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| courses:cs211:winter2012:journals:jeanpaul:chaptter2section2 [2012/01/17 05:25] – mugabej | courses:cs211:winter2012:journals:jeanpaul:chaptter2section2 [2012/01/18 02:47] (current) – mugabej | ||
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| These three functions are the most frequent functions encountered when analyzing running times. Logarithms grow more slowly than polynomials and polynomials grow more slowly than exponentials.\\ | These three functions are the most frequent functions encountered when analyzing running times. Logarithms grow more slowly than polynomials and polynomials grow more slowly than exponentials.\\ | ||
| - | There are several motivations for studying the bounds of the running times of algorithms. Knowing the running time of an algorithm, one can easily perfect an algorithm as much he/she can. The running times serve as a measure of comparison when searching for which algorithm is more efficient for a given problem. Thus a programmer should always aim for the most efficient algorithm he/she can possibly design and be convinced it's more efficient than that he/she had implemented | + | There are several motivations for studying the bounds of the running times of algorithms. Knowing the running time of an algorithm, one can easily perfect an algorithm as much he/she can. The running times serve as a measure of comparison when searching for which algorithm is more efficient for a given problem. Thus a programmer should always aim for the most efficient algorithm he/she can possibly design and be convinced it's more efficient than that he/she had previously |
| + | I don't have a specific question about this section as most of the material makes sense. After rereading this section and seeing how it was presented in class, I can now see the importance of tight bounds. Tight bounds are a new concept for me when it comes to running times of algorithms, | ||
| + | This section was interesting too, I give it a 9/10. | ||
