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The average time complexity for an algorithm can be found by adding the best time and worst time and dividing that answer by 2. True False

The average time complexity for an algorithm can be found by adding the best time and worst time and dividing that answer by 2. True False

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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The average time complexity for an algorithm can be found by adding the best time and worst time and dividing that answer by 2. O True False
Transcribed Image Text:The average time complexity for an algorithm can be found by adding the best time and worst time and dividing that answer by 2. O True False
The linear search algorithm's growth rate is O(n). (The linear search searches the first item, then the second, then the third, etc., until it finds what it's looking for or until it runs out of items to search.) The binary search algorithm's growth rate is O(log n). Which is the faster search for a large number of items? The binary search because log n is smaller than n for large values of n. O The binary search because log n is always smaller than 1. The linear search because log n is smaller than n for large values of n. O The linear search because n grows faster than log n as n gets large.
Transcribed Image Text:The linear search algorithm's growth rate is O(n). (The linear search searches the first item, then the second, then the third, etc., until it finds what it's looking for or until it runs out of items to search.) The binary search algorithm's growth rate is O(log n). Which is the faster search for a large number of items? The binary search because log n is smaller than n for large values of n. O The binary search because log n is always smaller than 1. The linear search because log n is smaller than n for large values of n. O The linear search because n grows faster than log n as n gets large.
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