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Practical Implementation of Krylov Subspace Spectral Methods
Journal of Scientific Computing, 2007Krylov subspace spectral methods have been shown to be high-order accurate in time and more stable than time-stepping methods but also more difficult for an efficient implementation. The author shows how these methods should be used in practical solvers by exploiting the simple structure of differential operators.
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A Krylov subspace method for large estimation problems
1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258), 1999Computing the linear least-squares estimate of a high-dimensional random quantity given noisy data requires solving a large system of linear equations. In many situations, one can solve this system efficiently using the conjugate gradient (CG) algorithm. Computing the estimation error variances is a more intricate task.
Michael K. Schneider, Alan S. Willsky
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On Coarse Grid Correction Methods in Krylov Subspaces
Journal of Mathematical Sciences, 2018The authors consider the numerical solution of large, sparse, and ill-conditioned systems of linear algebraic equations (SLAEs) originated from a boundary value problem with Dirichlet boundary conditions for a convection-diffusion equation based on a finite volume method of second order derived by the second author [J. Math. Sci., New York 224, No.
Gurieva, Y. L., Il'in, V. P.
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The Origin and Development of Krylov Subspace Methods
Computing in Science & Engineering, 2022openaire +1 more source
s-Step Enlarged Krylov Subspace Conjugate Gradient Methods
SIAM Journal of Scientific Computing, 2020Sophie Moufawad
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On the Occurrence of Superlinear Convergence of Exact and Inexact Krylov Subspace Methods
SIAM Review, 2005Valeria Simoncini, Daniel B Szyld
exaly
Krylov subspace methods for large-scale matrix problems in control
Future Generation Computer Systems, 2003Biswa Nath Datta
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