Abstract. PDE-constrained optimization problems have a wide range of applications, but they lead to very large and ill-conditioned linear systems, especially if the problems are time dependent. In this paper we outline an approach for dealing with such problems by decomposing them in time and applying an additive Schwarz preconditioner in time, so that we can take advantage of parallel computers to deal with the very large linear systems. We then illustrate the performance of our method on a variety of problems. Key words. PDE-constrained optimization, space-time methods, preconditioning, Schur com-plement, domain decomposition, parallel computing
In this article, we motivate, derive, and test effective preconditioners to be used with the MINRES ...
International audienceNew parallel methods, based on the Schwarz and Schur domain decomposition tech...
PDE-constrained optimization is a frontier problem in computational science and engineering. All PDE...
Optimal control problems governed by time-dependent partial differential equations (PDEs) lead to la...
In this paper we review several methods for solving large sparse linear systems arising from discret...
International audienceWe developed a parallel time domain decomposition method to solve systems of O...
International audienceWe developed a parallel time domain decomposition method to solve systems of O...
International audienceOptimizing solvers for linear systems is a major challenge in scientific comp...
International audienceOptimizing solvers for linear systems is a major challenge in scientific comp...
Abstract. With the continued evolution of computing architectures towards many-core com-puting, algo...
The efficient solution of large-scale systems resulting from the discretization of partial different...
Abstract. Large-scale optimization of systems governed by partial differential equations (PDEs) is a...
Domain decomposition methods in space applied to Partial Differential Equations (PDEs) expanded cons...
International audienceNew parallel methods, based on the Schwarz and Schur domain decomposition tech...
International audienceNew parallel methods, based on the Schwarz and Schur domain decomposition tech...
In this article, we motivate, derive, and test effective preconditioners to be used with the MINRES ...
International audienceNew parallel methods, based on the Schwarz and Schur domain decomposition tech...
PDE-constrained optimization is a frontier problem in computational science and engineering. All PDE...
Optimal control problems governed by time-dependent partial differential equations (PDEs) lead to la...
In this paper we review several methods for solving large sparse linear systems arising from discret...
International audienceWe developed a parallel time domain decomposition method to solve systems of O...
International audienceWe developed a parallel time domain decomposition method to solve systems of O...
International audienceOptimizing solvers for linear systems is a major challenge in scientific comp...
International audienceOptimizing solvers for linear systems is a major challenge in scientific comp...
Abstract. With the continued evolution of computing architectures towards many-core com-puting, algo...
The efficient solution of large-scale systems resulting from the discretization of partial different...
Abstract. Large-scale optimization of systems governed by partial differential equations (PDEs) is a...
Domain decomposition methods in space applied to Partial Differential Equations (PDEs) expanded cons...
International audienceNew parallel methods, based on the Schwarz and Schur domain decomposition tech...
International audienceNew parallel methods, based on the Schwarz and Schur domain decomposition tech...
In this article, we motivate, derive, and test effective preconditioners to be used with the MINRES ...
International audienceNew parallel methods, based on the Schwarz and Schur domain decomposition tech...
PDE-constrained optimization is a frontier problem in computational science and engineering. All PDE...