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- W2115991326 abstract "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 $(frac{1}{2}+o(1)){n choose 2} $ upper bound and a nearly matching $0.32{n choose 2}$ 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)." @default.
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- W2115991326 date "2004-01-01" @default.
- W2115991326 modified "2023-09-30" @default.
- W2115991326 title "Learning a Hidden Matching" @default.
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- W2115991326 doi "https://doi.org/10.1137/s0097539702420139" @default.
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