4 ms·

This is a fine example of Stack* giving huge rankings to very bad answers. Big O is about execution time not complexity. (And describing execution time as "time

This is a fine example of Stack* giving huge rankings to very bad answers. Big O is about execution time not complexity. (And describing execution time as "time complexity" as some comments do is ridiculous. A lot of something is not more complicated than a little of something. 4 isn't more complex than π -- and no, I'm not saying the reverse is true either.) A plain English explanation of Big O notation shouldn't take more than a few sentences. It's not that complicated, especially if you don't get into nitty gritty.

barrkel 13y ago
You can talk about memory usage just as easily with big O as execution time (or space complexity vs time complexity).
- podperson 13y ago
  Sure, but again not complexity.
oskarkv 13y ago
Big O is not necessarily about execution time. Also, why is it ridiculous to describe execution time as "time complexity"? From http://en.wikipedia.org/wiki/Analysis_of_algorithms http://en.wikipedia.org/wiki/Analysis_of_algorithms "Usually, the efficiency or running time of an algorithm is stated as a function relating the input length to the number of steps (time complexity) or storage locations (space complexity)."
- haberman 13y ago
  > Also, why is it ridiculous to describe execution time as "time complexity"? It's unnecessarily different than the normal definition of "complexity." When you say "complex" in normal conversation, you mean "complicated" or "hard to understand." You would never say "the time complexity of getting to the airport is 30 minutes." It also begs the question. Wrapping your head around the idea of asymptotic complexity is the hard part of understanding big O. Defining big O in terms of complexity doesn't help if you don't understand the concept of asymptotic complexity yet. I think the best way to explain this, by far, is with a graph.
- podperson 13y ago
  If the definition uses "complexity" in this way then it's not using it in the "plain English" sense, but in a specialized sense. Quantity isn't complexity.

barrkel 13y ago

You can talk about memory usage just as easily with big O as execution time (or space complexity vs time complexity).

podperson 13y ago
Sure, but again not complexity.

oskarkv 13y ago

Big O is not necessarily about execution time. Also, why is it ridiculous to describe execution time as "time complexity"? From http://en.wikipedia.org/wiki/Analysis_of_algorithms http://en.wikipedia.org/wiki/Analysis_of_algorithms "Usually, the efficiency or running time of an algorithm is stated as a function relating the input length to the number of steps (time complexity) or storage locations (space complexity)."

haberman 13y ago
> Also, why is it ridiculous to describe execution time as "time complexity"? It's unnecessarily different than the normal definition of "complexity." When you say "complex" in normal conversation, you mean "complicated" or "hard to understand." You would never say "the time complexity of getting to the airport is 30 minutes." It also begs the question. Wrapping your head around the idea of asymptotic complexity is the hard part of understanding big O. Defining big O in terms of complexity doesn't help if you don't understand the concept of asymptotic complexity yet. I think the best way to explain this, by far, is with a graph.
podperson 13y ago
If the definition uses "complexity" in this way then it's not using it in the "plain English" sense, but in a specialized sense. Quantity isn't complexity.