2013
DOI: 10.1016/j.ic.2011.09.008
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Limits on the computational power of random strings

Abstract: How powerful is the set of random strings? What can one say about a set A that is efficiently reducible to R, the set of Kolmogorov-random strings? We present the first upper bound on the class of computable sets in P R and NP R . The two most widely-studied notions of Kolmogorov complexity are the "plain" complexity C (x) and "prefix" complexity K (x); this gives rise to two common ways to define the set of random strings "R": R C and R K . (Of course, each different choice of universal Turing machine U in th… Show more

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Cited by 15 publications
(25 citation statements)
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“…• Then in Section 4 we prove that TTRT ⊆ PSPACE/α(n), which is a non-trivial adaptation of the techniques from [AFG13]. In Section 5 we show how to get an analogous result without the super-constant advice term.…”
Section: Reductions To the Set Of Random Stringsmentioning
confidence: 94%
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“…• Then in Section 4 we prove that TTRT ⊆ PSPACE/α(n), which is a non-trivial adaptation of the techniques from [AFG13]. In Section 5 we show how to get an analogous result without the super-constant advice term.…”
Section: Reductions To the Set Of Random Stringsmentioning
confidence: 94%
“…That is, we succeed in obtaining a P/poly upper bound (of the sort that we were unable to obtain for DTTR in [ADF + 13]), and we "nearly" obtain a PSPACE upper bound (analogous to the PSPACE upper bound that was obtained for DTTR in [AFG13]). We believe that this indicates that TTRT is "closer" to BPP than it is to PSPACE.…”
Section: Reductions To the Set Of Random Stringsmentioning
confidence: 97%
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