lineair

anonymous asked:

Is the story lineair or are there different paths?

I’m planning on there being two endings. The only reason I don’t want too many different endings is because I’ve worked so hard on this one story, I don’t think I could possibly change a thing about it ever

Some common time complexities of algorithms.

The time complexity of an algorithm describes the amount time the algorithm takes to handle an input of a certain length (in worst case scenario). For instance, an algorithm checking whether a certain number is present in a given list, possibly needs to run over the whole list to find the answer, so its complexity f(n) is a lineair function of the length n of the list (f(n)=n). It has been shown that to sort a generic list, the fastest algorithms possible are of the complexity class n ln(n).

More difficult problems generally require more time to be solved. For example, solving the traveling salesman problem via brute-force search has time complexity n!, and there are even algorithms with complexity 2^(2^n) or worse, which plainly is not useful for practical applications. An algorithm is said to be efficient if its time complexity is bounded by a polynomial, because then its runtime remains “reasonable” for bigger input values. Notice the plot above is a log plot, so complexities like n! grow even faster by comparison to polynomials.

The Millennium Problem P versus NP is related to this concept, as it asks whether or not one can find a solution to a problem in polynomial time (deterministically) if one can check a solution in polynomial time.