Mixed-Integer Optimization with Constraint Learning
References
- Amos B, Xu L, Kolter JZ (2017) Input convex neural networks. Int. Conf. Mach. Learn. (PMLR, New York), 146–155.Google Scholar
- (2020) Strong mixed-integer programming formulations for trained neural networks. Math. Programming 183(1–2):3–39.Crossref, Google Scholar
- (2016) Recursive partitioning for heterogeneous causal effects. Proc. National Acad. Sci. USA 113(27):7353–7360.Crossref, Google Scholar
- (2021) Learning in high dimension always amounts to extrapolation. Preprint, submitted October 18, https://arxiv.org/abs/2110.09485.Google Scholar
- (2021) Machine learning for combinatorial optimization: A methodological tour d’horizon. Eur. J. Oper. Res. 290(2):405–421.Crossref, Google Scholar
- (2022) JANOS: An integrated predictive and prescriptive modeling framework. INFORMS J. Comput. 34(2):807–816.Link, Google Scholar
- (2017) Optimal classification trees. Machine Learn. 106(7):1039–1082.Crossref, Google Scholar
- (2018) Machine Learning Under a Modern Optimization Lens (Dynamic Ideas, Belmont, MA).Google Scholar
- (2020) From predictive to prescriptive analytics. Management Sci. 66(3):1025–1044.Link, Google Scholar
- (2016) An analytics approach to designing combination chemotherapy regimens for cancer. Management Sci. 62(5):1511–1531.Link, Google Scholar
- (2021) Personalized prescription of ACEI/ARBs for hypertensive COVID-19 patients. Healthcare Management Sci. 24(2):339–355.Crossref, Google Scholar
- (2021) Optimizing objective functions determined from random forests. Preprint, submitted June 16, https://dx.doi.org/10.2139/ssrn.2986630.Google Scholar
- (2015) Embedding decision trees and random forests in constraint programming. Michel L, ed. Integration of AI and OR Techniques in Constraint Programming. CPAIOR 2015. Lecture Notes in Computer Science, vol. 9075 (Springer, Cham, Switzerland), 74–90.Google Scholar
- (2001) Random forests. Machine Learn. 45(1):5–32.Crossref, Google Scholar
- (1984) Classification and Regression Trees (Routledge, London).Google Scholar
- (2020) Input convex neural networks for optimal voltage regulation. Preprint, submitted February 20, https://arxiv.org/abs/2002.08684.Google Scholar
- (2019) Data-driven power system operation: Exploring the balance between cost and risk. IEEE Trans. Power Systems 34(1):791–801.Crossref, Google Scholar
- (2014) Interpolation and extrapolation: Comparison of definitions and survey of algorithms for convex and concave hulls. Proc. IEEE Sympos. on Computational Intelligence and Data Mining (IEEE, Piscataway, NJ), 310–314.Google Scholar
- (2021) Smart “predict, then optimize”. Management Sci. 68(1):9–26.Google Scholar
- Fajemisin AO, Maragno D, den Hertog D (2023) Optimization with constraint learning: A framework and survey. Eur. J. Oper. Res. ePub ahead of print May 3, https://doi.org/10.1016/j.ejor.2023.04.041.Google Scholar
- (2003) Robust portfolio selection problems. Math. Oper. Res. 28(1):1–38.Link, Google Scholar
- (2015) Explaining and harnessing adversarial examples. Preprint, submitted December 20, https://arxiv.org/abs/1412.6572.Google Scholar
- (2019) ReLU networks as surrogate models in mixed-integer linear programs. Computers Chemical Engrg. 131:106580.Crossref, Google Scholar
- Gurobi Optimization LLC (2021) Gurobi optimizer reference manual. Accessed June 28, 2021, https://www.gurobi.com.Google Scholar
- (2011) Neural-network security-boundary constrained optimal power flow. IEEE Trans. Power Systems 26(1):63–72.Crossref, Google Scholar
- (2018) Data-driven security-constrained AC-OPF for operations and markets. Proc. 20th Power Systems Computation Conf. (IEEE, Piscataway, NJ), 1–7.Google Scholar
- Kleijnen JP (2018) Design and analysis of simulation experiments (Springer, Berlin).Google Scholar
- (2018) One-class synthesis of constraints for Mixed-Integer Linear Programming with C4.5 decision trees. Applied Soft Comput. J. 68:1–12.Crossref, Google Scholar
- (2017) Empirical decision model learning. Artificial Intelligence 244:343–367.Crossref, Google Scholar
- (2020) Optimization of tree ensembles. Oper. Res. 68(5):1605–1624.Link, Google Scholar
- Murzakhanov I, Venzke A, Misyris GS, Chatzivasileiadis S (2022) Neural networks for encoding dynamic security-constrained optimal power flow. Preprint, submitted March 17, https://arxiv.org/abs/2003.07939.Google Scholar
- National Cancer Institute (2021) Treatment clinical trials for gastric (stomach) cancer. Accessed October 26, 2021, https://www.cancer.gov/about-cancer/treatment/clinical-trials/disease/stomach-cancer/treatment.Google Scholar
- (2019) Synthesis of mathematical programming models with one-class evolutionary strategies. Swarm Evolutionary Comput. 44:335–348.Crossref, Google Scholar
- (2019) Synthesis of constraints for mathematical programming with one-class genetic programming. IEEE Trans. Evolutionary Comput. 23(1):117–129.Crossref, Google Scholar
- (2021) Ellipsoidal one-class constraint acquisition for quadratically constrained programming. Eur. J. Oper. Res. 293(1):36–49.Crossref, Google Scholar
- (2021) The nutritious supply chain: Optimizing humanitarian food assistance. INFORMS J. Optim. 3(2):200–226.Link, Google Scholar
- (2019) Deterministic global optimization with artificial neural networks embedded. J. Optim. Theory Appl. 180(3):925–948.Crossref, Google Scholar
- (2017) Estimating individual treatment effect: Generalization bounds and algorithms. Proc. Internat. Conf. on Machine Learn. (PMLR, New York), 3076–3085.Google Scholar
- (2008) The Algorithm Design Manual, 2nd ed. (Springer Publishing Company, Berlin).Crossref, Google Scholar
- (2020) From decision trees and neural networks to MILP: Power system optimization considering dynamic stability constraints. Proc. Eur. Control Conf. (IEEE, Piscataway, NJ), 594–594.Google Scholar
- (2018) One-class constraint acquisition with local search. Proc. Genetic and Evolutionary Comput. Conf. (Association for Computing Machinery, New York), 363–370.Google Scholar
- (2017) Data-driven security-constrained OPF. Proc. 10th Bulk Power Systems Dynamic Control Sympos. (IEEE, Piscataway, NJ), 1–10.Google Scholar
- (2017) Auction optimization using regression trees and linear models as integer programs. Artificial Intelligence 244:368–395.Crossref, Google Scholar
- (2011) Survival of metastatic gastric cancer: Significance of age, sex and race/ethnicity. J. Gastrointestinal Oncology 2(2):77–84.Google Scholar

