Multivariate Interpolation in Python

The Python package minterpy is based on an optimised implementation of the multivariate interpolation algorithm given by M. Hecht et al. [1,2]. It thereby provides software solutions that lift the curse of dimensionality from interpolation tasks. While interpolation occurs as the bottleneck of most computational challenges, minterpy aims to free empirical sciences from their computational limitations.

minterpy is continuously extended and improved by adding further functionality and modules that provide novel digital solutions to a broad field of computational challenges, including but not limited to:

• multivariate interpolation
• non-linear polynomial regression
• numerical integration
• global (black-box) optimization
• surface level-set methods
• non-periodic spectral partial differential equations (PDE) solvers on flat and complex geometries
• machine learning regularization
• data reconstruction
• computational solutions in algebraic geometry

minterpy is an open-source Python package that makes it easily accessible and allows for further development and improvement by the Python community.

Further information:

https://github.com/casus/minterpy

References:

[1] M. Hecht, K. Gonciarz, J. Michelfeit, V. Sivkin and I. F. Sbalzarini (2020). Multivariate interpolation on unisolvent nodes–lifting the curse of dimensionality. arxiv.org/abs/2010.10824

[2] M. Hecht and I. F. Sbalzarini (2018). Fast interpolation and Fourier transform in high-dimensional spaces. In Intelligent Computing. Proc. 2018 IEEE Computing Conf., Vol. 2, volume 857 of Advances in Intelligent Systems and Computing, pages 53–75, London, UK,  Springer Nature