KKT Preconditioners for PDE-Constrained Optimization with the Helmholtz Equation

2021 ◽  
pp. S225-S248
Author(s):  
Drew P. Kouri ◽  
Denis Ridzal ◽  
Ray Tuminaro
Keyword(s):  
Helmholtz Equation ◽  
Constrained Optimization ◽  
Pde Constrained Optimization

scholarly journals On quantitative stability in infinite-dimensional optimization under uncertainty

2021 ◽  
Author(s):  
M. Hoffhues ◽  
W. Römisch ◽  
T. M. Surowiec
Keyword(s):  
Stochastic Optimization ◽  
Constrained Optimization ◽  
Optimization Problems ◽  
Optimization Under Uncertainty ◽  
Optimal Solutions ◽  
Probability Metrics ◽  
Pde Constrained Optimization ◽  
Quantitative Stability ◽  
Infinite Dimensional ◽  
Optimal Value

AbstractThe vast majority of stochastic optimization problems require the approximation of the underlying probability measure, e.g., by sampling or using observations. It is therefore crucial to understand the dependence of the optimal value and optimal solutions on these approximations as the sample size increases or more data becomes available. Due to the weak convergence properties of sequences of probability measures, there is no guarantee that these quantities will exhibit favorable asymptotic properties. We consider a class of infinite-dimensional stochastic optimization problems inspired by recent work on PDE-constrained optimization as well as functional data analysis. For this class of problems, we provide both qualitative and quantitative stability results on the optimal value and optimal solutions. In both cases, we make use of the method of probability metrics. The optimal values are shown to be Lipschitz continuous with respect to a minimal information metric and consequently, under further regularity assumptions, with respect to certain Fortet-Mourier and Wasserstein metrics. We prove that even in the most favorable setting, the solutions are at best Hölder continuous with respect to changes in the underlying measure. The theoretical results are tested in the context of Monte Carlo approximation for a numerical example involving PDE-constrained optimization under uncertainty.

scholarly journals Large Scale Non-Linear Programming for PDE Constrained Optimization

2002 ◽  
Author(s):  
BART G VAN BLOEMEN WAANDERS ◽  
ROSCOE A BARTLETT ◽  
KEVIN R LONG ◽  
PAUL T BOGGS ◽  
ANDREW G SALINGER
Keyword(s):  
Linear Programming ◽  
Constrained Optimization ◽  
Large Scale ◽  
Pde Constrained Optimization ◽  
Non Linear Programming ◽  
Non Linear
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