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

Time

-

Locations

Online seminar

Speaker:

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

Title:

BayesCG: A probabilistic numeric linear solver

Abstract:

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 for the efficient generation of accurate samples to probe uncertainty in subsequent computations.

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Computational Mathematics and Statistics Seminar

 

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