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  2. Randomized algorithm - Wikipedia

    en.wikipedia.org/wiki/Randomized_algorithm

    A randomized algorithm is an algorithm that employs a degree of randomness as part of its logic or procedure. The algorithm typically uses uniformly random bits as an auxiliary input to guide its behavior, in the hope of achieving good performance in the "average case" over all possible choices of random determined by the random bits; thus either the running time, or the output (or both) are ...

  3. Monte Carlo algorithm - Wikipedia

    en.wikipedia.org/wiki/Monte_carlo_algorithm

    In computing, a Monte Carlo algorithm is a randomized algorithm whose output may be incorrect with a certain (typically small) probability. Two examples of such algorithms are the Karger–Stein algorithm [ 1 ] and the Monte Carlo algorithm for minimum feedback arc set .

  4. Category:Randomized algorithms - Wikipedia

    en.wikipedia.org/wiki/Category:Randomized_algorithms

    Main page; Contents; Current events; Random article; About Wikipedia; Contact us

  5. BPP (complexity) - Wikipedia

    en.wikipedia.org/wiki/BPP_(complexity)

    A Monte Carlo algorithm is a randomized algorithm which is likely to be correct. Problems in the class BPP have Monte Carlo algorithms with polynomial bounded running time. This is compared to a Las Vegas algorithm which is a randomized algorithm which either outputs the correct answer, or outputs "fail" with low probability.

  6. Multiplicative weight update method - Wikipedia

    en.wikipedia.org/wiki/Multiplicative_Weight...

    Then, there might be a tie. Following the weight update rule in weighted majority algorithm, the predictions made by the algorithm would be randomized. The algorithm calculates the probabilities of experts predicting positive or negatives, and then makes a random decision based on the computed fraction: [further explanation needed] predict

  7. Random optimization - Wikipedia

    en.wikipedia.org/wiki/Random_optimization

    Random optimization (RO) is a family of numerical optimization methods that do not require the gradient of the optimization problem and RO can hence be used on functions that are not continuous or differentiable. Such optimization methods are also known as direct-search, derivative-free, or black-box methods.

  8. Probabilistic analysis of algorithms - Wikipedia

    en.wikipedia.org/wiki/Probabilistic_analysis_of...

    In analysis of algorithms, probabilistic analysis of algorithms is an approach to estimate the computational complexity of an algorithm or a computational problem. It starts from an assumption about a probabilistic distribution of the set of all possible inputs.

  9. Yao's principle - Wikipedia

    en.wikipedia.org/wiki/Yao's_principle

    Any randomized algorithm may be interpreted as a randomized choice among deterministic algorithms, and thus as a mixed strategy for Alice. Similarly, a non-random algorithm may be thought of as a pure strategy for Alice. In any two-player zero-sum game, if one player chooses a mixed strategy, then the other player has an optimal pure strategy ...

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