reading-notes

Code Fellows 401

Big O notation is a way to describe the performance of an algorithm in terms of how the number of inputs (n) affects the number of operations it performs.
It is used to describe the upper bound of an algorithm’s running time.
The most common complexity classes are O(1), O(log n), O(n), O(n log n), O(n^2), O(2^n) and O(n!).
O(1) represents a constant time algorithm, which performs the same number of operations regardless of the input size.
O(log n) represents a logarithmic time algorithm, which performs a number of operations proportional to the logarithm of the input size.
O(n) represents a linear time algorithm, which performs a number of operations proportional to the input size.
O(n log n) represents a log-linear time algorithm, which performs a number of operations proportional to the input size multiplied by the logarithm of the input size.
O(n^2) represents a quadratic time algorithm, which performs a number of operations proportional to the input size squared.
O(2^n) represents an exponential time algorithm, which performs a number of operations proportional to 2 raised to the power of the input size.
O(n!) represents a factorial time algorithm, which performs a number of operations proportional to the factorial of the input size.