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{{Short description|Mathematical optimization theory}}
'''Robust optimization''' is a field of [[mathematical optimization]] theory that deals with optimization problems in which a certain measure of robustness is sought against [[uncertainty]] that can be represented as deterministic variability in the value of the parameters of the problem itself and/or its solution. It is related to, but often distinguished from, [[probabilistic optimization]] methods such as chance-constrained optimization.<ref>{{cite journal | doi=10.3390/en15030825 | doi-access=free | title=Probabilistic Optimization Techniques in Smart Power System | date=2022 | last1=Riaz | first1=Muhammad | last2=Ahmad | first2=Sadiq | last3=Hussain | first3=Irshad | last4=Naeem | first4=Muhammad | last5=Mihet-Popa | first5=Lucian | journal=Energies | volume=15 | issue=3 | page=825 | hdl=11250/2988376 | hdl-access=free }}</ref><ref>{{Cite web| title=Robust Optimization: Chance Constraints | date=2008-04-28 | url=https://people.eecs.berkeley.edu/~elghaoui/Teaching/EE227A/lecture24.pdf | archive-url=https://web.archive.org/web/20230605233436/https://people.eecs.berkeley.edu/~elghaoui/Teaching/EE227A/lecture24.pdf | archive-date=2023-06-05}}</ref>
== History ==
The origins of robust optimization date back to the establishment of modern [[decision theory]] in the 1950s and the use of '''worst case analysis''' and [[Wald's maximin model]] as a tool for the treatment of severe uncertainty. It became a discipline of its own in the 1970s with parallel developments in several scientific and technological fields. Over the years, it has been applied in [[statistics]], but also in [[operations research]],<ref>{{cite journal|last=Bertsimas|first=Dimitris|author2=Sim, Melvyn |title=The Price of Robustness|journal=Operations Research|year=2004|volume=52|issue=1|pages=35–53|doi=10.1287/opre.1030.0065|hdl=2268/253225 |s2cid=8946639 |hdl-access=free}}</ref> [[electrical engineering]],<ref>{{Cite journal |last1=Giraldo |first1=Juan S. |last2=Castrillon |first2=Jhon A. |last3=Lopez |first3=Juan Camilo |last4=Rider |first4=Marcos J. |last5=Castro |first5=Carlos A. |date=July 2019 |title=Microgrids Energy Management Using Robust Convex Programming |journal=IEEE Transactions on Smart Grid |volume=10 |issue=4 |pages=4520–4530 |doi=10.1109/TSG.2018.2863049 |bibcode=2019ITSG...10.4520G |s2cid=115674048 |issn=1949-3053}}</ref><ref name="VPP Robust 2015">{{Cite journal| title = The design of a risk-hedging tool for virtual power plants via robust optimization approach | journal= Applied Energy | date = October 2015 | doi = 10.1016/j.apenergy.2015.06.059 | author = Shabanzadeh M | volume = 155 | pages = 766–777 | last2 = Sheikh-El-Eslami | first2 = M-K |last3 = Haghifam | first3 = P|last4 = M-R| bibcode= 2015ApEn..155..766S }}</ref><ref name="RO2015">{{Cite book
== Example 1==
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: <math>\max_{x\in X}\min_{u\in U(x)} f(x,u)</math>
where the <math>\max</math> represents the decision maker, the <math>\min</math> represents Nature, namely [[uncertainty]], <math>X</math> represents the decision space and <math>U(x)</math> denotes the set of possible values of <math>u</math> associated with decision <math>x</math>. This is the ''classic'' format of the generic model, and is often referred to as ''minimax'' or ''maximin'' optimization problem. The non-probabilistic ('''deterministic''') model has been and is being extensively used for robust optimization especially in the field of signal processing.<ref>{{cite journal | last1 = Verdu | first1 = S. | last2 = Poor | first2 = H. V. | year = 1984 | title = On Minimax Robustness: A general approach and applications
The equivalent [[mathematical programming]] (MP) of the classic format above is
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===Robust counterpart===
The solution method to many robust program involves creating a deterministic equivalent, called the robust counterpart. The practical difficulty of a robust program depends on if its robust counterpart is computationally tractable.<ref>Ben-Tal A., El Ghaoui, L. and Nemirovski, A. (2009). Robust Optimization. ''Princeton Series in Applied Mathematics,'' Princeton University Press, 9-16.</ref><ref>[[Sven Leyffer|Leyffer S.]], Menickelly M., Munson T., Vanaret C. and Wild S. M (2020). A survey of nonlinear robust optimization. ''INFOR: Information Systems and Operational Research,'' Taylor \& Francis.</ref>
== See also ==
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* [[Robust statistics]]
* [[Robust decision making]]
* [[Robust fuzzy programming]]
* [[Stochastic programming]]
* [[Stochastic optimization]]
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== Further reading ==
*H.J. Greenberg. Mathematical Programming Glossary. World Wide Web, http://glossary.computing.society.informs.org/, 1996-2006. Edited by the INFORMS Computing Society.
*{{cite journal | last1 = Ben-Tal | first1 = A. | last2 = Nemirovski | first2 = A. | year = 1998 | title = Robust Convex Optimization
*{{cite journal | last1 = Ben-Tal | first1 = A. | last2 = Nemirovski | first2 = A. | year = 1999 | title = Robust solutions to uncertain linear programs
*{{cite journal | last1 = Ben-Tal | first1 = A. | last2 = Arkadi Nemirovski | first2 = A. | year = 2002 | title = Robust optimization—methodology and applications
*Ben-Tal A., El Ghaoui, L. and Nemirovski, A. (2006). ''Mathematical Programming, Special issue on Robust Optimization,'' Volume 107(1-2).
*Ben-Tal A., El Ghaoui, L. and Nemirovski, A. (2009). Robust Optimization. ''Princeton Series in Applied Mathematics,'' Princeton University Press.
*{{cite journal | last1 = Bertsimas | first1 = D. | last2 = Sim | first2 = M. | year = 2003 | title = Robust Discrete Optimization and Network Flows
*{{cite journal | last1 = Bertsimas | first1 = D. | last2 = Sim | first2 = M. | year = 2006 | title = Tractable Approximations to Robust Conic Optimization Problems Dimitris Bertsimas
*{{cite journal | last1 = Chen | first1 = W. | last2 = Sim | first2 = M. | year = 2009 | title = Goal Driven Optimization
*{{cite journal | last1 = Chen | first1 = X. | last2 = Sim | first2 = M. | last3 = Sun | first3 = P. | last4 = Zhang | first4 = J. | year = 2008 | title = A Linear-Decision Based Approximation Approach to Stochastic Programming
*{{cite journal | last1 = Chen | first1 = X. | last2 = Sim | first2 = M. | last3 = Sun | first3 = P. | year = 2007 | title = A Robust Optimization Perspective on Stochastic Programming
*{{cite journal | last1 = Dembo | first1 = R | year = 1991 | title = Scenario optimization
* Dodson, B., Hammett, P., and Klerx, R. (2014) ''Probabilistic Design for Optimization and Robustness for Engineers'' John Wiley & Sons, Inc. {{ISBN|978-1-118-79619-1}}
*{{cite journal | last1 = Gupta | first1 = S.K. | last2 = Rosenhead | first2 = J. | year = 1968 | title = Robustness in sequential investment decisions | doi = 10.1287/mnsc.15.2.B18 | journal = Management Science | volume = 15 | issue = 2| pages = 18–29 }}
*Kouvelis P. and Yu G. (1997). ''Robust Discrete Optimization and Its Applications,'' Kluwer.
*{{cite journal | last1 = Mutapcic | first1 = Almir | last2 = Boyd | first2 = Stephen | year = 2009 | title = Cutting-set methods for robust convex optimization with pessimizing oracles
*{{cite journal | last1 = Mulvey | first1 = J.M. | last2 = Vanderbei | first2 = R.J. | last3 = Zenios | first3 = S.A. | year = 1995 | title = Robust Optimization of Large-Scale Systems
*Nejadseyfi, O., Geijselaers H.J.M, van den Boogaard A.H. (2018). "Robust optimization based on analytical evaluation of uncertainty propagation". ''Engineering Optimization'' '''51''' (9): 1581-1603. [[doi:10.1080/0305215X.2018.1536752]].
*{{cite journal | last1 =
*{{cite journal | last1 = Rosenhead | first1 = M.J | last2 = Elton | first2 = M | last3 = Gupta | first3 = S.K. | year = 1972 | title = Robustness and Optimality as Criteria for Strategic Decisions | journal = Operational Research Quarterly | volume = 23 | issue = 4| pages = 413–430 | doi=10.2307/3007957| jstor = 3007957 }}
*Rustem B. and Howe M. (2002). ''Algorithms for Worst-case Design and Applications to Risk Management,'' Princeton University Press.
*{{cite journal | last1 = Sniedovich | first1 = M | year = 2007 | title = The art and science of modeling decision-making under severe uncertainty
*{{cite journal | last1 = Sniedovich | first1 = M | year = 2008 | title = Wald's Maximin Model: a Treasure in Disguise!
*{{cite journal | last1 = Sniedovich | first1 = M | year = 2010 | title = A bird's view of info-gap decision theory
*{{cite journal | last1 = Wald | first1 = A | year = 1939 | title = Contributions to the theory of statistical estimation and testing hypotheses |
*{{cite journal | last1 = Wald | first1 = A | year = 1945 | title = Statistical decision functions which minimize the maximum risk
*Wald, A. (1950). ''Statistical Decision Functions,'' John Wiley, NY.
*{{cite book |doi=10.1109/IranianCEE.2015.7146458|isbn=978-1-4799-1972-7|chapter=Generation Maintenance Scheduling via robust optimization|title=2015 23rd Iranian Conference on Electrical Engineering|year=2015|last1=Shabanzadeh|first1=Morteza|last2=Fattahi|first2=Mohammad|pages=1504–1509|s2cid=8774918 }}
==External links==
* [
* [http://robust.moshe-online.com: Robust Decision-Making Under Severe Uncertainty]
* [https://robustimizer.com/ Robustimizer: Robust optimization software]
{{Major subfields of optimization}}
[[Category:Mathematical optimization]]
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