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Monte Carlo algorithm
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== Classes of Monte Carlo and Las Vegas algorithms == Randomized algorithms are primarily divided by its two main types, Monte Carlo and Las Vegas, however, these represent only a top of the hierarchy and can be further categorized.<ref name=":0" /> * Las Vegas ** Sherwood—"performant and effective special case of Las Vegas" ** [[Numerical algorithm|Numerical]]—"numerical Las Vegas" * Monte Carlo ** [[Atlantic City algorithm|Atlantic City]]—"bounded error special case of Monte Carlo" ** Numerical—"numerical approximation Monte Carlo" "Both Las Vegas and Monte Carlo are dealing with decisions, i.e., problems in their [[Decision problem|decision version]]."<ref name=":0" /> "This however should not give a wrong impression and confine these algorithms to such problems—both types of [[randomized algorithm]]s can be used on numerical problems as well, problems where the output is not simple ‘yes’/‘no’, but where one needs to receive a result that is numerical in nature."<ref name=":0" /> {| class="wikitable" |+Comparison of Las Vegas and Monte Carlo algorithms ! !Efficiency !Optimum !Failure (LV) / Error (MC) |- |Las Vegas (LV) |Probabilistic |Certain |<math><\tfrac{1}{2}</math> |- |Sherwood |Certain, or Sherwood probabilistic (stronger bound than regular LV) |Certain |0 |- |Numerical |Probabilistic, certain, or Sherwood probabilistic |Certain |<math><\tfrac{1}{2}</math>or 0 |- |Monte Carlo (MC) |Certain |Probabilistic |<math><1</math>(probability which through repeated runs grows sub-exponentially will inhibit usefulness of the algorithm; typical case is <math><\tfrac{1}{2}</math>) |- |Atlantic City |Certain |Probabilistic |<math><\tfrac{1}{4}</math> |- |Numerical |Certain |Probabilistic |<math><1</math>(algorithm type dependent) |} Previous table represents a general framework for Monte Carlo and Las Vegas randomized algorithms.<ref name=":0" /> Instead of the mathematical symbol <math><</math> one could use <math>\leq</math>, thus making probabilities in the worst case equal.<ref name=":0" />
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