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| courses:cs211:winter2012:journals:carrie:ch2 [2012/01/19 21:13] – hopkinsc | courses:cs211:winter2012:journals:carrie:ch2 [2012/01/23 14:08] (current) – hopkinsc | ||
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| - | * * Unordered List Item====== Chapter 2 ====== | + | ====== Chapter 2 ====== |
| - | ===== 2.1: Computational Tractability | + | ==== 2.1: Computational Tractability ==== |
| The goal of this course is to write correct and efficient algorithms and this sections goes through how we define efficiency. | The goal of this course is to write correct and efficient algorithms and this sections goes through how we define efficiency. | ||
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| * Too specific | * Too specific | ||
| - | ===== 2.2 Asymptotic Order of Growth | + | ==== 2.2 Asymptotic Order of Growth ==== |
| This section discuss how to measure the order of an algorithm and looks at how to find a lower bound, an upper bound, and a tight bound. Our goal is to express the growth rate of running times of algorithms. | This section discuss how to measure the order of an algorithm and looks at how to find a lower bound, an upper bound, and a tight bound. Our goal is to express the growth rate of running times of algorithms. | ||
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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' | ||
| ==== 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. | ||
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| + | 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. |
