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Relative discrepancy does not separate information and communication complexity

par Fontes, Lila;Jain, Rahul;Kerenidis, Iordanis;Laplante, Sophie;Laurière, Mathieu;Roland, Jérémie
Référence ACM Transactions on Computational Theory, 9, 1, 4
Publication Publié, 2016-12

Article révisé par les pairs

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Résumé :

Does the information complexity of a function equal its communication complexity? We examine whether any currently known techniques might be used to show a separation between the two notions. Ganor et al. [2014] recently provided such a separation in the distributional case for a specific input distribution. We show that in the non-distributional setting, the relative discrepancy bound is smaller than the information complexity; hence, it cannot separate information and communication complexity. In addition, in the distributional case, we provide a linear program formulation for relative discrepancy and relate it to variants of the partition bound, resolving also an open question regarding the relation of the partition bound and information complexity. Last, we prove the equivalence between the adaptive relative discrepancy and the public-coin partition, implying that the logarithm of the adaptive relative discrepancy bound is quadratically tight with respect to communication.