Las Vegas algorithm

In computing, a Las Vegas algorithm is a randomized algorithm that always gives correct results; that is, it always produces the correct result or it informs about the failure. However, the runtime of a Las Vegas algorithm differs depending on the input. The usual definition of a Las Vegas algorithm includes the restriction that the expected runtime be finite, where the expectation is carried out over the space of random information, or entropy, used in the algorithm. An alternative definition requires that a Las Vegas algorithm always terminates (is effective), but may output a symbol not part of the solution space to indicate failure in finding a solution. The nature of Las Vegas algorithms makes them suitable in situations where the number of possible solutions is limited, and where verifying the correctness of a candidate solution is relatively easy while finding a solution is complex.

Las Vegas algorithms are prominent in the field of artificial intelligence, and in other areas of computer science and operations research. In AI, stochastic local search (SLS) algorithms are considered to be of Las Vegas type. SLS algorithms have been used to address NP-complete decision problems and NP-hard combinatorial optimization problems. However, some systematic search methods, such as modern variants of the Davis–Putnam algorithm for propositional satisfiability (SAT), also utilize non-deterministic decisions, and can thus also be considered Las Vegas algorithms.