Asymptotic

overview

Summary

Asymptotic analysis describes algorithm growth as input_size_n increases. It uses bounds: big_O upper bound, big_Omega lower bound, big_Theta tight bound, plus little_o and little_omega. We measure time_complexity and space_complexity. Typical growth: constant_time, logarithmic_time, linear_time, n_log_n_time, quadratic_time, exponential_time. Cases include worst_case, average_case, best_case. Ignore constants and low-order terms to focus on growth_rate. These notations compare algorithms independent of hardware and implementation details. Log bases are treated as constants.