Noise sensitivity from fractional query algorithms and the axis-aligned Laplacian

Renan Gross

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arXiv · 2201.10350

Noise sensitivity from fractional query algorithms and the axis-aligned Laplacian

Renan Gross

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Abstract

We introduce the notion of classical fractional query algorithms, which generalize decision trees in the average-case setting, and can potentially perform better than them. We show that the limiting run-time complexity of a natural class of these algorithms obeys the non-linear partial differential equation $\min_{k}\partial^{2}u/\partial x_{k}^{2}=-2$, and that the individual bit revealment satisfies the Schramm-Steif bound for Fourier weight, connecting noise sensitivity with PDEs. We discuss relations with other decision tree results.

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Renan Gross. 2022-01-25. Noise sensitivity from fractional query algorithms and the axis-aligned Laplacian. https://arxiv.org/abs/2201.10350

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