5. n log? n = 0(n²) 6. n log n 20(n) n 3=
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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BIG 0 practice
true or false and explain why true or false

Transcribed Image Text:5. \( n \log^2 n = O(n^2) \)
6. \( n \log^2 n = 2^{O(n)} \)
This transcription contains mathematical equations typical in algorithm analysis, dealing with asymptotic notation. Here, \( n \) is a variable often representing the size of the input, and \( O \) is the Big O notation used to describe an upper bound on the time complexity of an algorithm.
- In equation 5, \( n \log^2 n = O(n^2) \), it suggests that the function \( n \log^2 n \) grows at a rate that is eventually slower than or equal to that of \( n^2 \).
- In equation 6, \( n \log^2 n = 2^{O(n)} \), it implies that the function \( n \log^2 n \) grows at a rate slower than or equal to an exponential function \( 2^n \).
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