Imperial College London
Alternate

Emeritus ProfessorBercRustem

Faculty of Engineering, Department of Computing

Emeritus Professor
 
 
 
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Contact

 

+44 (0)20 7594 8345b.rustem Website

 
 
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Assistant

 

Dr Amani El-Kholy +44 (0)20 7594 8220

 
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Location

 

361Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Zymler:2012:10.1007/s10107-011-0494-7,
author = {Zymler, S and Kuhn, D and Rustem, B},
doi = {10.1007/s10107-011-0494-7},
journal = {Mathematical Programming},
title = {Distributionally Robust Joint Chance Constraints with Second-Order Moment Information},
url = {http://dx.doi.org/10.1007/s10107-011-0494-7},
year = {2012}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - We develop tractable semidefinite programming based approximations  for distributionally robust individual and joint chance constraints, assuming that only  the first- and second-order moments as well as the support of the uncertain parameters  are given. It is known that robust chance constraints can be conservatively approximated by Worst-Case Conditional Value-at-Risk (CVaR) constraints. We first prove that this approximation is exact for robust individual chance constraints with concave or (not necessarily concave) quadratic constraint functions, and we demonstrate that the Worst-Case CVaR can be computed efficiently for these classes of constraint functions. Next, we study the Worst-Case CVaR approximation for joint chance constraints. This approximation affords intuitive dual interpretations and is provably tighter than two popular benchmark approximations. The tightness depends on a set of scaling parameters, which can be tuned via a sequential convex optimization algorithm. We show that the approximation becomes essentially exact when the scaling parameters are chosen optimally and that the Worst-Case CVaR can be evaluated efficiently if the scaling parameters are kept constant.We evaluate our joint chance constraint approximation in the context of a dynamic water reservoir control problem and numerically  demonstrate its superiority over the two benchmark approximations.
AU  - Zymler,S
AU  - Kuhn,D
AU  - Rustem,B
DO  - 10.1007/s10107-011-0494-7
PY  - 2012///
SN  - 0025-5610
TI  - Distributionally Robust Joint Chance Constraints with Second-Order Moment Information
T2  - Mathematical Programming
UR  - http://dx.doi.org/10.1007/s10107-011-0494-7
UR  - http://hdl.handle.net/10044/1/13955
ER  -
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