Kac-Rice formulas and the number of solutions of parametrized systems of polynomial equations

TL;DR

This paper develops a Kac-Rice formula for counting solutions of parametrized polynomial systems with random parameters, enabling probabilistic analysis of solution counts in complex systems like chemical networks.

Contribution

It introduces a Kac-Rice based method for estimating the expected number of solutions in parametrized polynomial systems with random parameters, applicable to larger parameter spaces than previous exact methods.

Findings

Successfully applies the formula to complex systems with many parameters.

Enables partitioning of parameter space based on the number of solutions.

Provides a probabilistic tool for analyzing steady states in chemical networks.

Abstract

Kac-Rice formulas express the expected number of elements a fiber of a random field has in terms of a multivariate integral. We consider here parametrized systems of polynomial equations that are linear in enough parameters, and provide a Kac-Rice formula for the expected number of solutions of the system when the parameters follow continuous distributions. Combined with Monte Carlo integration, we apply the formula to partition the parameter region according to the number of solutions or find a region in parameter space where the system has the maximal number of solutions. The motivation stems from the study of steady states of chemical reaction networks and gives new tools for the open problem of identifying the parameter region where the network has at least two positive steady states. We illustrate with numerous examples that our approach successfully handles a larger number of parameters than exact methods.