Constructive Approximation of Nonlinear Operators Based on Piecewise Interpolation Technique

Suppose KY and KX are the image and the preimage of a nonlinear operator F:KY→KX. It is supposed that the cardinality of each KY and KX is N and N is large. We provide an approximation to the map F that requires prior information only on a few elements p from KY, where p≪N, but still effectively represents F(KY). It is achieved under Lipschitz continuity assumptions. The device behind the proposed method is based on a special extension of the piecewise linear interpolation technique to the case of sets of stochastic elements. The proposed technique provides a single operator that transforms any element from the arbitrarily large set KY. The operator is determined in terms of pseudo-inverse matrices so that it always exists.

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Publication Details

Journal
Axioms
Published
2026-01-26
DOI
https://doi.org/10.3390/axioms15020091
Primary Topic
Neural Networks and Applications
Type
article
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article

Constructive Approximation of Nonlinear Operators Based on Piecewise Interpolation Technique

Peter Pudney, Anatoli Torokhti
Axioms
Neural Networks and Applications
article

Constructive Approximation of Nonlinear Operators Based on Piecewise Interpolation Technique

Peter Pudney, Anatoli Torokhti
article en

Abstract

Suppose KY and KX are the image and the preimage of a nonlinear operator F:KY→KX. It is supposed that the cardinality of each KY and KX is N and N is large. We provide an approximation to the map F that requires prior information only on a few elements p from KY, where p≪N, but still effectively represents F(KY). It is achieved under Lipschitz continuity assumptions. The device behind the proposed method is based on a special extension of the piecewise linear interpolation technique to the case of sets of stochastic elements. The proposed technique provides a single operator that transforms any element from the arbitrarily large set KY. The operator is determined in terms of pseudo-inverse matrices so that it always exists.

AxiomsVol. 15(2)
University of South Australia (AU)
Reduced inequalities
Openalex Percentile: Top 78%
Neural Networks and Applications
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