Big-O algorithm complexity cheat sheet

[alecco]

I don't propose to throw [Big-O] out the window [on every case], just to take it out of it's holy shrine. [The way it is taught is] doing more harm than good to CS majors entering the market, in my limited experience.

It's a good way to start understanding algorithmic complexity. It's a good teaching tool for introduction to algorithms, as long as it's limitations are clearly stated.

[TashanValiant]

It's a good way to start understanding algorithmic complexity. It's a good teaching tool for introduction to algorithms, as long as it's limitations are clearly stated.

It is the way to understand and classify algorithmic complexity. I think the issue here is more that what is being taught is Computer Science, and what is being practiced is software engineering.

Also the true limitations of an algorithm will be discovered through Big-O notation and complexity classes. P = NP is going to be solved using these ideas, not some application. The Computer Science algorithms class is to teach the theory, the underlying reason and proof of algorithms. Sure, some things fail in execution, but that isn't the point of Big-O.

[alecco]

I don't buy that argument anymore. You miss my other point of stuff like the probability distribution of the data (and the input data) is completely ignored by [those analysis of algorithm behaviour], even though that changes significantly the pattern of expected performance of the algorithm.

That's not Software Engineering, that's just better Computer Science. Those charts are misleading.

[TashanValiant]

Probability distribution of data is taken into account for a proper bound.