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        <datestamp>2024-03-06T11:06:46Z</datestamp>
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          <dc:title>On Probabilistic Time versus Alternating Time</dc:title>
          <dc:creator>Viola, Emanuele</dc:creator>
          <dc:subject>Probabilistic time</dc:subject>
          <dc:subject>alternating time</dc:subject>
          <dc:subject>polynomial-time hierarchy</dc:subject>
          <dc:subject>approximate majority</dc:subject>
          <dc:subject>constant-depth circuit</dc:subject>
          <dc:description>Sipser and GÃƒÂ¡cs, and independently Lautemann, proved in '83 that probabilistic polynomial time is contained in the second level of the polynomial-time hierarchy, i.e. BPP is in Sigma_2 P. This is essentially the only non-trivial upper bound that we have on the power of probabilistic computation. More precisely, the Sipser-GÃƒÂ¡cs-Lautemann simulation shows that probabilistic time can be simulated deterministically, using two quantifiers, **with a quadratic blow-up in the running time**. That is, BPTime(t) is contained in Sigma_2 Time(t^2).&#13;
&#13;
In this talk we discuss whether this quadratic blow-up in the running time is necessary. We show that the quadratic blow-up is indeed necessary for black-box simulations that use two quantifiers, such as those of Sipser, GÃƒÂ¡cs, and Lautemann. To obtain this result, we prove a new circuit lower bound for computing **approximate majority**, i.e. computing the majority of a given bit-string whose fraction of 1's is bounded away from 1/2 (by a constant): We show that small depth-3 circuits for approximate majority must have bottom fan-in Omega(log n).&#13;
&#13;
On the positive side, we obtain that probabilistic time can be simulated deterministically, using three quantifiers, in quasilinear time. That is, BPTime(t) is contained in Sigma_3 Time(t polylog t). Along the way, we show that approximate majority can be computed by uniform polynomial-size depth-3 circuits. This is a uniform version of a striking result by Ajtai that gives *non-uniform* polynomial-size depth-3 circuits for approximate majority.&#13;
&#13;
If time permits, we will discuss some applications of our results to proving lower bounds on randomized Turing machines.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Emanuele Viola</dc:contributor>
          <dc:date>2006</dc:date>
          <dc:relation>Is Part Of Dagstuhl Seminar Proceedings, Volume 6111, Complexity of Boolean Functions (2006)</dc:relation>
          <dc:type>InProceedings</dc:type>
          <dc:type>Text</dc:type>
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          <dc:identifier>doi:10.4230/DagSemProc.06111.11</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-6194</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.06111.11</dc:identifier>
          <dc:language>eng</dc:language>
          <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
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