CSE331 Lecture 8

CSE331 Lecture 8 - Lecture 8 CSE 331 Sep 15, 2011 HW 1due...

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Lecture 8 CSE 331 Sep 15, 2011
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HW 1due today Q1, Q2 and Q3 in different piles I will not take any HW after 1:15pm
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7 more to go… I’ll need confirmation in writing. No graded material will be handed back till I get this signed form from you!
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Solutions to HW 1 Handed out at the END of the lecture
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HW 2 Has been posted (link on the blog by 3pm) Start early
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Definition-III Should scale with input size If N increases by a constant factor, so should the measure Polynomial running time At most c . N d steps (c>0, d>0 absolute constants) At most c . N d steps ( c>0 , d>0 absolute constants) Step: “primitive computational step”
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Reading Assignments Sections 1.2, 2.1, 2.2 and 2.4 in [KT]
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The actual run times n! 100n 2 n 2 Asymptotic View Asymptotic View
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Asymptotic Notation is O with glasses is Ω with glasses = is Θ with glasses
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Run time of an algorithm (Worst-case) run time T(n) for input size n Maximum number of steps taken by the algorithm for any input of size n
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g(n) is O(f(n)) g(n) n 0 c*f(n) for some c>0 n
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g(n) is Ω(f(n)) g(n) n 1 n ε*f(n) for some ε>0
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Questions?
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This note was uploaded on 12/11/2011 for the course CSE 331 taught by Professor Rudra during the Fall '11 term at SUNY Buffalo.

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CSE331 Lecture 8 - Lecture 8 CSE 331 Sep 15, 2011 HW 1due...

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