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courses:cs211:winter2012:journals:carrie:ch2 [2012/01/19 21:14] โ€“ hopkinsccourses:cs211:winter2012:journals:carrie:ch2 [2012/01/23 14:08] (current) โ€“ hopkinsc
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 This gives O(n^2) running time.  This gives O(n^2) running time. 
  
 +Getting into this stuff is more difficult for me. I don't remember running times as much and I try to think of it logically and actually go through the process, but it doesn't always work. Any step by step process would be helpful, but I am guessing it's something you get better at with time. 
 ==== 2.4: A Survey of Common Running Times ==== ==== 2.4: A Survey of Common Running Times ====
 This section was easier to read since we just spent so much time on it and I felt pretty comfortable with it.  This section was easier to read since we just spent so much time on it and I felt pretty comfortable with it. 
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   * Binary search = logn   * Binary search = logn
   * O(logn) arises when we are doing constant amount of work in order to throw away a fraction of the input.    * O(logn) arises when we are doing constant amount of work in order to throw away a fraction of the input. 
 +
 +This all makes sense to me, I think the biggest struggle will be when I need to design an alogirthm with a certain running time, but these are the notes I should look at! 
  
 ==== 2.5: A More Complex Data Structure Priority Queues ==== ==== 2.5: A More Complex Data Structure Priority Queues ====
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   * ExtractMin(H)   * ExtractMin(H)
  
-I think heaps are soooo fun! +I think heaps are soooo fun! I enjoyed this chapter because it layed out the code in a pretty readabe way. 
  
  
    
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