A randomized algorithm is a type of algorithm that uses random numbers or random choices to make decisions during its execution. This randomness can help simplify complex problems, improve performance, or provide approximate solutions. Randomized algorithms are often faster than their deterministic counterparts, especially for large datasets. These algorithms are commonly used in various fields, including computer science, statistics, and optimization. Examples include the Monte Carlo method, which relies on random sampling to estimate numerical results, and the Las Vegas algorithm, which always produces the correct result but may take varying amounts of time to do so.