Computing with continuous objects: a uniform co-inductive approach

A uniform approach to computing with infinite objects like real numbers, tuples of these, compacts sets and uniformly continuous maps is presented. In the work of Berger, it was shown how to extract certified algorithms working with the signed digit representation from constructive proofs. Berger and the present author generalised this approach to complete metric spaces and showed how to deal with compact sets. Here, we unify this work and lay the foundations for doing a similar thing for the much more comprehensive class of compact Hausdorff spaces occurring in applications. The approach is of the same computational power as Weihrauch’s Type-Two Theory of Effectivity.

DISCRETE APPROXIMATION OF CONTINUOUS OBJECTS WITH MATLAB

This work is dedicated to the study of various discrete approximation methods for continuous links, which is the obligatory step in the digital control systems synthesis for continuous dynamic objects and the guidelines development for performing these opera tions using the MATLAB programming system. The paper investigates such sampling methods as pulse-, step-, and linearly invariant Z-transformations, substitution methods based on the usage of numerical integration various methods and the zero-pole correspond ence method. The paper presents examples of using numerical and symbolic instruments of the MATLAB to perform these opera tions, offers an m-function improved version for continuous systems discretization by the zero-pole correspondence method, which allows this method to approach as step-invariant as linearly invariant Z-transformations; programs for continuous objects discrete approximation in symbolic form have been developed, which allows to perform comparative analysis of sampling methods and sys tems synthesized with their help and to study quantization period influence on sampling accuracy by analytical methods. A compari son of discrete transfer functions obtained by different methods and the corresponding reactions in time to different signals is per formed. Using of the developed programs it is determined that the pulse-invariant Z-transformation can be used only when the input of a continuous object receives pulse signals, and the linear-invariant transformation should be used for intermittent signals at the input. The paper also presents an algorithm for applying the Tustin method, which corresponds to the replacement of analogue inte gration by numerical integration using trapezoidal method. It is shown that the Tustin method is the most suitable for sampling of first-order regulators with output signal limitation. The article also considers the zero-pole correspondence method and shows that it has the highest accuracy among the rough methods of discrete approximation. Based on the performed research, recommendations for the use of these methods in the synthesis of control systems for continuous dynamic objects are given.

Graph fractal dimension and the structure of fractal networks

Fractals are geometric objects that are self-similar at different scales and whose geometric dimensions differ from so-called fractal dimensions. Fractals describe complex continuous structures in nature. Although indications of self-similarity and fractality of complex networks has been previously observed, it is challenging to adapt the machinery from the theory of fractality of continuous objects to discrete objects such as networks. In this article, we identify and study fractal networks using the innate methods of graph theory and combinatorics. We establish analogues of topological (Lebesgue) and fractal (Hausdorff) dimensions for graphs and demonstrate that they are naturally related to known graph-theoretical characteristics: rank dimension and product dimension. Our approach reveals how self-similarity and fractality of a network are defined by a pattern of overlaps between densely connected network communities. It allows us to identify fractal graphs, explore the relations between graph fractality, graph colourings and graph descriptive complexity, and analyse the fractality of several classes of graphs and network models, as well as of a number of real-life networks. We demonstrate the application of our framework in evolutionary biology and virology by analysing networks of viral strains sampled at different stages of evolution inside their hosts. Our methodology revealed gradual self-organization of intra-host viral populations over the course of infection and their adaptation to the host environment. The obtained results lay a foundation for studying fractal properties of complex networks using combinatorial methods and algorithms.