Big-O Complexity Remember, Big-O time complexity gives us an idea of the growth rate of a function. In other words, for a large input size N, as N increases, in what order of magnitude is the volume of statements executed expected to increase? A) Rearrange the following functions in increasing order of their Big-O Complexity B) Also Find the time if n=5: 4n2 log3 n 20n n2.5 n0.00000001 log n! nn 2n 2n+1 22n 3n n log n 100 n2/3 log [(log n)2 ] N! (n−1)!
Big-O Complexity Remember, Big-O time complexity gives us an idea of the growth rate of a function. In other words, for a large input size N, as N increases, in what order of magnitude is the volume of statements executed expected to increase? A) Rearrange the following functions in increasing order of their Big-O Complexity B) Also Find the time if n=5: 4n2 log3 n 20n n2.5 n0.00000001 log n! nn 2n 2n+1 22n 3n n log n 100 n2/3 log [(log n)2 ] N! (n−1)!
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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Question
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Big-O Complexity Remember, Big-O time complexity gives us an idea of the growth rate of a function. In other words, for a large input size N, as N increases, in what order of magnitude is the volume of statements executed expected to increase? A) Rearrange the following functions in increasing order of their Big-O Complexity B) Also Find the time if n=5:
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4n2
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log3 n
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20n
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n2.5
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n0.00000001
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log n!
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nn
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2n
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2n+1
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22n
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3n
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n log n
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100 n2/3
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log [(log n)2 ]
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N!
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(n−1)!
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