What question did this study set out to answer?

The aim is to enhance multivariate rational approximations using the p-AAA algorithm with low-rank tensors.

Multivariate rational approximation via low-rank tensors and the p-AAA algorithm

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Randomized trial demonstrates improved computational efficiency in multivariate approximations, indicating potential for large-scale applications.

The aim is to enhance multivariate rational approximations using the p-AAA algorithm with low-rank tensors.
Introduced barycentric forms based on separable functions.
Developed the low-rank p-AAA algorithm utilizing low-rank tensor decompositions.
Evaluated the framework's performance through four numerical examples in parametric modeling.
The low-rank p-AAA algorithm effectively reduced computational demands in dealing with high-dimensional data.
Showcased notable performance improvements compared to standard multivariate methods.

Balicki et al. (2026) studied this question.

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View Full Paper

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Authors

Randomized trial demonstrates improved computational efficiency in multivariate approximations, indicating potential for large-scale applications.

The aim is to enhance multivariate rational approximations using the p-AAA algorithm with low-rank tensors.
Introduced barycentric forms based on separable functions.
Developed the low-rank p-AAA algorithm utilizing low-rank tensor decompositions.
Evaluated the framework's performance through four numerical examples in parametric modeling.
The low-rank p-AAA algorithm effectively reduced computational demands in dealing with high-dimensional data.
Showcased notable performance improvements compared to standard multivariate methods.

Balicki et al. (2026) studied this question.

View Full Paper

Ask AI

Bookmark