Publications

Here are a list of my published articles, technical reports, and preprints in chronological order.

Published or accepted

  1. T. Chen, E. N. Epperly, R. A. Meyer, C. Musco, & A. Rao (2026). Does block size matter in randomized block Krylov low-rank approximation?. Proceedings of the 2026 Annual ACM-SIAM Symposium on Discrete Algorithms, 1026–1046. (preprint)
  2. C. Camaño, E. N. Epperly, & J. A. Tropp (2026). Successive Randomized Compression: A Randomized Algorithm for the Compressed MPO-MPS Product. Quantum 10, 2022. (preprint)
  3. M. Dereziński, E. N. Epperly, & R. A. Meyer (2026). The Matrix-Vector Complexity of Ax=b. Proceedings of the 2026 Conference on Learning Theory, accepted. (preprint)
  4. E. N. Epperly, A. Greenbaum, & Y. Nakatsukasa (2026). Stable Algorithms for General Linear Systems by Preconditioning the Normal Equations. Numerische Mathematik. (preprint)
  5. E. N. Epperly, G. Goldshlager, & R. J. Webber (2026). Randomized Kaczmarz with Tail Averaging. Applied and Computational Harmonic Analysis 80, 101812. (preprint)
  6. E. N. Epperly, M. Meier, & Y. Nakatsukasa (2026). Fast Randomized Least-Squares Solvers Can Be Just as Accurate and Stable as Classical Direct Solvers. Communications on Pure and Applied Mathematics 79, 293–339. (preprint)
  7. E. N. Epperly (2026). Adaptive Randomized Pivoting and Volume Sampling. SIAM Journal on Matrix Analysis and Applications, accepted. (preprint)
  8. Y. Chen, E. N. Epperly, J. A. Tropp, & R. J. Webber (2025). Randomly Pivoted Cholesky: Practical Approximation of a Kernel Matrix with Few Entry Evaluations. Communications on Pure and Applied Mathematics 78, 995–1041. (preprint)
  9. E. N. Epperly, J. A. Tropp, & R. J. Webber (2025). Embrace Rejection: Kernel Matrix Approximation by Accelerated Randomly Pivoted Cholesky. SIAM Journal on Matrix Analysis and Applications, 2527–2557. (preprint)
  10. H. Wilber, E. N. Epperly, & A. H. Barnett (2025). Superfast Direct Inversion of the Nonuniform Discrete Fourier Transform via Hierarchically Semiseparable Least Squares. SIAM Journal on Scientific Computing, A1702-A1732. (preprint)
  11. Z. Ding, E. N. Epperly, L. Lin, & R. Zhang (2024). The ESPRIT Algorithm under High Noise: Optimal Error Scaling and Noisy Super-Resolution. 2024 IEEE 65th Annual Symposium on Foundations of Computer Science (FOCS), 2344–2366. (preprint)
  12. E. N. Epperly (2024). Fast and Forward Stable Randomized Algorithms for Linear Least-Squares Problems. SIAM Journal on Matrix Analysis and Applications, 1782–1804. (preprint)
  13. E. N. Epperly & J. A. Tropp (2024). Efficient Error and Variance Estimation for Randomized Matrix Computations. SIAM Journal on Scientific Computing 46, A508-A528. (preprint)
  14. E. N. Epperly, J. A. Tropp, & R. J. Webber (2024). XTrace: Making the Most of Every Sample in Stochastic Trace Estimation. SIAM Journal on Matrix Analysis and Applications, 1–23. (preprint)
  15. E. Epperly & E. Moreno (2023). Kernel Quadrature with Randomly Pivoted Cholesky. Advances in Neural Information Processing Systems 36, 65850–65868. (preprint)
  16. E. N. Epperly, L. Lin, & Y. Nakatsukasa (2022). A Theory of Quantum Subspace Diagonalization. SIAM Journal on Matrix Analysis and Applications 43, 1263–1290. (preprint)
  17. N. Govindarajan, E. N. Epperly, & L. D. Lathauwer (2022). (L_r,L_r,1)-decompositions, Sparse Component Analysis, and the Blind Separation of Sums of Exponentials. SIAM Journal on Matrix Analysis and Applications 43, 912–938.
  18. E. N. Epperly, N. Govindarajan, & S. Chandrasekaran (2021). Minimal Rank Completions for Overlapping Blocks. Linear Algebra and its Applications 627, 185–198. (preprint)
  19. E. N. Epperly & R. B. Sills (2020). Transient Solute Drag and Strain Aging of Dislocations. Acta Materialia 193, 182–190.
  20. E. N. Epperly & R. B. Sills (2020). Comparison of Continuum and Cross-Core Theories of Dynamic Strain Aging. Journal of the Mechanics and Physics of Solids 141, 103944.

Under review

  1. E. N. Epperly, T. Park, & Y. Nakatsukasa (2026). Fast, High-Accuracy, Randomized Nullspace Computations for Tall Matrices. arXiv preprint. (preprint)
  2. E. N. Epperly & R. J. Webber (2026). Sharp Analysis of Sketched Least Squares and Randomized Low-Rank Approximation. arXiv preprint. (preprint)
  3. C. Camaño, E. N. Epperly, R. A. Meyer, & J. A. Tropp (2025). Faster Linear Algebra Algorithms with Structured Random Matrices. arXiv preprint. (preprint)
  4. M. Díaz, E. N. Epperly, Z. Frangella, J. A. Tropp, & R. J. Webber (2023). Robust, Randomized Preconditioning for Kernel Ridge Regression. arXiv preprint. (preprint)

Reports and other

  1. N. Amsel, Y. Baumann, P. Beckman, P. Bürgisser, C. Camaño, T. Chen, E. Chow, A. Damle, M. Derezinski, M. Embree, E. N. Epperly, R. Falgout, M. Fornace, A. Greenbaum, C. Greif, D. Halikias, Z. Huang, E. Jarlebring, Y. Koutis, D. Kressner, R. Kyng, J. Liesen, J. Lok, R. A. Meyer, Y. Nakatsukasa, K. Pearce, R. Peng, D. Persson, E. Rebrova, R. Schneider, R. Shah, E. Solomonik, N. Srivastava, A. Townsend, R. J. Webber, & J. Williams (2026). Linear Systems and Eigenvalue Problems: Open Questions from a Simons Workshop. arXiv preprint. (preprint)
  2. E. N. Epperly (2025). Make the Most of What You Have: Resource-efficient Randomized Algorithms for Matrix Computations. PhD dissertation, California Institute of Technology.
  3. E. N. Epperly, A. T. Barker, & R. D. Falgout (2020). Smoothers for Matrix-Free Algebraic Multigrid Preconditioning of High-Order Finite Elements. Technical report LLNL-TR-814531, Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States).
  4. S. Chandrasekaran, E. Epperly, & N. Govindarajan (2019). Graph-Induced Rank Structures and Their Representations. arXiv preprint. (preprint)
  5. D. K. Ward, X. Zhou, R. A. Karnesky, R. Kolasinski, M. E. Foster, K. Thurmer, P. Chao, E. N. Epperly, J. A. Zimmerman, B. M. Wong, & R. B. Sills (2015). Understanding H Isotope Adsorption and Absorption of Al-alloys Using Modeling and Experiments (LDRD: #165724). Technical report SAND-2015-8388, Sandia National Lab. (SNL-CA), Livermore, CA (United States).