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--- courses:cs211:winter2012:journals:jeanpaul:chaptter2section2 [2012/01/17 04:35] – mugabej
+++ courses:cs211:winter2012:journals:jeanpaul:chaptter2section2 [2012/01/18 02:47] (current) – mugabej
@@ Line 31: / Line 31: @@
   * If we can show that a running time T(n) is both O(//f(n)//) and also Ω (//f(n)//),then we have found the //right// bound: T(//n//) grows exactly like //f(n)//to within a constant factor.
   * If a function is both  O(//f(n)//) and also Ω (//f(n)//), we say that T(//n//) is Θ(//f(n)//). In this case, we say that //f(n)// is an //asymptotically  tight bound // for T(n).
-  * If we let //f// and //g// be two functions such that lim (n tends to infinity) of //f(n)// / //g(n)// exists and is equal to some number c > 0, then //f(n)// = Θ(//g(n)//).
+  * If we let //f// and //g// be two functions such that the limit as n tends to infinity of //f(n)// / //g(n)// exists and is equal to some number c > 0, then //f(n)// = Θ(//g(n)//).
@@ Line 44: / Line 44: @@
       * If f = Ω(h) and g = Ω(h) then f + g = Ω(h)
       * If f = Θ(h) and g = Θ(h) then f + g = Θ(h)
-      * Let k be a fixed constant and let f<sub>1</sub>,f<sub>2</sub>,...,f<sub>k</sub> // and //h// be functions such that f<sub>i</sub> = O(//h//) for all //i//. Then //f<sub>1</sub> + f<sub>2</sub>+...+ f<sub>k</sub> = O(//h//).\\
+      * Let k be a fixed constant and let f<sub>1</sub>,f<sub>2</sub>,...,f<sub>k</sub> // and //h// be functions such that f<sub>i</sub> = O(//h//) for all //i//.\\ Then //f<sub>1</sub> + f<sub>2</sub>+...+ f<sub>k</sub> =O(//h//).\\
-==Asymptotic Bounds for some Common Functions ==
+===Asymptotic Bounds for some Common Functions ===
@@ Line 53: / Line 53: @@
   * __Polynomials__: For a polynomial T(n) = a<sub>0</sub> + a<sub>1</sub>n + … + a<sub>d</sub>n<sup>d</sup> of degree //d//, in which the coefficient a<sub>d</sub> is positive, //f// = O(n<sup>d</sup>)
   * __Logarithms__: log<sub>b</sub> n is the number //x// such that //b//<sup>//x//</sup> = //n//. If we round log<sub>b</sub>n to the nearest integer, it is one less than the number of digits in the base-b representation of the number //n//.
-       * For every //b// > 1 and every //x// > 0, log<sub>b</sub>n = O(n<sup>//x//</sup>.
+       * For every //b// > 1 and every //x// > 0,\\ log<sub>b</sub>n = O(n<sup>//x//</sup>).
    * 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.