McDonald, Pestana, and Wathen [SIAM J. Sci. Comput., 40 (2018), pp. A1012–A1033] present a method for preconditioning time-dependent PDEs via an approximation by a nearby time-periodic problem, that is, they employ circulant-related matrices as preconditioners for the non-symmetric block Toeplitz matrices which arise from an all-at-once formulation. They suggest that such an approach might be efficiently implemented in parallel. In this short article, we present parallel numerical results for their preconditioner which exhibit strong scaling. We also extend their preconditioner via a Neumann series approach which also allows for efficient parallel execution. Results are shown for both parabolic and hyperbolic PDEs. Our simple implementation...
We present a robust and scalable preconditioner for the solution of large-scale linear systems that ...
In this thesis, the design of the preconditioners we propose starts from applications instead of tre...
In this article, we derive fast and robust preconditioned iterative methods for the all-at-once lin...
In this thesis, we examine the solution to a range of time-dependent Partial Differential Equation (...
Standard Krylov subspace solvers for self-adjoint problems have rigorous convergence bounds based so...
The recent development of the high performance computer platforms shows a clear trend towards hetero...
In this work, we propose a simple yet generic preconditioned Krylov subspace method for a large clas...
In this work, we propose a simple yet generic preconditioned Krylov subspace method for a large clas...
Numerical Linear Algebra—specifically the computational solution of equations—forms a significant pa...
In this work, we propose a novel parallel-in-time preconditioner for an all-at-once system, arising ...
International audienceWe present original time-parallel algorithms for the solution of the implicit ...
In this paper, we consider preconditioning for PDE-constrained optimization problems. The underlying...
Optimal control problems governed by time-dependent partial differential equations (PDEs) lead to la...
Parallel-in-time methods have become increasingly popular in the simulation of time-dependent numeri...
Parallel-in-time (PinT) methods have become an increasingly popular tool in the numerical solution o...
We present a robust and scalable preconditioner for the solution of large-scale linear systems that ...
In this thesis, the design of the preconditioners we propose starts from applications instead of tre...
In this article, we derive fast and robust preconditioned iterative methods for the all-at-once lin...
In this thesis, we examine the solution to a range of time-dependent Partial Differential Equation (...
Standard Krylov subspace solvers for self-adjoint problems have rigorous convergence bounds based so...
The recent development of the high performance computer platforms shows a clear trend towards hetero...
In this work, we propose a simple yet generic preconditioned Krylov subspace method for a large clas...
In this work, we propose a simple yet generic preconditioned Krylov subspace method for a large clas...
Numerical Linear Algebra—specifically the computational solution of equations—forms a significant pa...
In this work, we propose a novel parallel-in-time preconditioner for an all-at-once system, arising ...
International audienceWe present original time-parallel algorithms for the solution of the implicit ...
In this paper, we consider preconditioning for PDE-constrained optimization problems. The underlying...
Optimal control problems governed by time-dependent partial differential equations (PDEs) lead to la...
Parallel-in-time methods have become increasingly popular in the simulation of time-dependent numeri...
Parallel-in-time (PinT) methods have become an increasingly popular tool in the numerical solution o...
We present a robust and scalable preconditioner for the solution of large-scale linear systems that ...
In this thesis, the design of the preconditioners we propose starts from applications instead of tre...
In this article, we derive fast and robust preconditioned iterative methods for the all-at-once lin...