<span class="var-sub_title">Large-Scale PDE-Constrained Optimization</span> SC18 Proceedings

The International Conference for High Performance Computing, Networking, Storage, and Analysis

Women in HPC: Diversifying the HPC Community

Large-Scale PDE-Constrained Optimization

Authors: Oana Marin (Argonne National Laboratory)

Abstract: Optimization of time-dependent PDE-constrained optimization problems is extremely challenging from a computational perspective. Presume one forward simulation of a differential equation with N degrees of freedom, advancing in time for M timesteps requires a time to solution T. In this case optimizing for a certain parameter constrained by the PDE takes at least 2kT, where k is the number of iterations up to the convergence of the optimization scheme. We hereby explore strategies for achieving maximum speedup under controlled incurred errors. It is noteworthy that high errors in the computation of the gradient increase the number of iterations required to achieve convergence of the optimization algorithm, which is, in essence, more damaging than any gains made in the computation of the PDE.

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