On the Bivariate Vector-Valued Rational Interpolation and Recovery Problems

This paper investigates bivariate vector-valued rational interpolation and recovery problems with common divisors in their denominators. By leveraging these shared divisors and employing a component-wise rational interpolation approach, we reduce the degree of the interpolation problem. Furthermore, a new algorithm for recovering bivariate vector-valued rational functions is proposed. Experimental results demonstrate that, compared to classical algorithms, our method achieves faster computation speed without compromising accuracy. This advantage is particularly evident in the recovery of bivariate vector-valued rational functions.

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Publication Details

Journal
Axioms
Published
2025-04-29
DOI
https://doi.org/10.3390/axioms14050341
Primary Topic
Advanced Optimization Algorithms Research
Type
article
Field-Weighted Citation Impact
0.00

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article

On the Bivariate Vector-Valued Rational Interpolation and Recovery Problems

Peng Xia, Shugong Zhang, Lixia Xiao
Axioms
Advanced Optimization Algorithms Research
article

On the Bivariate Vector-Valued Rational Interpolation and Recovery Problems

Peng Xia, Shugong Zhang, Lixia Xiao
article en

Abstract

This paper investigates bivariate vector-valued rational interpolation and recovery problems with common divisors in their denominators. By leveraging these shared divisors and employing a component-wise rational interpolation approach, we reduce the degree of the interpolation problem. Furthermore, a new algorithm for recovering bivariate vector-valued rational functions is proposed. Experimental results demonstrate that, compared to classical algorithms, our method achieves faster computation speed without compromising accuracy. This advantage is particularly evident in the recovery of bivariate vector-valued rational functions.

AxiomsVol. 14(5)
Liaoning University (CN), Minzu University of China (CN), Jilin University (CN)
Natural Science Foundation of Inner Mongolia, Department of Education of Liaoning Province, Scientific Research Fund of Liaoning Provincial Education Department, Minzu University of China
Openalex Percentile: Top 86%
Advanced Optimization Algorithms Research
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