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--- courses:cs211:winter2012:journals:jeanpaul:chaptter2section2 [2012/01/17 04:41] – mugabej
+++ courses:cs211:winter2012:journals:jeanpaul:chaptter2section2 [2012/01/18 02:47] (current) – mugabej
@@ Line 56: / Line 56: @@
    * Exponentials : Let //f(//n//)// = r<sup>n</sup>. For every r > 1 and every //d// > 0, //n// <sup>d</sup> = O(r<sup>n</sup>).
-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 previously implemented thanks to the knowledge of the running times of algorithms.\\
+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,but I was able to see why they are useful in analyzing the algorithm: They give approximate,more precise limits to the running time of the algorithms. I would like to remember the properties of the running times of algorithms, they look very useful and I'm convinced they can help a lot in analyzing running times of algorithms.\\
+This section was interesting too, I give it a 9/10.