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math.NA2024
Randomly pivoted Cholesky: Practical approximation of a kernel matrix with few entry evaluations
Yifan Chen, Ethan N. Epperly, Joel A. Tropp +1
The randomly pivoted partial Cholesky algorithm (RPCholesky) computes a factorized rank-k approximation of an N x N positive-semidefinite (psd) matrix. RPCholesky requires only (k…
math.NA2024
Efficient error and variance estimation for randomized matrix computations
Ethan N. Epperly, Joel A. Tropp
Randomized matrix algorithms have become workhorse tools in scientific computing and machine learning. To use these algorithms safely in applications, they should be coupled with p…
math.NA2024
Fast and forward stable randomized algorithms for linear least-squares problems
Ethan N. Epperly
Iterative sketching and sketch-and-precondition are randomized algorithms used for solving overdetermined linear least-squares problems. When implemented in exact arithmetic, these…