Information-Theoretic Bounds for Integral Estimation
This work addresses the fundamental challenge of integral estimation in high-dimensional settings for computational mathematics and statistics, though it is incremental as it builds on existing methods and bounds.
The paper tackles the problem of estimating definite integrals using noisy function queries, establishing an information-theoretic lower bound of Ω(2^d r^{d+1}√(d/T)) and showing that Gaussian Quadrature achieves O(2^d r^d/√T) for certain functions, but is not minimax optimal for others.
In this paper, we consider a zero-order stochastic oracle model of estimating definite integrals. In this model, integral estimation methods may query an oracle function for a fixed number of noisy values of the integrand function and use these values to produce an estimate of the integral. We first show that the information-theoretic error lower bound for estimating the integral of a $d$-dimensional function over a region with $l_\infty$ radius $r$ using at most $T$ queries to the oracle function is $Ω(2^d r^{d+1}\sqrt{d/T})$. Additionally, we find that the Gaussian Quadrature method under the same model achieves a rate of $O(2^{d}r^d/\sqrt{T})$ for functions with zero fourth and higher-order derivatives with respect to individual dimensions, and for Gaussian oracles, this rate is tight. For functions with nonzero fourth derivatives, the Gaussian Quadrature method achieves an upper bound which is not tight with the information-theoretic lower bound. Therefore, it is not minimax optimal, so there is space for the development of better integral estimation methods for such functions.