The code we choose to use can impact the speed and the performance of our program. How would we know which algorithm is most efficient? Big O Notation is used in Computer Science and measures how quickly the runtime of an algorithm based on the number of input in a function.
Big O looks at the worst case scenario or the max number of steps to take in a problem. On the other hand, Big Omega looks at the best case scenario or the least number of steps to take in a problem.
Common Runtimes from least to greatest effectiveness:
- O(n^2): Quadratic time - as (n) grows, runtime squares.
- O(n): Linear - as (n) scales, runtime scales.
- O(log n): Logarithmic time - halves dataset until it finds (n).
- O(1): Constant - as (n) grows, there is no impact.
Big O Complexity chart
This chart shows the runtime with green shaded area being the most effective to the red shaded areas being the least effective.
Top comments (1)
Thanks!