77 research outputs found
Trading Information Complexity for Error
We consider the standard two-party communication model. The central problem studied in this article is how much can one save in information complexity by allowing a certain error.
* For arbitrary functions, we obtain lower bounds and upper bounds indicating a gain that is of order Omega(h(epsilon)) and O(h(sqrt{epsilon})). Here h denotes the binary entropy function.
* We analyze the case of the two-bit AND function in detail to show that for this function the gain is Theta(h(epsilon)). This answers a question of Braverman et al. [Braverman, STOC 2013].
* We obtain sharp bounds for the set disjointness function of order n. For the case of the distributional error, we introduce a new protocol that achieves a gain of Theta(sqrt{h(epsilon)}) provided that n is sufficiently large. We apply these results to answer another of question of Braverman et al. regarding the randomized communication complexity of the set disjointness function.
* Answering a question of Braverman [Braverman, STOC 2012], we apply our analysis of the set disjointness function to establish a gap between the two different notions of the prior-free information cost. In light of [Braverman, STOC 2012], this implies that amortized randomized communication complexity is not necessarily equal to the amortized distributional communication complexity with respect to the hardest distribution.
As a consequence, we show that the epsilon-error randomized communication complexity of the set disjointness function of order n is n[C_{DISJ} - Theta(h(epsilon))] + o(n), where C_{DISJ} ~ 0.4827$ is the constant found by Braverman et al. [Braverman, STOC 2012]
Lifting query complexity to time-space complexity for two-way finite automata
Time-space tradeoff has been studied in a variety of models, such as Turing
machines, branching programs, and finite automata, etc. While communication
complexity as a technique has been applied to study finite automata, it seems
it has not been used to study time-space tradeoffs of finite automata. We
design a new technique showing that separations of query complexity can be
lifted, via communication complexity, to separations of time-space complexity
of two-way finite automata. As an application, one of our main results exhibits
the first example of a language such that the time-space complexity of
two-way probabilistic finite automata with a bounded error (2PFA) is
, while of exact two-way quantum finite automata with
classical states (2QCFA) is , that is, we demonstrate
for the first time that exact quantum computing has an advantage in time-space
complexity comparing to classical computing
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