RPCholesky.jl Documentation

Randomly Pivoted Cholesky algorithm, implemented in Julia, based on the description in https://arxiv.org/abs/2207.06503 by Chen et al.

Acknowledgements

This package has been developed in conjunction with R.J. Webber and D. Aristoff.

This work was supported in part by the US National Science Foundation Grant DMS-2111278.

References

• Y. Chen, E. N. Epperly, J. A. Tropp, and R. J. Webber, “Randomly pivoted Cholesky: Practical approximation of a kernel matrix with few entry evaluations.” arXiv, Feb. 22, 2023. doi: 10.48550/arXiv.2207.06503.
• M. Díaz, E. N. Epperly, Z. Frangella, J. A. Tropp, and R. J. Webber, “Robust, randomized preconditioning for kernel ridge regression.” arXiv, Aug. 02, 2023. doi: 10.48550/arXiv.2304.12465.