31 research outputs found
Computable randomness is about more than probabilities
We introduce a notion of computable randomness for infinite sequences that
generalises the classical version in two important ways. First, our definition
of computable randomness is associated with imprecise probability models, in
the sense that we consider lower expectations (or sets of probabilities)
instead of classical 'precise' probabilities. Secondly, instead of binary
sequences, we consider sequences whose elements take values in some finite
sample space. Interestingly, we find that every sequence is computably random
with respect to at least one lower expectation, and that lower expectations
that are more informative have fewer computably random sequences. This leads to
the intriguing question whether every sequence is computably random with
respect to a unique most informative lower expectation. We study this question
in some detail and provide a partial answer
Differential calculus with imprecise input and its logical framework
We develop a domain-theoretic Differential Calculus for locally Lipschitz functions on finite dimensional real spaces with imprecise input/output. The inputs to these functions are hyper-rectangles and the outputs are compact real intervals. This extends the domain of application of Interval Analysis and exact arithmetic to the derivative. A new notion of a tie for these functions is introduced, which in one dimension represents a modification of the notion previously used in the one-dimensional framework. A Scott continuous sub-differential for these functions is then constructed, which satisfies a weaker form of calculus compared to that of the Clarke sub-gradient. We then adopt a Program Logic viewpoint using the equivalence of the category of stably locally compact spaces with that of semi-strong proximity lattices. We show that given a localic approximable mapping representing a locally Lipschitz map with imprecise input/output, a localic approximable mapping for its sub-differential can be constructed, which provides a logical formulation of the sub-differential operator
A Survey on Continuous Time Computations
We provide an overview of theories of continuous time computation. These
theories allow us to understand both the hardness of questions related to
continuous time dynamical systems and the computational power of continuous
time analog models. We survey the existing models, summarizing results, and
point to relevant references in the literature
Computability and dynamical systems
In this paper we explore results that establish a link between dynamical
systems and computability theory (not numerical analysis). In the last few decades,
computers have increasingly been used as simulation tools for gaining insight into
dynamical behavior. However, due to the presence of errors inherent in such numerical
simulations, with few exceptions, computers have not been used for the
nobler task of proving mathematical results. Nevertheless, there have been some recent
developments in the latter direction. Here we introduce some of the ideas and
techniques used so far, and suggest some lines of research for further work on this
fascinating topic
Computability of ordinary differential equations
In this paper we provide a brief review of several results about the
computability of initial-value problems (IVPs) defined with ordinary differential
equations (ODEs). We will consider a variety of settings and analyze
how the computability of the IVP will be affected. Computational
complexity results will also be presented, as well as computable versions
of some classical theorems about the asymptotic behavior of ODEs.info:eu-repo/semantics/publishedVersio
Computable randomness is about more than probabilities
We introduce a notion of computable randomness for infinite sequences that generalises the classical version in two important ways. First, our definition of computable randomness is associated with imprecise probability models, in the sense that we consider lower expectations (or sets of probabilities) instead of classical 'precise' probabilities. Secondly, instead of binary sequences, we consider sequences whose elements take values in some finite sample space. Interestingly, we find that every sequence is computably random with respect to at least one lower expectation, and that lower expectations that are more informative have fewer computably random sequences. This leads to the intriguing question whether every sequence is computably random with respect to a unique most informative lower expectation. We study this question in some detail and provide a partial answer
Upper and lower bounds on continuous-time computation
Abstract. We consider various extensions and modifications of Shannon’s General Purpose Analog Computer, which is a model of computation by differential equations in continuous time. We show that several classical computation classes have natural analog counterparts, including the primitive recursive functions, the elementary functions, the levels of the Grzegorczyk hierarchy, and the arithmetical and analytical hierarchies. Key words: Continuous-time computation, differential equations, recursion theory, dynamical systems, elementary functions, Grzegorczyk hierarchy, primitive recursive functions, computable functions, arithmetical and analytical hierarchies.