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Pietro Belotti, Christian Kirches, Sven Leyffer, Jeff Linderoth, James Luedtke, and Ashutosh Mahajan. Mixed-integer nonlinear optimization. Acta Numerica, 22:1–131, 2013. doi:10.1017/S0962492913000032.

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Pietro Belotti, Jon Lee, Leo Liberti, François Margot, and Andreas Wächter. Branching and bounds tightening techniques for non-convex MINLP. Optimization Methods and Software, 24(4–5):597–634, 2009. doi:10.1080/10556780903087124.

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John T. Betts. Practical Methods for Optimal Control and Estimation Using Nonlinear Programming. SIAM, 2nd edition, 2010. ISBN 978-0-89871-688-7. doi:10.1137/1.9780898718577.

[Bie10]

Lorenz T. Biegler. Nonlinear Programming: Concepts, Algorithms, and Applications to Chemical Processes. SIAM, 2010. ISBN 978-0-89871-702-0. doi:10.1137/1.9780898719383.

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John R. Birge and François Louveaux. Introduction to Stochastic Programming. Springer Series in Operations Research and Financial Engineering. Springer, 2nd edition, 2011. doi:10.1007/978-1-4614-0237-4.

[BD20]

Julian Blank and Kalyanmoy Deb. Pymoo: Multi-objective optimization in Python. IEEE Access, 8:89497–89509, 2020. doi:10.1109/ACCESS.2020.2990567.

[BBC+22]

Mathieu Blondel, Quentin Berthet, Marco Cuturi, Roy Frostig, Stephan Hoyer, Felipe Llinares-López, Fabian Pedregosa, and Jean-Philippe Vert. Efficient and modular implicit differentiation. Advances in Neural Information Processing Systems, 35:5230–5242, 2022.

[BBC+08]

Pierre Bonami, Lorenz T. Biegler, Andrew R. Conn, Gérard Cornuéjols, Ignacio E. Grossmann, Carl D. Laird, Jon Lee, Andrea Lodi, François Margot, Nicolas Sawaya, and Andreas Wächter. An algorithmic framework for convex mixed integer nonlinear programs. Discrete Optimization, 5(2):186–204, 2008. doi:10.1016/j.disopt.2006.10.011.

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Stephen Boyd and Lieven Vandenberghe. Convex Optimization. Cambridge University Press, 2004. ISBN 978-0-521-83378-3.

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Michael R. Bussieck, Arne Stolbjerg Drud, and Alexander Meeraus. MINLPLib — A collection of test models for mixed-integer nonlinear programming. INFORMS Journal on Computing, 15(1):114–119, 2003. doi:10.1287/ijoc.15.1.114.15159.

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Pedro M. Castro. Tightening piecewise McCormick relaxations for bilinear problems. Computers & Chemical Engineering, 72:300–311, 2015. doi:10.1016/j.compchemeng.2014.03.025.

[CJH+22]

Federico Ceccon, Jordan Jalving, Joshua Haddad, Alexander Thebelt, Calvin Tsay, Carl D. Laird, and Ruth Misener. OMLT: optimization & machine learning toolkit. Journal of Machine Learning Research, 23(349):1–8, 2022.

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Federico Ceccon, John D. Siirola, and Ruth Misener. SUSPECT: MINLP special structure detector for Pyomo. TOP, 28(2):579–594, 2020. doi:10.1007/s11750-020-00552-3.

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Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, and David K. Duvenaud. Neural ordinary differential equations. In Advances in Neural Information Processing Systems (NeurIPS), volume 31. 2018.

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John W. Chinneck. Feasibility and Infeasibility in Optimization: Algorithms and Computational Methods. Volume 118 of International Series in Operations Research & Management Science. Springer, 2008. doi:10.1007/978-0-387-74932-7.

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Carleton Coffrin, Hassan L. Hijazi, and Pascal Van Hentenryck. The QC relaxation: a theoretical and computational study on optimal power flow. IEEE Transactions on Power Systems, 31(4):3008–3018, 2016. doi:10.1109/TPWRS.2015.2463111.

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Benoît Colson, Patrice Marcotte, and Gilles Savard. An overview of bilevel optimization. Annals of Operations Research, 153(1):235–256, 2007. doi:10.1007/s10479-007-0176-2.

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Alberto Costa and Giacomo Nannicini. RBFOpt: an open-source library for black-box optimization with costly function evaluations. Mathematical Programming Computation, 10(4):597–629, 2018. doi:10.1007/s12532-018-0144-7.

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Keely L. Croxton, Bernard Gendron, and Thomas L. Magnanti. A comparison of mixed-integer programming models for nonconvex piecewise linear cost minimization problems. Management Science, 49(9):1268–1273, 2003. doi:10.1287/mnsc.49.9.1268.16570.

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Stephan Dempe. Foundations of Bilevel Programming. Kluwer Academic, 2002. doi:10.1007/b101970.

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Elizabeth D. Dolan and Jorge J. Moré. Benchmarking optimization software with performance profiles. Mathematical Programming, 91(2):201–213, 2002. doi:10.1007/s101070100263.

[DMore02b]

Elizabeth D. Dolan and Jorge J. Moré. Benchmarking optimization software with performance profiles. Mathematical Programming, 91(2):201–213, 2002. doi:10.1007/s101070100263.

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Iain Dunning, Joey Huchette, and Miles Lubin. JuMP: a modeling language for mathematical optimization. SIAM Review, 59(2):295–320, 2017. doi:10.1137/15M1020575.

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Adam N. Elmachtoub and Paul Grigas. Smart “Predict, then Optimize”. Management Science, 68(1):9–26, 2022. doi:10.1287/mnsc.2020.3922.

[EPG+19]

David Eriksson, Michael Pearce, Jacob Gardner, Ryan D. Turner, and Matthias Poloczek. Scalable global optimization via local Bayesian optimization. In Advances in Neural Information Processing Systems 32, 5496–5507. 2019. URL: https://proceedings.neurips.cc/paper/2019/hash/6c990b7aca7bc7058f5e98ea909e924b-Abstract.html.

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Ignacio Fdez. Galván and Roland Lindh. Smooth things come in threes: a diabatic surrogate model for conical intersection optimization. Journal of Chemical Theory and Computation, 19(11):3418–3427, 2023. doi:10.1021/acs.jctc.3c00389.

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Nicholas I. M. Gould, Dominique Orban, and Philippe L. Toint. CUTEst: A constrained and unconstrained testing environment with safe threads for mathematical optimization. Computational Optimization and Applications, 60(3):545–557, 2015. doi:10.1007/s10589-014-9687-3.

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Ignacio E. Grossmann. Advanced Optimization for Process Systems Engineering. Cambridge University Press, 2021. ISBN 978-1-107-18920-5.

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H.-M. Gutmann. A radial basis function method for global optimization. Journal of Global Optimization, 19(3):201–227, 2001. doi:10.1023/A:1011255519438.

[HLW+17]

William E. Hart, Carl D. Laird, Jean-Paul Watson, David L. Woodruff, Gabriel A. Hackebeil, Bethany L. Nicholson, and John D. Siirola. Pyomo — Optimization Modeling in Python. Volume 67 of Springer Optimization and Its Applications. Springer, second edition, 2017. doi:10.1007/978-3-319-58821-6.

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Q. Huangfu and J. A. J. Hall. Parallelizing the dual revised simplex method. Mathematical Programming Computation, 10(1):119–142, 2018. doi:10.1007/s12532-017-0130-5.

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[JM21]

Donald R. Jones and Joaquim R. R. A. Martins. The DIRECT algorithm: 25 years later. Journal of Global Optimization, 79(3):521–566, 2021. doi:10.1007/s10898-020-00952-6.

[JSW98]

Donald R. Jones, Matthias Schonlau, and William J. Welch. Efficient global optimization of expensive black-box functions. Journal of Global Optimization, 13(4):455–492, 1998. doi:10.1023/A:1008306431147.

[KW94]

Peter Kall and Stein W. Wallace. Stochastic Programming. John Wiley and Sons, 1994.

[KK98]

George Karypis and Vipin Kumar. A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM Journal on Scientific Computing, 20(1):359–392, 1998. doi:10.1137/S1064827595287997.

[KLabbeLjubicS21]

Thomas Kleinert, Martine Labbé, Ivana Ljubić, and Martin Schmidt. A survey on mixed-integer programming techniques in bilevel optimization. EURO Journal on Computational Optimization, 9:100007, 2021. doi:10.1016/j.ejco.2021.100007.

[Kno06]

Joshua Knowles. ParEGO: A hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems. IEEE Transactions on Evolutionary Computation, 10(1):50–66, 2006. doi:10.1109/TEVC.2005.851274.

[KAA+11]

Thorsten Koch, Tobias Achterberg, Erling Andersen, Oliver Bastert, Timo Berthold, Robert E. Bixby, Emilie Danna, Gerald Gamrath, Ambros M. Gleixner, Stefan Heinz, and others. MIPLIB 2010. Springer, 2011. Discusses MIP-start and warm-starting strategies for Branch and Bound solvers. doi:10.1007/s12532-011-0025-9.

[KBG20]

Jan Kronqvist, David E. Bernal, and Ignacio E. Grossmann. Using regularization and second order information in outer approximation for convex MINLP. Mathematical Programming, 180:285–310, 2020. doi:10.1007/s10107-018-1356-3.

[KBLG19]

Jan Kronqvist, David E. Bernal, Andreas Lundell, and Ignacio E. Grossmann. A review and comparison of solvers for convex MINLP. Optimization and Engineering, 20:397–455, 2019. doi:10.1007/s11081-018-9411-8.

[KrogerCHN18]

Ole Kröger, Carleton Coffrin, Hassan Hijazi, and Harsha Nagarajan. Juniper: an open-source nonlinear branch-and-bound solver in Julia. Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR), pages 377–386, 2018. doi:10.1007/978-3-319-93031-2_27.

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Gilbert Laporte and François V. Louveaux. The integer L-shaped method for stochastic integer programs with complete recourse. Operations Research Letters, 13(3):133–142, 1993. doi:10.1016/0167-6377(93)90002-X.

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Sangbum Lee and Ignacio E. Grossmann. New algorithms for nonlinear generalized disjunctive programming. Computers & Chemical Engineering, 24(9–10):2125–2141, 2000. doi:10.1016/S0098-1354(00)00581-0.

[LCMartinez08]

Benjamin G. Levine, Joshua D. Coe, and Todd J. Martínez. Optimizing conical intersections without derivative coupling vectors: application to multistate multireference second-order perturbation theory (MS-CASPT2). The Journal of Physical Chemistry B, 112(2):405–413, 2008. doi:10.1021/jp0761618.

[LSRohrMitric19]

Joachim O. Lindner, Karina Sultangaleeva, Merle I. S. Röhr, and Roland Mitrić. metaFALCON: A program package for automatic sampling of conical intersection seams using multistate metadynamics. Journal of Chemical Theory and Computation, 15(6):3450–3460, 2019. doi:10.1021/acs.jctc.9b00029.

[LS14]

Marco Locatelli and Fabio Schoen. Global Optimization: Theory, Algorithms, and Applications. SIAM, 2014. ISBN 978-1-611972-66-5. doi:10.1137/1.9781611972672.

[LDDG+23]

Miles Lubin, Oscar Dowson, Joaquim Dias Garcia, Joey Huchette, Benoît Legat, and Juan Pablo Vielma. JuMP 1.0: recent improvements to a modeling language for mathematical optimization. Mathematical Programming Computation, 15:581–589, 2023. doi:10.1007/s12532-023-00239-3.

[LAS+25]

Laurens R. Lueg, Victor Alves, Daniel Schicksnus, John R. Kitchin, Carl D. Laird, and Lorenz T. Biegler. A simultaneous approach for training neural differential-algebraic systems of equations. arXiv preprint arXiv:2504.04665, 2025. doi:10.48550/arXiv.2504.04665.

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Zhi-Quan Luo, Jong-Shi Pang, and Daniel Ralph. Mathematical Programs with Equilibrium Constraints. Cambridge University Press, 1996. doi:10.1017/CBO9780511983658.

[LKdP+16]

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