TY - GEN A1 - Götschel, Sebastian A1 - Minion, Michael L. T1 - An Efficient Parallel-in-Time Method for Optimization with Parabolic PDEs N2 - To solve optimization problems with parabolic PDE constraints, often methods working on the reduced objective functional are used. They are computationally expensive due to the necessity of solving both the state equation and a backward-in-time adjoint equation to evaluate the reduced gradient in each iteration of the optimization method. In this study, we investigate the use of the parallel-in-time method PFASST in the setting of PDE constrained optimization. In order to develop an efficient fully time-parallel algorithm we discuss different options for applying PFASST to adjoint gradient computation, including the possibility of doing PFASST iterations on both the state and adjoint equations simultaneously. We also explore the additional gains in efficiency from reusing information from previous optimization iterations when solving each equation. Numerical results for both a linear and a non-linear reaction-diffusion optimal control problem demonstrate the parallel speedup and efficiency of different approaches. T3 - ZIB-Report - 19-04 KW - PDE-constrained optimization KW - parallel-in-time methods KW - PFASST Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-71490 SN - 1438-0064 ER - TY - JOUR A1 - Götschel, Sebastian A1 - Minion, Michael L. T1 - An Efficient Parallel-in-Time Method for Optimization with Parabolic PDEs JF - SIAM J. Sci. Comput. N2 - To solve optimization problems with parabolic PDE constraints, often methods working on the reduced objective functional are used. They are computationally expensive due to the necessity of solving both the state equation and a backward-in-time adjoint equation to evaluate the reduced gradient in each iteration of the optimization method. In this study, we investigate the use of the parallel-in-time method PFASST in the setting of PDE-constrained optimization. In order to develop an efficient fully time-parallel algorithm, we discuss different options for applying PFASST to adjoint gradient computation, including the possibility of doing PFASST iterations on both the state and the adjoint equations simultaneously. We also explore the additional gains in efficiency from reusing information from previous optimization iterations when solving each equation. Numerical results for both a linear and a nonlinear reaction-diffusion optimal control problem demonstrate the parallel speedup and efficiency of different approaches. Y1 - 2019 U6 - https://doi.org/10.1137/19M1239313 VL - 41 IS - 6 SP - C603 EP - C626 ER - TY - GEN A1 - Götschel, Sebastian A1 - Minion, Michael L. T1 - Parallel-in-Time for Parabolic Optimal Control Problems Using PFASST BT - DD 2017. T2 - Domain Decomposition Methods in Science and Engineering XXIV N2 - In gradient-based methods for parabolic optimal control problems, it is necessary to solve both the state equation and a backward-in-time adjoint equation in each iteration of the optimization method. In order to facilitate fully parallel gradient-type and nonlinear conjugate gradient methods for the solution of such optimal control problems, we discuss the application of the parallel-in-time method PFASST to adjoint gradient computation. In addition to enabling time parallelism, PFASST provides high flexibility for handling nonlinear equations, as well as potential extra computational savings from reusing previous solutions in the optimization loop. The approach is demonstrated here for a model reaction-diffusion optimal control problem. Y1 - 2018 U6 - https://doi.org/10.1007/978-3-319-93873-8_34 SP - 363 EP - 371 PB - Springer CY - Cham ER - TY - GEN A1 - Götschel, Sebastian A1 - Minion, Michael L. T1 - Parallel-in-Time for Parabolic Optimal Control Problems Using PFASST N2 - In gradient-based methods for parabolic optimal control problems, it is necessary to solve both the state equation and a backward-in-time adjoint equation in each iteration of the optimization method. In order to facilitate fully parallel gradient-type and nonlinear conjugate gradient methods for the solution of such optimal control problems, we discuss the application of the parallel-in-time method PFASST to adjoint gradient computation. In addition to enabling time parallelism, PFASST provides high flexibility for handling nonlinear equations, as well as potential extra computational savings from reusing previous solutions in the optimization loop. The approach is demonstrated here for a model reaction-diffusion optimal control problem. T3 - ZIB-Report - 17-51 Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-64989 SN - 1438-0064 ER -