An Extended Validity Domain for Constraint Learning

Published Online:https://doi.org/10.1287/ijoc.2024.0815

References

  • Balestriero R, Pesenti J, LeCun Y (2021) Learning in high dimension always amounts to extrapolation. Preprint, submitted October 18, https://arxiv.org/abs/2110.09485.Google Scholar
  • Bengio Y, Lodi A, Prouvost A (2021) Machine learning for combinatorial optimization: A methodological tour d’horizon. Eur. J. Oper. Res. 290(2):405–421.CrossrefGoogle Scholar
  • Bergman D, Huang T, Brooks P, Lodi A, Raghunathan AU (2022) JANOS: An integrated predictive and prescriptive modeling framework. INFORMS J. Comput. 34(2):807–816.LinkGoogle Scholar
  • Ceccon F, Jalving J, Haddad J, Thebelt A, Tsay C, Laird CD, Misener R (2022) OMLT: Optimization & machine learning toolkit. J. Machine Learn. Res. 23(349):1–8.Google Scholar
  • Courrieu P (1994) Three algorithms for estimating the domain of validity of feedforward neural networks. Neural Networks 7(1):169–174.CrossrefGoogle Scholar
  • De Filippo A, Lombardi M, Milano M (2018) Methods for off-line/on-line optimization under uncertainty. Lang J, ed. Proc. Twenty-Seventh Internat. Joint Conf. Artificial Intelligence, vol.18 (International Joint Conferences on Artificial Intelligence Organization, Montreal, CA), 1270–1276.Google Scholar
  • Fajemisin AO, Maragno D, Den Hertog D (2024) Optimization with constraint learning: A framework and survey. Eur. J. Oper. Res. 314(1):1–14. Google Scholar
  • Gurobi (2023) Gurobi optimizer reference manual. https://www.gurobi.com.Google Scholar
  • Kotary J, Fioretto F, Van Hentenryck P, Wilder B (2021) End-to-end constrained optimization learning: A survey. Zhou Z-H, ed. Internat. Joint Conf. Artificial Intelligence, IJCAI (International Joint Conferences on Artificial Intelligence Organization, Montreal, CA), 4475–4482Google Scholar
  • Liu FT, Ting KM, Zhou ZH (2008) Isolation forest. 2008 Eighth IEEE Internat. Conf. Data Mining (IEEE, Piscataway, NJ), 413–422.Google Scholar
  • Long W, Wu T, Liang X, Xu S (2019) Solving high-dimensional global optimization problems using an improved sine cosine algorithm. Expert Systems Appl. 123:108–126.CrossrefGoogle Scholar
  • Maragno D, Wiberg H, Bertsimas D, Birbil Şİ, den Hertog D, Fajemisin AO (2025) Mixed-integer optimization with constraint learning. Oper. Res. 73(2):1011–1028.LinkGoogle Scholar
  • Mistry M, Letsios D, Krennrich G, Lee RM, Misener R (2021) Mixed-integer convex nonlinear optimization with gradient-boosted trees embedded. INFORMS J. Comput. 33(3):1103–1119.LinkGoogle Scholar
  • Sadana U, Chenreddy A, Delage E, Forel A, Frejinger E, Vidal T (2025) A survey of contextual optimization methods for decision-making under uncertainty. Eur. J. Oper. Res. 320(2):271–289.CrossrefGoogle Scholar
  • Schweidtmann AM, Weber JM, Wende C, Netze L, Mitsos A (2022) Obey validity limits of data-driven models through topological data analysis and one-class classification. Optim. Engrg. 23(2):855–876.CrossrefGoogle Scholar
  • Shi C, Emadikhiav M, Lozano L, Bergman D (2024) Constraint learning to define trust regions in optimization over pre-trained predictive models. INFORMS J. Comput. 36(6):1382–1399.LinkGoogle Scholar
  • Surjanovic S, Bingham D (2023) Virtual library of simulation experiments: Test functions and datasets. Accessed June 11, 2024, https://www.sfu.ca/∼ssurjano/optimization.html.Google Scholar
  • Tang B, Khalil EB (2024) Pyepo: A pytorch-based end-to-end predict-then-optimize library for linear and integer programming. Math. Program. Comput. 16(3):297–335.CrossrefGoogle Scholar
  • Tjeng V, Xiao KY, Tedrake R (2019) Evaluating robustness of neural networks with mixed integer programming. Internat. Conf. Learn. Representations (ICLR, Appleton, WI), 43–47.Google Scholar
  • Zhu Y, Burer S (2025) An extended validity domain for constraint learning. https://doi.org/10.1287/ijoc.2024.0815.cd, https://github.com/INFORMSJoC/2024.0815.Google Scholar
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