Computational Mathematics and Statistics Seminar By Ilse Ipsen: BayesCG: A Probabilistic Numeric Linear Solver




Online seminar

Social Media Links


Professor Ilse Ipsen, Department of Mathematics at North Carolina State University


BayesCG: A probabilistic numeric linear solver


We present the probabilistic numeric solver BayesCG, for solving linear systems with real symmetric positive definite coefficient matrices. BayesCG is an uncertainty aware extension of the conjugate gradient (CG) method that performs solution-based inference with Gaussian distributions to capture the uncertainty in the solution due to early termination. Under a structure exploiting Krylov prior, BayesCG produces the same iterates as CG. The Krylov posterior covariances have low rank, and are maintained in factored form to preserve symmetry and positive semi-definiteness. This allows efficient generation of accurate samples to probe uncertainty in subsequent computations.

Join Seminar


Computational Mathematics and Statistics Seminar


Join Seminar


Getting to Campus