On the Functions Generated by the General Purpose Analog Computer

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 $f: \mathbb{R} \to \mathbb{R}$) but also to the multivariate case of (i.e. functions $f: \mathbb{R}^n \to \mathbb{R}^m$), 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 stability properties of this class, mostly stability by arithmetic operations, composition and ODE solving. We establish that generable functions are always analytic. We prove that generable functions include some basic (useful) generable functions, and that we can (uniformly) approximate a wide range of functions this way. This extends some of the results from \cite{Sha41} to the multidimensional case, and this also strengths the approximation result from \cite{Sha41} over a compact domain to a uniform approximation result over unbounded domains. We also discuss the issue of constants, and we prove that involved constants can basically assumed to always be polynomial time computable numbers.

[1]  Diogo Poças,et al.  Analog networks on function data streams , 2018, Comput..

[2]  Amaury Pouly,et al.  On the complexity of solving initial value problems , 2012, ISSAC.

[3]  Grégory Lafitte,et al.  Continuous models of computation : from computability to complexity , 2016 .

[4]  Ker-I Ko,et al.  Complexity Theory of Real Functions , 1991, Progress in Theoretical Computer Science.

[5]  Amaury Pouly,et al.  Computing with Polynomial Ordinary Differential Equations , 2016, J. Complex..

[6]  Daniel S. Graça,et al.  Computability with polynomial differential equations , 2008, Adv. Appl. Math..

[7]  Vannevar Bush,et al.  The differential analyzer. A new machine for solving differential equations , 1931 .

[8]  Jaime Muñoz Masqué,et al.  A short proof of the generalized Faàdi Bruno's formula , 2003, Appl. Math. Lett..

[9]  Daniel Silva Graça,et al.  Some recent developments on Shannon's General Purpose Analog Computer , 2004, Math. Log. Q..

[10]  Tsoy-Wo Ma Higher Chain Formula proved by Combinatorics , 2009, Electron. J. Comb..

[11]  M. B. Pour-El,et al.  Abstract computability and its relation to the general purpose analog computer (some connections between logic, differential equations and analog computers) , 1974 .

[12]  Olivier Bournez,et al.  Polynomial differential equations compute all real computable functions on computable compact intervals , 2007, J. Complex..

[13]  N. G. Parke,et al.  Ordinary Differential Equations. , 1958 .

[14]  Marian Boykan Pour-El,et al.  Computability in analysis and physics , 1989, Perspectives in Mathematical Logic.

[15]  Claude E. Shannon,et al.  Mathematical Theory of the Differential Analyzer , 1941 .

[16]  A. Tarski A LATTICE-THEORETICAL FIXPOINT THEOREM AND ITS APPLICATIONS , 1955 .

[17]  Harold R. Parks,et al.  A Primer of Real Analytic Functions , 1992 .

[18]  Andrea Sorbi,et al.  New Computational Paradigms: Changing Conceptions of What is Computable , 2007 .

[19]  Stefan Friedrich,et al.  Topology , 2019, Arch. Formal Proofs.

[20]  José Félix Costa,et al.  Analog computers and recursive functions over the reals , 2003, J. Complex..

[21]  V. Arnold,et al.  Ordinary Differential Equations , 1973 .