Existence and Optimality Conditions for Risk-Averse PDE-Constrained Optimization
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Philipps-Universitat Marburg (Germany). FB12 Mathematik und Informatik
Uncertainty is ubiquitous in virtually all engineering applications, and, for such problems, it is inadequate to simulate the underlying physics without quantifying the uncertainty in unknown or random inputs, boundary and initial conditions, and modeling assumptions. Here in this paper, we introduce a general framework for analyzing risk-averse optimization problems constrained by partial differential equations (PDEs). In particular, we postulate conditions on the random variable objective function as well as the PDE solution that guarantee existence of minimizers. Furthermore, we derive optimality conditions and apply our results to the control of an environmental contaminant. Lastly, we introduce a new risk measure, called the conditional entropic risk, that fuses desirable properties from both the conditional value-at-risk and the entropic risk measures.
- Research Organization:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA); Defense Advanced Research Projects Agency (DARPA)
- Grant/Contract Number:
- AC04-94AL85000; NA0003525
- OSTI ID:
- 1441483
- Report Number(s):
- SAND-2018-5345J; 663230
- Journal Information:
- SIAM/ASA Journal on Uncertainty Quantification, Vol. 6, Issue 2; ISSN 2166-2525
- Publisher:
- SIAMCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
Spectral risk measures: the risk quadrangle and optimal approximation
|
journal | May 2018 |
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