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Flexible Inner-Outer Krylov Subspace Methods [PDF]
SIAM Journal on Numerical Analysis, 2002 Flexible Krylov methods refer to a class of methods which accept preconditioning that can change from one step to the next. An important special case is found when a fixed preconditioner is only approximated and the approximation changes from one step to the next.
Valeria Simoncini, Daniel B Szyld
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A Framework for Deflated and Augmented Krylov Subspace Methods [PDF]
SIAM Journal on Matrix Analysis and Applications, 2013 We consider deflation and augmentation techniques for accelerating the convergence of Krylov subspace methods for the solution of nonsingular linear algebraic systems. Despite some formal similarity, the two techniques are conceptually different from preconditioning.
André Gaul +2 more
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Flexible Krylov Methods for Edge Enhancement in Imaging [PDF]
Journal of Imaging, 2021 Many successful variational regularization methods employed to solve linear inverse problems in imaging applications (such as image deblurring, image inpainting, and computed tomography) aim at enhancing edges in the solution, and often involve non ...
Silvia Gazzola +2 more
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Krylov Subspace Solvers and Preconditioners
ESAIM: Proceedings and Surveys, 2018 In these lecture notes an introduction to Krylov subspace solvers and preconditioners is presented. After a discretization of partial differential equations large, sparse systems of linear equations have to be solved.
Vuik C.
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An Improved Reduced-Dimension Robust Capon Beamforming Method Using Krylov Subspace Techniques [PDF]
SensorsA reduced-dimension robust Capon beamforming method using Krylov subspace techniques (RDRCB) is a diagonal loading algorithm with low complexity, fast convergence and strong anti-interference ability.
Xiaolin Wang, Xihai Jiang, Yaowu Chen
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Model-order-reduced spectral-element method for high-accuracy and fast 3-D transient electromagnetic forward modeling with SAI-Krylov [PDF]
Scientific ReportsWith the increasing demand for both accuracy and efficiency in transient electromagnetic (TEM) simulations, conventional 3-D forward modeling methods face growing challenges.
Ya’nan Fan +4 more
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Krylov Subspace Methods on Supercomputers [PDF]
SIAM Journal on Scientific and Statistical Computing, 1989 This paper presents a short survey of recent research on Krylov subspace methods with emphasis on implementation on vector and parallel computers. Conjugate gradient methods have proven very useful on traditional scalar computers, and their popularity is likely to increase as three-dimensional models gain importance.
Yousef Saad
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Reduced-Rank Adaptive Filtering Using Krylov Subspace
EURASIP Journal on Advances in Signal Processing, 2002 A unified view of several recently introduced reduced-rank adaptive filters is presented. As all considered methods use Krylov subspace for rank reduction, the approach taken in this work is inspired from Krylov subspace methods for iterative solutions ...
Burykh Sergueï, Abed-Meraim Karim
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Efficient preconditioning strategies for accelerating GMRES in block-structured nonlinear systems for image deblurring. [PDF]
PLoS ONEWe propose an efficient preconditioning strategy to accelerate the convergence of Krylov subspace methods, specifically for solving complex nonlinear systems with a block five-by-five structure, commonly found in cell-centered finite difference ...
Rizwan Khalid +4 more
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IDR: A new generation of Krylov subspace methods?
Linear Algebra and its Applications, 2013 Die Induzierte Dimensions-Reduktions-Technik (IDR-Technik), entwickelt von Sonneveld und van Gijzen, ist ein mächtiges Konzept, welches in einer Unzahl von Transponierten-freien Krylov-Unterraum-Verfahren basierend auf kurzen Rekursionen gipfelt. Wir stellen die wesentlichen Unterschiede zwischen und Gemeinsamkeiten von IDR-Methoden und klassischen ...
Rendel, Olaf +2 more
openaire +3 more sources