average-case complexity classes

Average-case complexity classes analyze how efficiently algorithms perform on typical inputs, rather than the worst possible scenarios. While worst-case complexity assesses how long a problem might take in the most difficult cases, average-case considers how the algorithm behaves on average, assuming inputs follow a certain probability distribution. These classes help us understand real-world performance, which often differs from worst-case predictions. For example, some problems might be very hard in the worst case but are usually easy on average. Studying average-case complexity guides better algorithm design and provides more practical expectations for typical use cases.