Summary

Use asymptotic_notation to describe algorithm growth for large inputs. It ignores constants and lower order terms. big_O gives an upper bound, big_Omega a lower bound, and big_Theta a tight bound. little_o and little_omega are strict bounds. Apply it to time_complexity and space_complexity, often for worst_case or average_case. Common classes: constant, logarithmic, linear, n_log_n, quadratic, exponential, factorial. As n grows, higher classes dominate.