matsketch
Randomized Matrix Computations from Few Entries and Products
Implements recent randomized algorithms that answer questions about a large positive-semidefinite matrix while touching only a small part of it. Randomly pivoted Cholesky builds a low-rank approximation from a few rows of a kernel matrix (Chen, Epperly, Tropp and Webber (2025) <doi:10.1002/cpa.22234>), with an accelerated variant based on rejection sampling (Epperly, Tropp and Webber (2025) <doi:10.1137/24m1699048>). The XTrace, XNysTrace and XDiag estimators recover the trace and diagonal of a matrix that is available only through matrix-vector products (Epperly, Tropp and Webber (2024) <doi:10.1137/23m1548323>), alongside the Hutch++ estimator of Meyer, Musco, Musco and Woodruff (2021) <doi:10.1137/1.9781611976496.16>. Randomized Nystrom preconditioning speeds up the conjugate gradient method for regularized linear systems (Frangella, Tropp and Udell (2023) <doi:10.1137/21m1466244>). These pieces are combined to fit restricted maximum likelihood variance-component models on genomic relationship matrices without forming or factorizing the covariance matrix.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.0 |
rolling linux/jammy R-4.5 | matsketch_0.1.0.tar.gz |
228.4 KiB |
0.1.0 |
rolling linux/noble R-4.5 | matsketch_0.1.0.tar.gz |
228.3 KiB |
0.1.0 |
rolling source/ R- | matsketch_0.1.0.tar.gz |
127.5 KiB |
0.1.0 |
latest linux/jammy R-4.5 | matsketch_0.1.0.tar.gz |
228.4 KiB |
0.1.0 |
latest linux/noble R-4.5 | matsketch_0.1.0.tar.gz |
228.3 KiB |
0.1.0 |
latest source/ R- | matsketch_0.1.0.tar.gz |
127.5 KiB |
0.1.0 |
2026-04-23 source/ R- | matsketch_0.1.0.tar.gz |
0 B |