

A Gentle Introduction to Algorithm Complexity Analysis (2012) - epenn
http://discrete.gr/complexity/

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tlarkworthy
A handy tip to reverse engineer complexity from empirical examples is plot on
a log-log plot and measure the gradient (if its a straight line). The gradient
is the exponent, ie. O(n^gradient). Great if your algorithm is so complex you
can't analyze it.

[http://www.dgp.toronto.edu/people/JamesStewart/378notes/05lo...](http://www.dgp.toronto.edu/people/JamesStewart/378notes/05logLog/)

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copperx
Isn't this the same as the power law?

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men
Very nice resource to have in mind. Thanks. I just started reading SICP.
Decided to take a short detour to get a better grasp on all this complexity
stuff that's hardly elaborated in SICP. Right now I am working through the
chapter on Algorithm Efficiency Analysis in Discrete Math and Applications by
Susanna Epp.

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mholt
Well, I appreciate that the article is easier to read than most textbooks on
the subject. (But for as long as it is, it really is just an _introduction_ \-
scrapes the surface.)

Also, what's up with the random pictures in the comments?

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shobhitverma
Which problem is more important a) Make simple algorithms more accessible to
the masses who do not get it even after reading a book. OR b) Make more
complex algorithms and programming patters/styles more accessible to the good
programmers who want to become excellent programmers ? I would like your
opinion and how you define "importance" ( it could be market size, or economic
impact in the world, or as some people have argued -- reduces gender
inequality in tech)

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muyuu
What's with the pics in the comments? I'm a bit confused. Is this something
about that site?

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sleazebreeze
FTA: Thanks for reading. I didn't get paid to write this article, so if you
liked it, send me an e-mail to say hello. I enjoy receiving pictures of places
around the world, so feel free to attach a picture of yourself in your city!

