Run Times

In computing, there are usually all sorts of ways to tackle a problem, but coming up with an efficient and effective algorithm can be tricky. One of the ways we describe how good our algorithms are is expressing them as RUNTIMES.

big-o-complexity

As our problem size (N) increases, the amount of operations we need to do is related to our runtime. A runtime of N operations is preferred over a runtime of N^2, because as N grows, N^2 will grow much faster. A runtime of lg(N) is even better. That means that as the problem size grows, the amount of operations we need to do only goes up log base 2 of N (an extremely small number)

A good exercise is to come up with 2 very different values for N and figure out how many more operations need to be done between all the runtimes.

Author: christina

Christina is a first-year graduate student in the master's in English literature program here at DePaul. Her bachelor's is in English, and she comes from a strong background in tutoring kids K-12. Christina loves gaming, reading, and spreadsheets.

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