In this paper we provide a framework for computable analysis of measure, probability and integration theories. We work on computable metric spaces with computable Borel probability measures. We introduce and study the framework of layerwise computability which lies on Martin-Lof randomness and the existence of a universal randomness test. We then prove characterizations of effective notions of measurability and integrability in terms of layerwise computability. On the one hand it gives a simple way of handling effective measure theory, on the other hand it provides powerful tools to study Martin-Lof randomness, as illustrated in a sequel paper.

/pdf/an-application-of-martin-lof-randomness-to-effective-3r1y9whyl0.pdf

An Application of Martin-Löf Randomness to Effective Probability Theory

Towards computability of elliptic boundary value problems in variational formulation

Computing Schrödinger propagators on Type-2 Turing machines

The Fourier transform is an important operation in signal processing. However, its exact computation on digital computers can be problematic. In this paper we consider the computability of the Fourier transform and the discrete-time Fourier transform (DTFT). We construct a computable bandlimited absolutely integrable signal that has a continuous Fourier transform, which is, however, not Turing computable. Further, we also construct a computable sequence such that the DTFT is not Turing computable. Turing computability models what is theoretically implementable on a digital computer. Hence, our result shows that the Fourier transform of certain signals cannot be computed on digital hardware of any kind, including CPUs, FPGAs, and DSPs. This also implies that there is no symmetry between the time and frequency domain with respect to computability. Therefore, numerical approaches which employ the frequency domain representation of a signal (like calculating the convolution by performing a multiplication in the frequency domain) can be problematic. Interestingly, an idealized analog machine can compute the Fourier transform. However, it is unclear whether and how this theoretical superiority of the analog machine can be translated into practice. Further, we show that it is not possible to find an algorithm that can always decide for a given signal whether the Fourier transform is computable or not.

Turing Computability of Fourier Transforms of Bandlimited and Discrete Signals

We present ongoing work on the development of complexity theory in analysis. Kawamura and Cook recently showed how to carry out complexity theory on the space C[0,1] of continuous real functions on the unit interval. It is done, as in computable analysis, by representing objects by first-order functions (from finite words to finite words, say) and by measuring the complexity of a second-order functional in terms of second-order polynomials. We prove that this framework cannot be directly applied to spaces that are not $\sigma$-compact. However, representing objects by higher-order functions (over finite words, say) makes it possible to carry out complexity theory on such spaces: for this purpose we develop the complexity of higher-order functionals. At orders above 3, our class of polynomial-time computable functionals strictly contains the class BFF of Buss, Cook and Urquhart.

https://hal.inria.fr/hal-00915973/document

Higher-order complexity in analysis

Using Type-2 theory of effectivity, we define computability notions on the spaces of Lebesgue-integrable functions on the real line that are based on two natural approaches to integrability from measure theory. We show that Fourier transform and convolution on these spaces are computable operators with respect to these representations. By means of the orthonormal basis of Hermite functions in L2, we show the existence of a linear complexity bound for the Fourier transform. (© 2004 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)

Daren Kunkle

Papers

Type-2 computability on spaces of integrable functions

Some Examples of Non-Metrizable Spaces Allowing a Simple Type-2 Complexity Theory