Big O and Performance
We are very interested in performance
( efficiency, complexity)
of algorithms and how the cost of an algorithm increases as the size of
the problem increases. Cost can include:
- Time required
- Space (memory) required
- Network bandwidth
Understanding Big O is important to us:
- In many cases, Big O is the deciding factor in choosing a data
structure or algorithm.
- We need to be cognizant of Big O when writing code: even a single
line of code can raise the Big O of an application, making it unusable.