CoChat
Sign Up
Cited by 135Risk and Portfolio OptimizationAdvanced Optimization Algorithms ResearchAdvanced Control Systems Optimization

The DOI matches its Crossref record; 81 of the 81 references it cites were found in a registry or an index.

Theory and Applications of Robust Optimization

Open access
Bertsimas, Dimitris, Brown, David B., Caramanis, Constantine·2011·SIAM Review
DOI 10.1137/080734510
Open this paper in CoChat — free accountOpen PDF

Abstract

In this paper we survey the primary research, both theoretical and applied, in the area of robust optimization (RO). Our focus is on the computational attractiveness of RO approaches, as well as the modeling power and broad applicability of the methodology. In addition to surveying prominent theoretical results of RO, we also present some recent results linking RO to adaptable models for multistage decision-making problems. Finally, we highlight applications of RO across a wide spectrum of domains, including finance, statistics, learning, and various areas of engineering.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1137/080734510

Cites

81 of 81 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • The characterization of uncertainty sets in adaptive robust optimization can be data-driven and is often interpretable.

    “Furthermore, the characterization of the uncertainty sets can be datadriven and is often interpretable (Bertsimas, Brown, & Caramanis, 2011).”

    From Prescriptive analytics: Literature review and research challenges (2020) · cites this paper as Bertsimas, Brown, & Caramanis, 2011

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 43

  1. Banerjee O., 2008, J. Mach. Learn. Res., 9, 485
  2. A. Ben-Tal, L. El Ghaoui, and A. Nemirovski, Robustness , in Handbook of Semidefinite Programming, R. Saigal, L. Vandenberghe, and H. Wolkowicz, H., eds., Kluwer Academic, Dordrecht, The Netherlands, 2000, pp. 139–162. · DOI
  3. A. Ben-Tal, L. El Ghaoui, and A. Nemirovski, Robustness Optimization , Princeton University Press, Princeton, NJ, 2009. · DOI
  4. A. Ben-Tal, T. Margalit, and A. Nemirovski, Robust modeling of multi-stage portfolio problems , in High Performance Optimization, Appl. Optim. 33, Kluwer Academic, Dordrecht, The Netherlands, 2000, pp. 303–328. · DOI
  5. A. Ben-Tal and A. Nemirovski, On approximate robust counterparts of uncertain semidefinite and conic quadratic programs , in Proceedings of 20th IFIP TC7 Conference on System Modelling and Optimization, Trier, Germany, 2001, pp. 23–27.
  6. D. Bertsekas, Dynamic Programming and Optimal Control , Vol. 1, Athena Scientific, Belmont, MA, 1995.
  7. D. Bertsekas, Dynamic Programming and Optimal Control , Vol. 2, Athena Scientific, Belmont, MA, 1995.
  8. D. Bertsimas and C. Caramanis, Adaptability via sampling , in Proceedings of the 46th Conference on Decision and Control, 2007, pp. 4717–4722. · DOI
  9. D. Bertsimas, D. Iancu, and P. Parrilo, Optimality of affine policies in multi-stage robust optimization , in Proceedings of the 48th Conference on Decision and Control, 2009, pp. 1131–1138. · DOI
  10. J. Birge and F. Louveaux, Introduction to Stochastic Programming , Springer-Verlag, New York, 1997.
  11. S. Boyd, L. El Ghaoui, E. Feron, and V. Balakrishnan, Linear Matrix Inequalities in System and Control Theory , SIAM, Philadelphia, 1994. · DOI
  12. S. Boyd and L. Vandenberghe, Convex Optimization , Cambridge University Press, Cambridge, UK, 2004. · DOI · Paper page
  13. G. Calafiore and M. Campi, On two-stage portfolio allocation problems with affine recourse , in Proceedings of the 44th IEEE Conference on Decision and Control, 2005, pp. 8042–8046. · DOI
  14. C. Caramanis, Adaptable Optimization: Theory and Algorithms , Ph.D. dissertation, Massachusetts Institute of Technology, Cambridge, MA, 2006.
  15. C. Caramanis and S. Mannor, Learning in the limit with adversarial disturbances , in Proceedings of the 21st Annual Conference on Learning Theory, 2008, pp. 467–478.
  16. C. Caramanis, S. Mannor, and H. Xu, Robust optimization in machine learning , in Optimization in Machine Learning, MIT Press, Cambridge, MA, 2011. · DOI
  17. d'Aspremont A., 2008, J. Mach. Learn. Res., 9, 1269
  18. B. Dean, M. Goemans, and J. Vondrák, Approximating the stochastic knapsack problem: The benefit of adaptivity , in Proceedings of the 45th Annual IEEE Symposium on Foundations of Computer Science, Rome, Italy, 2004, pp. 208–217. · DOI
  19. C. Dellacherie, Quelque commentaires sur les prolongements de capacitiès , in Seminaire Probabilities V (Univ. Strasbourg, année universitaire 1969–1970), Lecture Notes in Math. 191, Springer-Verlag, Berlin, 1971, pp. 77–81. · DOI
  20. G. Dullerud and F. Paganini, A Course in Robust Control Theory: A Convex Approach , Springer-Verlag, New York, 1999. · DOI
  21. L. El Ghaoui and S.I. Niculescu, eds. Advances in Linear Matrix Inequality Methods in Control , Adv. Des. Control 2, SIAM, Philadelphia, 2000. · DOI
  22. M. Goemans and J. Vondrák, Stochastic covering and adaptivity , in Proceedings of LATIN 2006, Springer-Verlag, Berlin, 2006, pp. 532–543. · DOI
  23. P. J. Goulart and E. Kerrigan, Relationships between affine feedback policies for robust control with constraints , in Proceedings of the 16th IFAC World Congress on Automatic Control, 2005. · DOI
  24. P. Grieder, P. Parrilo, and M. Morari, Robust receding horizon control: Analysis and synthesis , in Proceedings of the 42nd IEEE Conference on Decision and Control, 2003, pp. 941–946. · DOI
  25. P. Huber, Robust Statistics , John Wiley, New York, 1981. · DOI · Paper page
  26. P. Kall and S. Wallace, Stochastic Programming , John Wiley, Chichester, UK, 1994.
  27. E. Kerrigan and J. Maciejowski, On robust optimization and the optimal control of constrained linear systems with bounded state disturbances , in Proceedings of the European Control Conference, 2003. · DOI
  28. Kim S.-J., 2006, Adv. Neural Inform. Process. Syst., 18, 659
  29. P. Kouvelis and G. Yu, Robust Discrete Optimization and Its Applications , Kluwer Academic, Norwell, MA, 1997. · DOI
  30. Lanckriet G., 2002, J. Mach. Learn. Res., 3, 555
  31. M. Mani, A. Singh, and M. Orshanksy, Joint design-time and post-silicon minimization on parametric yield loss using adjustable robust optimization , in Proceedings of the 2006 IEEE/ACM International Conference on Computer Aided Design, 2006, pp. 19–26. · DOI
  32. Markowitz H., 1952, J. Finance, 7, 77
  33. H. Markowitz, Portfolio Selection , John Wiley, New York, 1959.
  34. I. Paschalidis and S.C. Kang, Robust linear optimization: On the benefits of distributional information and applications in inventory control , in Proceedings of the 44th IEEE Conference on Decision and Control, 2005, pp. 4416–4421. · DOI
  35. D. Patil, S. Yun, S.J. Kim, A. Cheung, M. Horowitz, and S. Boyd, A new method for design of robust digital circuits , in Proceedings of the International Symposium on Quality Electronic Design (ISQED), 2005, pp. 676–681. · DOI
  36. A. Prékopa, Stochastic Programming , Kluwer Academic, Dordrecht, The Netherlands, 1995. · DOI
  37. M. Puterman, Markov Decision Processes: Discrete Stochastic Dynamic Programming , Wiley-Interscience, New York, 1994. · DOI
  38. Shivaswamy P., 2006, J. Mach. Learn. Res., 7, 1283
  39. A. Singh, K. He, C. Caramanis, and M. Orshansky, Mitigation of intra-array SRAM variability using adaptive voltage architecture , in Proceedings of the International Conference on Computer Aided Design (ICCAD), 2009, pp. 637–644. · DOI
  40. Xu H., 2009, J. Mach. Learn. Res., 10, 1485
  41. H. Xu, C. Caramanis, and S. Mannor, A distributional interpretation of robust optimization , in Proceedings of the Allerton Conference on Communication, Control and Computing, 2010, pp. 552–556. · DOI
  42. H. Xu and S. Mannor, Tradeoff performance and robustness in linear programming and Markov decision processes , in NIPS, 2006, pp. 1537–1544. · DOI
  43. S. Yun and C. Caramanis, System level optimization in wireless networks with uncertain customer arrival rates , in Proceedings of the Allerton Conference on Communications, Control and Computing, 2008, pp. 1022–1029. · DOI

Cited by

Sorted by citations

Related

Metadata

Authors
Bertsimas, D., Brown, D. B., & Caramanis, C.
ORCID
Venue
SIAM Review
Publisher
Society for Industrial & Applied Mathematics (SIAM)
Published
2011-01
Volume
53
Issue
3
Pages
464-501
ISSN
  • 0036-1445 (ISSN-L)
  • 1095-7200
DOI
10.1137/080734510
OpenAlex
W1504946885
Cited by
134
References
81
Citations by year
2026: 3 · 2025: 9 · 2024: 8 · 2023: 10 · 2022: 17 · 2021: 11 · 2020: 9 · 2019: 13 · 2018: 11 · 2017: 6 · 2016: 8 · 2015: 6 · 2014: 5 · 2013: 4 · 2012: 6
FWCI
7.82
Type
journal article
Language
en
Open access
Locations
  • SIAM Review (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
  • SIAM Journal on Control and Optimization
Topics
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Risk and Portfolio Optimization
  • Physical Sciences › Mathematics › Numerical Analysis › Advanced Optimization Algorithms Research
  • Physical Sciences › Engineering › Control and Systems Engineering › Advanced Control Systems Optimization
Keywords
robust optimization, computational tractability, multi-stage decision making, uncertainty modeling, machine learning, engineering applications

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review