 1 Define the “big-O/ Ω / Θ ” notations for comparing functions based on the rate at which they increase 2 Compare certain functions 3 Understand theorems to aid in comparison 4 Read and write procedures using operations (atomic and control flow) 5 Count atomic operations required for a procedure to complete “in the worst case” 6 Describe the complexity of procedures using this notation.
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Complexity of algorithms Discrete Math MATH 135 David Oury Introduction Big-O/ Ω / Θ Definitions Examples Theorems Exercises Algorithms & complexity Introduction Search Sort Exercises Recursion Exercises Definition: big-O, big- Ω & big- Θ I Let f and g be functions R R or Z R . Definition: big-O We write f = O ( g ( x )) and say “ f is big-O of g ” when there are constants C and k such that | f ( x ) | ≤ C | g ( x ) | if x > k . f grows more slowly then some fixed multiple of g C K x ( K < x → | f ( x ) | ≤ C · | g ( x ) | ) Use this to understand the negation, when f is not O ( g ) . Definition: big- Ω We write f = Ω( g ( x )) and say “ f is big- Ω of g ” when there are constants C and k such that C | g ( x ) | ≤ | f ( x ) | if x > k . f grows more quickly then some fixed multiple of g
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Complexity of algorithms Discrete Math MATH 135 David Oury Introduction Big-O/ Ω / Θ Definitions Examples Theorems Exercises Algorithms & complexity Introduction Search Sort Exercises Recursion Exercises Definition: big-O, big- Ω & big- Θ II Definition: big- Θ We write f = Θ( g ( x )) and say “ f is big- Θ of g or that “ f is the order of g ” when f = O ( g ( x )) and f = Ω( g ( x )) .
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Complexity of algorithms Discrete Math MATH 135 David Oury Introduction Big-O/ Ω / Θ Definitions Examples Theorems Exercises Algorithms & complexity Introduction Search Sort Exercises Recursion Exercises Examples: big-O, big- Ω & big- Θ I Example: n i =1 i = O ( n 2 ) 1 Notice that n i =1 i = n ( n + 1) / 2 = n 2 / 2 + n/ 2 . 2 Then n 2 / 2 + n/ 2 n 2 / 2 + n 2 / 2 = n 2 for n 1 . 3 Then n i =1 i = O ( n 2 ) by k = 1 and C = 1 . Example: n 1=1 i = Ω( n 2 ) 1 Then n 1=1 i (1 / 2) n 2 since n 1=1 i = n 2 / 2 + n/ 2 . 2 Then n 1=1 i = Ω( n 2 ) by k = 1 and C = 1 / 2 . Example: n i =1 i = Θ( n 2 ) By previous theorems, n 1=1 i = O ( n 2 ) and n 1=1 i = Ω( n 2 ) .
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Complexity of algorithms Discrete Math MATH 135 David Oury Introduction Big-O/ Ω / Θ Definitions Examples Theorems Exercises Algorithms & complexity Introduction Search Sort Exercises Recursion Exercises Examples: big-O, big- Ω & big- Θ II Example: n 2 is not O ( n ) Need to show that ¬∃ C k n ( n > k n 2
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