Results 11 to 20 of about 11,516 (229)
, 2000 Multigrid presents both an elementary introduction to multigrid methods for solving partial differential equations and a contemporary survey of advanced multigrid techniques and real-life applications.Multigrid methods are invaluable to researchers in ...
Trottenberg, Ulrich +2 more
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Multigrid Techniques in Economics
Operations Research, 2010 I present a self-contained introduction to multigrid methods with an emphasis on techniques relevant to dynamic programming and related problems. A probabilistic interpretation of the numerical principles is highlighted. Multigrid solvers are shown to be naturally matched to the challenges posed by intractable structural dynamic models routinely ...
Adam Speight
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Multigrid renormalization [PDF]
Journal of Computational Physics, 2018 We combine the multigrid (MG) method with state-of-the-art concepts from the variational formulation of the numerical renormalization group. The resulting MG renormalization (MGR) method is a natural generalization of the MG method for solving partial differential equations.
Michael Lubasch +2 more
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A Bootstrap Multigrid Eigensolver
SIAM Journal on Matrix Analysis and Applications, 2022 This paper introduces bootstrap multigrid methods for solving eigenvalue problems arising from the discretization of partial differential equations. Inspired by the full bootstrap algebraic multigrid (BAMG) setup algorithm that includes an AMG eigensolver, it is illustrated how the algorithm can be simplified for the case of a discretized partial ...
James J. Brannick, Shuhao Cao
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Autotuning multigrid with PetaBricks [PDF]
Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis, 2009 Algorithmic choice is essential in any problem domain to realizing optimal computational performance. Multigrid is a prime example: not only is it possible to make choices at the highest grid resolution, but a program can switch techniques as the problem is recursively attacked on coarser grid levels to take advantage of algorithms with different ...
Cy P. Chan +4 more
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Algebraic multigrid methods [PDF]
Acta Numerica, 2017 This paper provides an overview of AMG methods for solving large-scale systems of equations, such as those from discretizations of partial differential equations. AMG is often understood as the acronym of ‘algebraic multigrid’, but it can also be understood as ‘abstract multigrid’. Indeed, we demonstrate in this paper how and why an algebraic multigrid
Jinchao Xu, Ludmil T. Zikatanov
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An adaptive-smoothing multigrid method for the Navier-Stokes equations
, 2022 S.94-100The paper presents the development and investigation of an adaptive-smoothing (AS) procedure in conjunction with a full multigrid (FMG) - full approximation storage (FAS) method.
Drikakis, D., Iliev, O., Vassileva, D.
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Nonlinear Multigrid Implementation for the Two-Dimensional Cahn–Hilliard Equation
Mathematics, 2020 We present a nonlinear multigrid implementation for the two-dimensional Cahn−Hilliard (CH) equation and conduct detailed numerical tests to explore the performance of the multigrid method for the CH equation.
Chaeyoung Lee +3 more
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Моделирование и анализ информационных систем, 2017 In the work, we consider the problem of accelerating the iteration process of the numerical solution of boundary-value problems for partial differential equations (PDE) by the method of collocations and least residuals (CLR). To solve this problem, it is
Vasily P. Shapeev, Evgenii V. Vorozhtsov
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Optimizing the number of multigrid cycles in the full multigrid algorithm [PDF]
Numerical Linear Algebra with Applications, 2010 AbstractMultigrid (MG) methods are among the most efficient and widespread methods for solving large linear systems of equations that arise, for example, from the discretization of partial differential equations. In this paper we introduce a new approach for optimizing the computational cost of the full MG method to achieve a given accuracy by ...
Alexander Thekale +3 more
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