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In this video we're going to explore an example of a quasilinear runtime
Glossary
Quasilinear Time - O(n log n): Given a data set of size n, the algorithm executes an n number of operations where each operation runs in log n (logarithmic) time
Resources
The next worst case runtime we're going to
look at is one that's called quasilinear.
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And it's sort of easier to understand,
for lack of a better word,
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by starting with the big O notation.
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Quasilinear runtimes are written
out as big O(n log n).
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We learned what (log n) was, right?
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A logarithmic runtime where
as n grew the number of
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operations only increased
by a small factor.
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With a quasilinear runtime
what we're saying is that for
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every value of n we're going to
execute a log n number of operations.
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Hence the runtime of n times log n.
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So you saw earlier with a quadratic
run time, that for each value of n,
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we conducted n operations.
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It's sort of the same in that as we
go through the range of values in n,
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we're executing log n operations.
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In comparison to other runtimes,
a quasilinear algorithm has a runtime that
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lies somewhere between a linear
runtime and a quadratic runtime.
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So where would we expect to see this
kind of runtime in practical use?
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Well, sorting algorithms is one
place you will definitely see it.
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Merge Sort, for example,
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is a sorting algorithm that has a worst
case run time time of big O(n log n).
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Let's take a look at a quick example.
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Let's say we start off with a list
of numbers that looks like this, and
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we need to sort it.
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Merge Sort starts by splitting this
list into two lists down the middle.
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It then takes each sublist and
splits that in half down the middle again.
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It keeps doing this until we end up
with a list of just a single number.
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When we're down to single numbers
we can do one sort operation and
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merge these sublists back
in the opposite direction.
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The first part of Merge Sort cuts those
lists into sublists with half the numbers.
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This is similar to binary search,
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where each comparison operation cuts
down the range to half the values.
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You know the worst case run
time in binary search is log n.
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So the splitting operations have the same
runtime, big O(log n)or log arethmic.
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But splitting into half isn't the only
thing we need to do with Merge Sort.
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We also need to carry out comparison
operations so we can sort those values.
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And if you look at each
set of this algorithm,
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we carry out in n number
of comparison operations.
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And that brings the worst case run time
of this algorithm to n times log n,
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also known as quasilinear.
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Don't worry if you didn't
understand how Merge Sort works.
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That wasn't the point
of this demonstration.
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We will be covering Merge Sort soon,
in a future course.
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