randomized output-sensitive algorithms

Randomized output-sensitive algorithms are computational methods that adapt to the size of their output rather than just the size of their input. They use randomness to make decisions during execution, often resulting in faster performance for specific problems, particularly when the output size varies widely. For example, if you're searching for a small number of items in a large dataset, these algorithms can be more efficient than traditional ones, as they process only what’s necessary based on the actual output. This approach balances speed and resource use by tailoring the computation to the problem's specific requirements.