7 papers
Sharp analysis of sketched least squares and randomized low-rank approximation
Ethan N. Epperly, Robert J. Webber
Two widely used randomized algorithms are the sketch-and-solve method for least-squares regression and the randomized SVD for low-rank approximation. These algorithms apply a rando…
Linear Systems and Eigenvalue Problems: Open Questions from a Simons Workshop
Noah Amsel, Yves Baumann, Paul Beckman +36
This document presents a series of open questions arising in matrix computations, i.e., the numerical solution of linear algebra problems. It is a result of working groups at the w…
Everything is Vecchia: Unifying low-rank and sparse inverse Cholesky approximations
Eagan Kaminetz, Robert J. Webber
The partial pivoted Cholesky approximation accurately represents matrices that are close to being low-rank. Meanwhile, the Vecchia approximation accurately represents matrices with…
Keep the beat going: Automatic drum transcription with momentum
Alisha L. Foster, Robert J. Webber
How can we process a piece of recorded music to detect and visualize the onset of each instrument? A simple, interpretable approach is based on partially fixed nonnegative matrix f…
Variational Markov chain mixtures with automatic component selection
Christopher E. Miles, Robert J. Webber
Markov state modeling has gained popularity in various scientific fields since it reduces complex time-series data sets into transitions between a few states. Yet common Markov sta…
Randomized Kaczmarz with tail averaging
Ethan N. Epperly, Gil Goldshlager, Robert J. Webber
The randomized Kaczmarz (RK) method is a well-known approach for solving linear least-squares problems with a large number of rows. RK accesses and processes just one row at a time…