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Unformatted text preview: 4. Yes you can always estimate the running time of an algorithm in Big-O term so its easier to have an idea of how fast the running time is without wasting time on calculating the exact run time. 5. n! grows faster. The rate of growth is greater for n! compared to 2^n 6. a. O(n^5) b. O(5^n) c. O(n) d. O(n) e. O(n^2) 7. There is one for loop so the running time is O(n) 8. There is one for loop so the running time is O(n) 9. There are two for loop so the running time is O(n^2) 10. There is one for loop so the running time is O(n)...
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This note was uploaded on 04/07/2008 for the course CS 3345 taught by Professor Ozbirn during the Spring '08 term at University of Texas at Dallas, Richardson.
- Spring '08
- Data Structures