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Optimal speedup of Las Vegas algorithms. (English) Zbl 0797.68139
Summary: Let \(A\) be a Las Vegas algorithm, i.e., \(A\) is a randomized algorithm that always produces the correct answer when it stops but whose running time is a random variable. We consider the problem of minimizing the expected time required to obtain an answer from \(A\) using strategies which simulate \(A\) as follows: run \(A\) for a fixed amount of time \(t_ 1\), then run \(A\) independently for a fixed amount of time \(t_ 2\), etc. The simulation stops if \(A\) completes its execution during any of the runs. Let \({\mathcal S}= (t_ 1,t_ 2,\dots)\) be a strategy, and let \(\ell_ A= \inf_{\mathcal S} T(A,{\mathcal S})\), where \(T(A,{\mathcal S})\) is the expected value of the running time of the simulation of \(A\) under strategy \({\mathcal S}\).
We describe a simple universal strategy \({\mathcal S}^{\text{univ}}\), with the property that, for any algorithm \(A\), \(T(A,{\mathcal S}^{\text{univ}})= O(\ell_ A\log (\ell_ A))\). Furthermore, we show that this is the best performance that can be achieved, up to a constant factor, by any universal strategy.

MSC:
68T15 Theorem proving (deduction, resolution, etc.) (MSC2010)
68Q25 Analysis of algorithms and problem complexity
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[1] Alt, H.; Guibas, L.; Mehlhorn, K.; Karp, R.; Wigderson, A., A method for obtaining randomized algorithms with small tail probabilities, Tech. rept. TR-91-057, (1991), International Computer Science Institute Berkeley · Zbl 0857.68057
[2] Ertel, W., OR-parallel theorem proving with random competition, (), 226-237, Lecture Notes in Artificial Intelligence
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