Alpha-Theory has been introduced in 1995 to provide a simplified version of Robinson’s Non-Standard Analysis which overcomes the technicalities of symbolic logic. The theory has been improved during the years, and recently it has been used also to solve practical problems in a pure numerical way, thanks to the introduction of the algorithmic numbers. In this paper, we introduce Alpha-Theory using a novel axiomatic approach oriented towards real-world applications, to avoid the need to master mathematical logic and model theory. To corroborate the strong link of this Alpha-Theory axiomatization and scientific computations, we report numerical illustrative applications never carried out by means of non-standard numbers within a computer, i.e., the computation of the eigenvalues of a non-Archimedean matrix, some computations related to non-Archimedean Markov Chains, and the Cholesky factorization of a non-Archimedean matrix. We also highlight the differences between our numerical routines and pure symbolic approaches: as expected, the former scales better when the dimension of the problem increases.

### Non-Standard Analysis Revisited: an Easy Axiomatic Presentation Oriented Towards the Applications

#### Abstract

Alpha-Theory has been introduced in 1995 to provide a simplified version of Robinson’s Non-Standard Analysis which overcomes the technicalities of symbolic logic. The theory has been improved during the years, and recently it has been used also to solve practical problems in a pure numerical way, thanks to the introduction of the algorithmic numbers. In this paper, we introduce Alpha-Theory using a novel axiomatic approach oriented towards real-world applications, to avoid the need to master mathematical logic and model theory. To corroborate the strong link of this Alpha-Theory axiomatization and scientific computations, we report numerical illustrative applications never carried out by means of non-standard numbers within a computer, i.e., the computation of the eigenvalues of a non-Archimedean matrix, some computations related to non-Archimedean Markov Chains, and the Cholesky factorization of a non-Archimedean matrix. We also highlight the differences between our numerical routines and pure symbolic approaches: as expected, the former scales better when the dimension of the problem increases.
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2022
Benci, Vieri; Cococcioni, Marco; Fiaschi, Lorenzo
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Utilizza questo identificativo per citare o creare un link a questo documento: `https://hdl.handle.net/11568/1118368`
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