Formulations for Surrogate-Based Optimization Under Uncertainty

2002 ◽  
Author(s):  
Michael Eldred ◽  
Anthony Giunta ◽  
Steven Wojtkiewicz ◽  
Timothy Trucano
Keyword(s):  
Optimization Under Uncertainty ◽  
Surrogate Based 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 A density-matching approach for optimization under uncertainty

2016 ◽  
Vol 305 ◽  
pp. 562-578 ◽  
Author(s):  
Pranay Seshadri ◽  
Paul Constantine ◽  
Gianluca Iaccarino ◽  
Geoffrey Parks
Keyword(s):  
Optimization Under Uncertainty
Sign in / Sign up

Export Citation Format

Share Document