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| courses:cs211:winter2012:journals:garrett:entries:week_7 [2012/03/07 03:04] – added readability score garrettheath4 | courses:cs211:winter2012:journals:garrett:entries:week_7 [2012/03/07 04:00] (current) – fixed equation scaling garrettheath4 |
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| This section of the reading describes mergesort as the most canonical divide-and-conquer algorithm. A **divide-and-conquer algorithm** is an algorithm that //divides// the input into multiple pieces (usually 2), //solves// each subproblem using recursion, and then //combines// the results of those recursive calls into a solution. As with all recursion, the recursive calls in a divide-and-conquer algorithm need to have a base case so that the algorithm has a point at which it stops dividing and starts conquering. Usually this "bottom out" point is when the recursive method is called on an input of some predefined constant size, such as 1. | This section of the reading describes mergesort as the most canonical divide-and-conquer algorithm. A **divide-and-conquer algorithm** is an algorithm that //divides// the input into multiple pieces (usually 2), //solves// each subproblem using recursion, and then //combines// the results of those recursive calls into a solution. As with all recursion, the recursive calls in a divide-and-conquer algorithm need to have a base case so that the algorithm has a point at which it stops dividing and starts conquering. Usually this "bottom out" point is when the recursive method is called on an input of some predefined constant size, such as 1. |
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| Divide-and-conquer algorithms are a little less straightforward in computing an algorithm's order complexity, but their efficiency can be expressed as a **recurrence relation**. Mergesort can be measured by the number of comparisons based on the input size. This measurment can be expressed as a function that computes the number of comparisons (or "time") based on the input size ''n'': | Divide-and-conquer algorithms are a little less straightforward in computing an algorithm's time complexity, but their efficiency can be expressed as a **recurrence relation**. Mergesort can be measured by the number of comparisons based on the input size. This measurment can be expressed as a function that computes the number of comparisons (or "time") based on the input size ''n'': |
| | ''T(n) ≤ 2T(n/2) + cn'' | if ''n > 2'' | | | ''T(n) ≤ 2T(n/2) + cn'' | if ''n > 2'' | |
| | ''T(2) ≤ c'' | if ''n = 2'' | | | ''T(2) ≤ c'' | if ''n = 2'' | |
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| == Quadratic Split and Reduce == | == Quadratic Split and Reduce == |
| {{:courses:cs211:winter2012:journals:garrett:entries:journal_7_-_equation_5-2c.png?99}} | {{:courses:cs211:winter2012:journals:garrett:entries:journal_7_-_equation_5-2c.png?100|}} |
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| === 5.3: Counting Inversions === | === 5.3: Counting Inversions === |
| | A good way to quantify the differences between two comparable sets of data is to determine the degrees of difference between the two. A generalized way to do this is to calculate the number of inversions between the two. Assuming the data in question is organized like two lists of the same set of elements, an **inversion** is the minimum number of times that two elements need to trade places in the first list to become the second list. For example, if you have the following list: |
| | ^2^4^1^3^5^ |
| | and you are comparing it to: |
| | ^1^2^3^4^5^ |
| | you could quantify the differences between them by saying that it would take 3 inversions to get from the first list of data to the second list of data: |
| | ^2^4^1^3^5^ |
| | |1|4|2|3|5| |
| | |1|2|4|3|5| |
| | ^1^2^3^4^5^ |
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| | We can use the algorithm on __Pages 224-225__ to count inversions. This algorithm has a time complexity of: |
| | {{:courses:cs211:winter2012:journals:garrett:entries:bigo-n-log-n.png?80|}}, meaning that that is the the time it would take to find the number of inversions and perform them. |
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| ~~DISCUSSION~~ | ~~DISCUSSION~~ |
