3 citations · 6 across the 5 of their papers we have counts for
3 papers · 1 filter
Statistical Properties of the Probabilistic Numeric Linear Solver BayesCG
Tim W. Reid, Ilse C. F. Ipsen, Jon Cockayne +1
We analyse the calibration of BayesCG under the Krylov prior, a probabilistic numeric extension of the Conjugate Gradient (CG) method for solving systems of linear equations with s…
Randomized algorithms for rounding in the Tensor-Train format
Hussam Al Daas, Grey Ballard, Paul Cazeaux +5
The Tensor-Train (TT) format is a highly compact low-rank representation for high-dimensional tensors. TT is particularly useful when representing approximations to the solutions o…
BayesCG As An Uncertainty Aware Version of CG
Tim W. Reid, Ilse C. F. Ipsen, Jon Cockayne +1
The Bayesian Conjugate Gradient method (BayesCG) is a probabilistic generalization of the Conjugate Gradient method (CG) for solving linear systems with real symmetric positive def…