Zeroes and Extrema of Functions via Random Measures
Athanasios Christou Micheas
TL;DR
The paper introduces a differentiation-free framework (PPZ) to locate all zeroes and extrema of real or complex functions within a bounded window by modeling the zero set as a realization of a Poisson-type random measure. It defines a tractable intensity function that concentrates on zeros and presents a thinning-based algorithm to sample the zero set, with an adaptive variant for refining tolerance. The approach yields a global alternative to iterative optimization, demonstrated on simple real functions, complex polynomials, the Riemann zeta function, and high-dimensional multivariate cases. It also discusses extensions to gradient zeros, vector-valued targets, and potential Cox-process generalizations for richer dependence structures, underscoring practical applicability and avenues for Bayesian automation.
Abstract
We present methods that provide all zeroes and extrema of a function that do not require differentiation. Using point process theory, we are able to describe the locations of zeroes or maxima, their number, as well as their distribution over a given window of observation. The algorithms in order to accomplish the theoretical development are also provided, and they are exemplified using many illustrative examples, for real and complex functions.
