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Generic finiteness of solutions to parametrized systems of generalized polynomial inequalities

Stefan Müller

Source record

Source: arXiv

Published: Oct 20, 2026

arXiv: 2610.28588

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Source abstract

We study parametrized systems of generalized\mathit{generalized} polynomial inequalities, (c∘xB)∈C(c \circ x^B) \in C, for a positive variable vector x∈R>nx \in \mathbb{R}^n_>, a real\mathit{real} exponent matrix B∈Rn×mB \in \mathbb{R}^{n \times m}, a positive parameter vector c∈R>mc \in \mathbb{R}^m_>, and a positive, blunt coefficient cone C⊂R>mC \subset \mathbb{R}^m_>. This setting includes (i) generalized polynomial equations, A (c∘xB)=0A \, (c \circ x^B) = 0, arising e.g. from power-law dynamical systems with real exponents (and involving a real coefficient matrix A∈Rn′×mA \in \mathbb{R}^{n' \times m}), and (ii) generalized semi-algebraic sets. Our main result guarantees the generic finiteness of solutions modulo unavoidable exponential fibers. Building on a framework for parametrized polynomial\mathit{polynomial} systems developed by Müller and Regensburger, we reformulate the problem as a system of binomial\mathit{binomial} equations on the coefficient set P=C∩Δ⊆R>mP = C \cap Δ\subseteq \mathbb{R}^m_>, where ΔΔ is a product of (standard) simplices. Solvability is governed by the monomial-dependency subspace DD. Using elementary arguments from analysis and topology, including a logarithmic boundary analysis and Sard's theorem, we control the accumulation of solutions on the boundary and in the interior of PP. We show that, if dim⁡P≤dim⁡D\dim P \le \dim D, then generically in both the measure-theoretic and topological sense, there are finitely many solutions on PP (and hence finitely many exponential fibers of solutions in R>n\mathbb{R}^n_>). As an application, we prove generic finiteness of steady states (within forward-invariant sets) for parametrized power-law dynamical systems with real exponents.

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Generic finiteness of solutions to parametrized systems of generalized polynomial inequalities — Mathematical Frontier Network