On the Functions Generated by the General Purpose Analog Computer
Olivier Bournez, Daniel Gra\c{c}a, Amaury Pouly

TL;DR
This paper extends the theoretical framework of the General Purpose Analog Computer (GPAC) to multivariate functions, demonstrating that a broad class of functions can be approximated and analyzing their properties.
Contribution
It generalizes the GPAC model to multivariate functions, establishing their approximation capabilities and stability properties, and proving that these functions are always analytic.
Findings
Wide class of functions can be uniformly approximated
Generable functions are always analytic
Constants can be polynomial time computable numbers
Abstract
We consider the General Purpose Analog Computer (GPAC), introduced by Claude Shannon in 1941 as a mathematical model of Differential Analysers, that is to say as a model of continuous-time analog (mechanical, and later one electronic) machines of that time. We extend the model properly to a model of computation not restricted to univariate functions (i.e. functions ) but also to the multivariate case of (i.e. functions ), and establish some basic properties. In particular, we prove that a very wide class of (continuous and discontinuous) functions can be uniformly approximated over their full domain. Technically: we generalize some known results about the GPAC to the multidimensional case: we extend naturally the notion of \emph{generable} function, from the unidimensional to the multidimensional case. We prove a few…
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