Learning a hidden matching

Noga Alon, Richard Beigel, Simon Kasif, Steven Rudich, Benny Sudakov

Research output: Contribution to journalConference articlepeer-review

12 Scopus citations


We consider the problem of learning a matching (i.e., a graph in which all vertices have degree 0 or 1) in a model where the only allowed operation is to query whether a set of vertices induces an edge. This is motivated by a problem that arises in molecular biology. In the deterministic nonadaptive setting, we prove a (1/2 + o(1)) (2n) upper bound and a nearly matching 0.32 (2n) lower bound for the minimum possible number of queries. In contrast, if we allow randomness then we obtain (by a randomized, nonadaptive algorithm) a much lower O(n log n) upper bound, which is best possible (even for randomized fully adaptive algorithms).

Original languageEnglish (US)
Pages (from-to)197-206
Number of pages10
JournalAnnual Symposium on Foundations of Computer Science - Proceedings
StatePublished - 2002
Externally publishedYes
EventThe 34rd Annual IEEE Symposium on Foundations of Computer Science - Vancouver, BC, Canada
Duration: Nov 16 2002Nov 19 2002

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture


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