An Optimal Parallel Algorithm for Learning DFA

dc.creatorBalcázar,José
dc.creatorDíaz,Josep
dc.creatorGavaldà,Ricard
dc.creatorWatanabe,Osamu
dc.date1996
dc.date.accessioned2024-02-06T12:48:11Z
dc.date.available2024-02-06T12:48:11Z
dc.descriptionSequential algorithms given by Angluin (1987) and Schapire (1992) learn deterministic finite automata (DFA) exactly from Membership and Equivalence queries. These algorithms are feasible, in the sense that they take time polynomial in n and m, where n is the number of states of the automaton and m is the length of the longest counterexample to an Equivalence query. This paper studies whether parallelism can lead to substantially more efficient algorithms for the problem. We show that no CRCW PRAM machine using a number of processors polynomial in n and m can identify DFA in o(n/log n) time. Furthermore, this lower bound is tight up to constant factors: we develop a CRCW PRAM learning algorithm that uses polynomially many processors and exactly learns DFA in time O(n/log n).
dc.formattext/html
dc.identifierhttps://doi.org/10.3217/jucs-002-03-0097
dc.identifierhttps://lib.jucs.org/article/27223/
dc.identifier.urihttps://openrepository.mephi.ru/handle/123456789/6938
dc.languageen
dc.publisherJournal of Universal Computer Science
dc.relationinfo:eu-repo/semantics/altIdentifier/eissn/0948-6968
dc.relationinfo:eu-repo/semantics/altIdentifier/pissn/0948-695X
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsJ.UCS License
dc.sourceJUCS - Journal of Universal Computer Science 2(3): 97-112
dc.subjectcomputational learning theory
dc.subjectquery learning
dc.subjectmembership query
dc.subjectequivalence query
dc.subjectDFA
dc.subjectoptimal parallel learning
dc.titleAn Optimal Parallel Algorithm for Learning DFA
dc.typeResearch Article
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