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On Algebras of Algorithms and Specifications over Uninterpreted Data Cover

On Algebras of Algorithms and Specifications over Uninterpreted Data

Open Access
|Dec 2018

Abstract

This paper continues formalization in Mizar [2, 1] of basic notions of the composition-nominative approach to program semantics [13] which was started in [8, 11].

The composition-nominative approach studies mathematical models of computer programs and data on various levels of abstraction and generality and provides tools for reasoning about their properties. Besides formalization of semantics of programs, certain elements of the composition-nominative approach were applied to abstract systems in a mathematical systems theory [4, 6, 7, 5, 3].

In the paper we introduce a definition of the notion of a binominative function over a set D understood as a partial function which maps elements of D to D. The sets of binominative functions and nominative predicates [11] over given sets form the carrier of the generalized Glushkov algorithmic algebra [14]. This algebra can be used to formalize algorithms which operate on various data structures (such as multidimensional arrays, lists, etc.) and reason about their properties.

We formalize the operations of this algebra (also called compositions) which are valid over uninterpretated data and which include among others the sequential composition, the prediction composition, the branching composition, the monotone Floyd-Hoare composition, and the cycle composition. The details on formalization of nominative data and the operations of the algorithmic algebra over them are described in [10, 12, 9].

DOI: https://doi.org/10.2478/forma-2018-0011 | Journal eISSN: 1898-9934 | Journal ISSN: 1426-2630
Language: English
Page range: 141 - 147
Accepted on: Jun 29, 2018
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Published on: Dec 24, 2018
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2018 Ievgen Ivanov, Artur Korniłowicz, Mykola Nikitchenko, published by University of Białystok
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.