Big O complexity time
The following diagram reveals that, at some moment in time, O(n) surpasses O(1). So, until O(n) surpasses O(1), we can say that O(n) performs better than O(1):
Figure 7.1 – The asymptotic runtime (Big O time)
Besides the O(1)—constant time—and O(n)—linear time runtimes—we have many other runtimes, such as O(log n), O(n log n)—logarithmic time—O(n2)—quadratic time, O(2n)— exponential time, and O(n!)—factorial time. These are the most common runtimes, but many more also exist.
The following diagram represents the Big O complexity chart:
Figure 7.2 – Big O complexity chart
As you can see, not all O times perform the same. O(n!), O(2n), and O(n2) are considered horrible and we should strive to write algorithms that perform outside this area. O(n log n) is better than O(n!) but is still bad. O(n) is considered fair, while O(log n) and O...
