Learning Restart Strategies

  title={Learning Restart Strategies},
  author={Matteo Gagliolo and J{\"u}rgen Schmidhuber},
Restart strategies are commonly used for minimizing the computational cost of randomized algorithms, but require prior knowledge of the run-time distribution in order to be effective. We propose a portfolio of two strategies, one fixed, with a provable bound on performance, the other based on a model of run-time distribution, updated as the two strategies are run on a sequence of problems. Computational resources are allocated probabilistically to the two strategies, based on their performances… CONTINUE READING
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