Linked from

The 21 pages that link to Randomized algorithm, each with the reason it gives.

• CombinatoricsRelated: Counting estimates and probabilistic existence arguments help analyze its performance.

• RandomnessRelated: Random choices can make algorithms faster or simpler than deterministic alternatives.

• Random number generationRelated: Random choices can simplify algorithms or improve expected performance.

• Approximation algorithmRelated: Randomization can produce approximation guarantees, often in expectation or with high probability.

• Markov's inequalityRelated: Expected running time can bound the probability that a randomized algorithm runs exceptionally long.

• Random graphRelated: Random graph inputs help analyze algorithms on uncertain or typical graph structures.

• Pseudorandom number generatorRelated: Generated values supply the random choices that can improve an algorithm's expected performance.

• Chernoff boundRelated: Tail bounds certify that random choices produce reliable outcomes with high probability.

• HeuristicRelated: Randomness can complement heuristics, though it does not by itself make a method heuristic.

• Coupon collector's problemRelated: Random sampling algorithms can need repeated draws before every required case is encountered.

• Concentration inequalityRelated: Tail bounds certify that random choices produce reliable outputs with high probability.

• AKS primality testRelated: AKS showed that primality can be decided efficiently without relying on randomness.

• Continuous uniform distributionRelated: Uniform draws provide unbiased choices and randomized inputs.

• Azuma's inequalityRelated: Azuma bounds deviations in quantities exposed step by step during randomized computation.