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