Summary

Computational complexity studies how resources grow with input size. Two core measures are time_complexity and space_complexity. We describe growth using big_o, big_theta, and big_omega. Problems and algorithms are grouped into complexity_classes like p and np. np_complete problems capture the hardest in NP. reductions connect problems. We compare worst_case and average_case behavior as problem_size increases. The goal is to gauge scalability and choose efficient algorithms.