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        <identifier>oai:drops-oai.dagstuhl.de:18082</identifier>
        <datestamp>2024-03-06T11:00:58Z</datestamp>
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          <dc:title>Lower Bounds for Pseudo-Deterministic Counting in a Stream</dc:title>
          <dc:creator>Braverman, Vladimir</dc:creator>
          <dc:creator>Krauthgamer, Robert</dc:creator>
          <dc:creator>Krishnan, Aditya</dc:creator>
          <dc:creator>Sapir, Shay</dc:creator>
          <dc:subject>streaming algorithms</dc:subject>
          <dc:subject>pseudo-deterministic</dc:subject>
          <dc:subject>approximate counting</dc:subject>
          <dc:description>Many streaming algorithms provide only a high-probability relative approximation. These two relaxations, of allowing approximation and randomization, seem necessary - for many streaming problems, both relaxations must be employed simultaneously, to avoid an exponentially larger (and often trivial) space complexity. A common drawback of these randomized approximate algorithms is that independent executions on the same input have different outputs, that depend on their random coins. Pseudo-deterministic algorithms combat this issue, and for every input, they output with high probability the same "canonical" solution.&#13;
We consider perhaps the most basic problem in data streams, of counting the number of items in a stream of length at most n. Morris’s counter [CACM, 1978] is a randomized approximation algorithm for this problem that uses O(log log n) bits of space, for every fixed approximation factor (greater than 1). Goldwasser, Grossman, Mohanty and Woodruff [ITCS 2020] asked whether pseudo-deterministic approximation algorithms can match this space complexity. Our main result answers their question negatively, and shows that such algorithms must use Ω(√{log n / log log n}) bits of space.&#13;
Our approach is based on a problem that we call Shift Finding, and may be of independent interest. In this problem, one has query access to a shifted version of a known string F ∈ {0,1}^{3n}, which is guaranteed to start with n zeros and end with n ones, and the goal is to find the unknown shift using a small number of queries. We provide for this problem an algorithm that uses O(√n) queries. It remains open whether poly(log n) queries suffice; if true, then our techniques immediately imply a nearly-tight Ω(log n/log log n) space bound for pseudo-deterministic approximate counting.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Vladimir Braverman and Robert Krauthgamer and Aditya Krishnan and Shay Sapir</dc:contributor>
          <dc:date>2023</dc:date>
          <dc:relation>Is Part Of LIPIcs, Volume 261, 50th International Colloquium on Automata, Languages, and Programming (ICALP 2023)</dc:relation>
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          <dc:identifier>doi:10.4230/LIPIcs.ICALP.2023.30</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-180827</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2023.30</dc:identifier>
          <dc:language>eng</dc:language>
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