A new class of low-rank short recurrences is proposed for nonsymmetric linear matrix equations, combining subspace projection with truncation and randomization to limit memory while accelerating convergence.
GAMM-Mitt
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
math.NA 2verdicts
UNVERDICTED 2representative citing papers
New dimension and model reduction techniques for linear Bayesian inverse problems with rank-deficient priors, with approximation guarantees and efficiency demonstrations for high-dimensional inference.
citing papers explorer
-
A class of low-rank short recurrences for nonsymmetric linear matrix equations
A new class of low-rank short recurrences is proposed for nonsymmetric linear matrix equations, combining subspace projection with truncation and randomization to limit memory while accelerating convergence.
-
Dimension and model reduction approaches for linear Bayesian inverse problems with rank-deficient prior covariances
New dimension and model reduction techniques for linear Bayesian inverse problems with rank-deficient priors, with approximation guarantees and efficiency demonstrations for high-dimensional inference.