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Integer Optimization

mixed-integer (nonlinear) programming; nonconvex optimization; exact linear programming; cutting planes; branch-and-bound; SCIP Optimization Suite

17
6
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We develop theory and solution methodologies for linear and nonlinear mixed-integer optimization programs (MIPs). Our primary focus lies in enhancing the efficiency of integer and spatial branch-and-bound algorithms, which provide a general-purpose approach to finding provable global optima and coordinate within one framework components such as relaxations, cutting planes, primal heuristics, presolving procedures, and other solving techniques. Our group works on all these aspects and their interaction within the constraint integer programming solver SCIP. We also work on algorithms combining satisfiability and MIP techniques such as conflict analysis and domain propagation; on exact MIP and linear programming, guaranteeing that solutions are not invalidated by floating-point arithmetic errors; and on polynomial optimization problems.

🧑‍🎓 Members

Sebastian Pokutta
Department Head
pokutta (at) zib.de
Ksenia Bestuzheva
Research Area Lead
bestuzheva (at) zib.de
Suresh Bolusani
bolusani (at) zib.de
Mohammed Ghannam
ghannam (at) zib.de
Gioni Mexi
mexi (at) zib.de
Matea Miskovic
miskovic (at) zib.de
Leon Eifler
eifler (at) zib.de
Julian Manns
manns (at) zib.de
Alexander Hoen
hoen (at) zib.de
Antonia Chmiela
chmiela (at) zib.de
Deborah Hendrych
hendrych (at) zib.de
Stefan Vigerske
vigerske (at) zib.de
Liding Xu
liding.xu (at) zib.de
Patricia Ebert
ebert (at) zib.de
Lara Glessen
glessen (at) zib.de
Fritz Geis
geis (at) zib.de
Anuj Chandak
anuj.chandak.iitdelhi.cse (at) gmail.com

🔬 Projects

Convex Solver Adaptivity for Mixed-integer Optimization

We will investigate mixed-integer optimization with convex objectives using error-adaptive convex solvers in branch-and-bound. Focusing on improving lower bounds and balancing computational costs, we aim to develop a faster branch-and-bound methodology by leveraging modern MILP techniques and error-adaptive methods. Key aspects include warm-starting and controlled inexactness in early termination.

MATH+ AA3-15
Apr 2023 to Mar 2026
4
2
Research Campus MODAL SynLab

SynLab researches mathematical generalization of application-specific advances achieved in the Gas-, Rail– and MedLab of the research campus MODAL. The focus is on exact methods for solving a broad class of discrete-continuous optimization problems. This requires advanced techniques for structure recognition, consideration of nonlinear restrictions from practice, and the efficient implementation of mathematical algorithms on modern computer architectures. The results are bundled in a professional software package and complemented by a range of high-performance methods for specific applications with a high degree of innovation.

SynLab
Apr 2020 to Mar 2025
12
51
MiniMIP: a Faster, More Reliable, and Easier Way to Maintain and Solve MIP Problems

MiniMIP is an open source, machine learning oriented Mixed-Integer Programming (MIP) solver. We provide a range of interfaces for all aspects of solving MIPs (e.g. heuristics, cut generators, LP solvers), supplying users with a constant view of the internal state and allowing them to propose modifications that are integrated into the global state internally.

miniMIP
Jan 2021 to Dec 2023
2
Learning to Schedule Heuristics in IP

Heuristics play a crucial role in exact solvers for Mixed Integer Programming (MIP). However, the question of how to manage multiple MIP heuristics in a solver has not received sufficient attention. This project addresses the strategic management of primal heuristics in MIP solvers, aiming to replace static, hard-coded rules with dynamic, self-improving procedures.

HLEARN
Nov 2021 to Oct 2023
2
2
Adaptive Algorithms Through Machine Learning: Exploiting Interactions in Integer Programming

The performance of modern mixed-integer program solvers is highly dependent on a number of interdependent individual components. Using tools from machine learning, we intend to develop an integrated framework that is able to capture interactions of individual decisions made in these components with the ultimate goal to improve performance.

MATH+ EF1-9
Jan 2021 to Dec 2022
5
3
Globally Optimal Neural Network Training

Training artificial neural networks is a key optimization task in deep learning. To improve generalization, robustness, and explainability, we aim to compute globally optimal solutions. We will use integer programming methods, exploiting mixed-integer nonlinear programming and enhancing solving techniques like spatial branch-and-cut. Additionally, we'll leverage symmetry to reduce computational burden and ensure symmetry in solutions, and incorporate true sparsity using a mixed-integer nonlinear programming framework.

GONNT
Mar 2021 to Feb 2022
2

đź’¬ Talks and posters

Poster presentations

May 2022
Monoidal Strengthening for Intersection Cuts Using Maximal Quadratic-Free Sets by Antonia Chmiela
MIP Workshop
Dec 2021
Learning to Schedule Heuristics in Branch-and-Bound by Antonia Chmiela
NeurIPS Conference

đź“ť Publications and preprints

  1. Bolusani, S., Besançon, M., Bestuzheva, K., Chmiela, A., Dionísio, J., Donkiewicz, T., van Doornmalen, J., Eifler, L., Ghannam, M., Gleixner, A., Graczyk, C., Halbig, K., Hedtke, I., Hoen, A., Hojny, C., van der Hulst, R., Kamp, D., Koch, T., Kofler, K., … Xu, L. (2024). The SCIP Optimization Suite 9.0 (ZIB Report No. 24-02-29). Zuse Institute Berlin. [URL] [arXiv] [code]
    [BibTeX]
    @techreport{BolusaniEtal2024,
  year = {2024},
  institution = {Zuse Institute Berlin},
  type = {ZIB Report},
  month = feb,
  number = {24-02-29},
  url = {https://nbn-resolving.org/urn:nbn:de:0297-zib-95528},
  archiveprefix = {arXiv},
  eprint = {2402.17702},
  primaryclass = {math.OC},
  author = {Bolusani, Suresh and Besançon, Mathieu and Bestuzheva, Ksenia and Chmiela, Antonia and Dionísio, João and Donkiewicz, Tim and van Doornmalen, Jasper and Eifler, Leon and Ghannam, Mohammed and Gleixner, Ambros and Graczyk, Christoph and Halbig, Katrin and Hedtke, Ivo and Hoen, Alexander and Hojny, Christopher and van der Hulst, Rolf and Kamp, Dominik and Koch, Thorsten and Kofler, Kevin and Lentz, Jurgen and Manns, Julian and Mexi, Gioni and Mühmer, Erik and Pfetsch, Marc and Schlösser, Franziska and Serrano, Felipe and Shinano, Yuji and Turner, Mark and Vigerske, Stefan and Weninger, Dieter and Xu, Liding},
  title = {The SCIP Optimization Suite 9.0},
  code = {https://scipopt.org}
}
  2. Bestuzheva, K., Gleixner, A., and Achterberg, T. (2024). Efficient Separation of RLT Cuts for Implicit and Explicit Bilinear Terms. Mathematical Programming. DOI: https://doi.org/10.1007/s10107-024-02104-0 [arXiv]
    [BibTeX]
    @article{BestuzhevaGleixnerAchterberg2024_EfficientSeparationOfRLTCuts,
  year = {2024},
  journal = {Mathematical Programming},
  doi = {https://doi.org/10.1007/s10107-024-02104-0},
  archiveprefix = {arXiv},
  eprint = {2211.13545},
  primaryclass = {math.OC},
  author = {Bestuzheva, Ksenia and Gleixner, Ambros and Achterberg, Tobias},
  title = {Efficient Separation of RLT Cuts for Implicit and Explicit Bilinear Terms}
}
  3. Bolusani, S., Besançon, M., Gleixner, A., Berthold, T., D’Ambrosio, C., Muñoz, G., Paat, J., and Thomopulos, D. (2024). The MIP Workshop 2023 Computational Competition on Reoptimization. Mathematical Programming Computation. DOI: 10.1007/s12532-024-00256-w [arXiv]
    [BibTeX]
    @article{BolusaniEtal2023_TheMIP,
  year = {2024},
  journal = {Mathematical Programming Computation},
  doi = {10.1007/s12532-024-00256-w},
  archiveprefix = {arXiv},
  eprint = {2311.14834},
  primaryclass = {math.OC},
  author = {Bolusani, Suresh and Besançon, Mathieu and Gleixner, Ambros and Berthold, Timo and D'Ambrosio, Claudia and Muñoz, Gonzalo and Paat, Joseph and Thomopulos, Dimitri},
  title = {The MIP Workshop 2023 Computational Competition on Reoptimization}
}
  4. Borst, S., Eifler, L., and Gleixner, A. (2024). Certified Constraint Propagation and Dual Proof Analysis in a Numerically Exact MIP Solver. [arXiv]
    [BibTeX]
    @misc{BorstEiflerGleixner2024_CertifiedConstraintPropagation,
  archiveprefix = {arXiv},
  eprint = {2403.13567},
  primaryclass = {math.OC},
  year = {2024},
  author = {Borst, Sander and Eifler, Leon and Gleixner, Ambros},
  title = {Certified Constraint Propagation and Dual Proof Analysis in a Numerically Exact MIP Solver}
}
  5. Eifler, L., and Gleixner, A. (2024). Safe and Verified Gomory Mixed Integer Cuts in a Rational MIP Framework. SIAM Journal on Optimization, 34(1), 742–763. DOI: 10.1137/23M156046X [URL]
    [BibTeX]
    @article{EiflerGleixner2023_Safeandverified,
  year = {2024},
  journal = {SIAM Journal on Optimization},
  volume = {34},
  number = {1},
  pages = {742-763},
  doi = {10.1137/23M156046X},
  note = {ZIB report 23-09},
  url = {https://nbn-resolving.org/urn:nbn:de:0297-zib-90159},
  author = {Eifler, Leon and Gleixner, Ambros},
  title = {Safe and Verified Gomory Mixed Integer Cuts in a Rational MIP Framework}
}
  6. Eifler, L., Witzig, J., and Gleixner, A. (2024). Branch and Cut for Partitioning a Graph Into a Cycle of Clusters. Proceedings of International Symposium on Combinatorial Optimization. DOI: 10.1007/978-3-031-60924-4_8 [arXiv]
    [BibTeX]
    @inproceedings{EiflerWitzigGleixner2024,
  year = {2024},
  booktitle = {Proceedings of International Symposium on Combinatorial Optimization},
  doi = {10.1007/978-3-031-60924-4_8},
  archiveprefix = {arXiv},
  eprint = {2401.08412},
  primaryclass = {math.OC},
  author = {Eifler, Leon and Witzig, Jakob and Gleixner, Ambros},
  title = {Branch and Cut for Partitioning a Graph Into a Cycle of Clusters}
}
  7. Ghannam, M., Mexi, G., Lam, E., and Gleixner, A. (2024). Branch and Price for the Length-constrained Cycle Partition Problem. Proceedings of INFORMS Optimization Society Conference. [URL] [arXiv]
    [BibTeX]
    @inproceedings{GhannamMexiLamGleixner2024,
  year = {2024},
  booktitle = {Proceedings of INFORMS Optimization Society Conference},
  url = {https://sites.google.com/view/ios2024refereed},
  archiveprefix = {arXiv},
  eprint = {2401.17937},
  primaryclass = {math.OC},
  author = {Ghannam, Mohammed and Mexi, Gioni and Lam, Edward and Gleixner, Ambros},
  title = {Branch and Price for the Length-constrained Cycle Partition Problem}
}
  8. Halbig, K., Hoen, A., Gleixner, A., Witzig, J., and Weninger, D. (2024). A Diving Heuristic for Mixed-integer Problems with Unbounded Semi-continuous Variables. [arXiv]
    [BibTeX]
    @misc{HalbigHoenGleixnerWitzigWeninger2024_Adivingheuristic,
  archiveprefix = {arXiv},
  eprint = {2403.19411},
  primaryclass = {math.OC},
  year = {2024},
  author = {Halbig, Katrin and Hoen, Alexander and Gleixner, Ambros and Witzig, Jakob and Weninger, Dieter},
  title = {A Diving Heuristic for Mixed-integer Problems with Unbounded Semi-continuous Variables}
}
  9. Hoen, A., Kamp, D., and Gleixner, A. (2024). MIP-DD: A Delta Debugger for Mixed Integer Programming Solvers. [arXiv]
    [BibTeX]
    @misc{HoenKampGleixner2024_Adeltadebugger,
  archiveprefix = {arXiv},
  eprint = {2405.19770},
  primaryclass = {math.OC},
  year = {2024},
  author = {Hoen, Alexander and Kamp, Dominik and Gleixner, Ambros},
  title = {MIP-DD: A Delta Debugger for Mixed Integer Programming Solvers}
}
  10. Hoen, A., Oertel, A., Gleixner, A., and Nordström, J. (2024). Certifying MIP-based Presolve Reductions for 0-1 Integer Linear Programs. Proceedings of International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, 310–328. DOI: 10.1007/978-3-031-60597-0_20 [arXiv]
    [BibTeX]
    @inproceedings{HoenOertelGleixnerNordstroem2024,
  year = {2024},
  booktitle = {Proceedings of International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research},
  pages = {310-328},
  doi = {10.1007/978-3-031-60597-0_20},
  archiveprefix = {arXiv},
  eprint = {2401.09277},
  primaryclass = {math.OC},
  author = {Hoen, Alexander and Oertel, Andy and Gleixner, Ambros and Nordström, Jakob},
  title = {Certifying MIP-based Presolve Reductions for 0-1 Integer Linear Programs}
}
  11. Sharma, K., Hendrych, D., Besançon, M., and Pokutta, S. (2024). Network Design for the Traffic Assignment Problem with Mixed-Integer Frank-Wolfe. Proceedings of INFORMS Optimization Society Conference. [arXiv]
    [BibTeX]
    @inproceedings{SharmaNetworkDesignTrafficFrankWolfe24,
  year = {2024},
  booktitle = {Proceedings of INFORMS Optimization Society Conference},
  archiveprefix = {arXiv},
  eprint = {2402.00166},
  primaryclass = {math.OC},
  author = {Sharma, Kartikey and Hendrych, Deborah and Besançon, Mathieu and Pokutta, Sebastian},
  title = {Network Design for the Traffic Assignment Problem with Mixed-Integer Frank-Wolfe}
}
  12. Hendrych, D., Besançon, M., and Pokutta, S. (2024). Solving the Optimal Experiment Design Problem with Mixed-integer Convex Methods. Proceedings of Symposium on Experimental Algorithms. DOI: 10.4230/LIPIcs.SEA.2024.16 [arXiv] [code]
    [BibTeX]
    @inproceedings{design_of_experiments_boscia_23,
  year = {2024},
  booktitle = {Proceedings of Symposium on Experimental Algorithms},
  doi = {10.4230/LIPIcs.SEA.2024.16},
  archiveprefix = {arXiv},
  eprint = {2312.11200},
  primaryclass = {math.OC},
  author = {Hendrych, Deborah and Besançon, Mathieu and Pokutta, Sebastian},
  title = {Solving the Optimal Experiment Design Problem with Mixed-integer Convex Methods},
  code = {https://github.com/ZIB-IOL/OptimalDesignWithBoscia}
}
  13. Göß, A., Martin, A., Pokutta, S., and Sharma, K. (2024). Norm-induced Cuts: Optimization with Lipschitzian Black-box Functions. [URL] [arXiv]
    [BibTeX]
    @misc{gmps_nic_23,
  url = {https://opus4.kobv.de/opus4-trr154/files/518/nic_preprint.pdf},
  archiveprefix = {arXiv},
  eprint = {2403.11546},
  primaryclass = {math.OC},
  year = {2024},
  author = {Göß, Adrian and Martin, Alexander and Pokutta, Sebastian and Sharma, Kartikey},
  title = {Norm-induced Cuts: Optimization with Lipschitzian Black-box Functions}
}
  14. Mexi, G., Shamsi, S., Besançon, M., and le Bodic, P. (2024). Probabilistic Lookahead Strong Branching Via a Stochastic Abstract Branching Model. Proceedings of International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research. [arXiv]
    [BibTeX]
    @inproceedings{probabilistic_lookahead_23,
  year = {2024},
  booktitle = {Proceedings of International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research},
  archiveprefix = {arXiv},
  eprint = {2312.07041},
  primaryclass = {math.OC},
  author = {Mexi, Gioni and Shamsi, Somayeh and Besançon, Mathieu and le Bodic, Pierre},
  title = {Probabilistic Lookahead Strong Branching Via a Stochastic Abstract Branching Model}
}
  15. Tjusila, G., Besançon, M., Turner, M., and Koch, T. (2024). How Many Clues To Give? A Bilevel Formulation For The Minimum Sudoku Clue Problem. Operations Research Letters. DOI: 10.1016/j.orl.2024.107105 [URL] [arXiv]
    [BibTeX]
    @article{sudoku_clues_23,
  year = {2024},
  journal = {Operations Research Letters},
  doi = {10.1016/j.orl.2024.107105},
  url = {https://sciencedirect.com/science/article/abs/pii/S0167637724000415},
  archiveprefix = {arXiv},
  eprint = {2305.01697},
  primaryclass = {math.OC},
  author = {Tjusila, Gennesaret and Besançon, Mathieu and Turner, Mark and Koch, Thorsten},
  title = {How Many Clues To Give? A Bilevel Formulation For The Minimum Sudoku Clue Problem.}
}
  16. Berthold, T., Mexi, G., and Salvagnin, D. (2023). Using Multiple Reference Vectors and Objective Scaling in the Feasibility Pump. EURO Journal on Computational Optimization, 11. DOI: 10.1016/j.ejco.2023.100066
    [BibTeX]
    @article{BertholdMexiSalvagnin2023,
  year = {2023},
  journal = {EURO Journal on Computational Optimization},
  volume = {11},
  doi = {10.1016/j.ejco.2023.100066},
  author = {Berthold, Timo and Mexi, Gioni and Salvagnin, Domenico},
  title = {Using Multiple Reference Vectors and Objective Scaling in the Feasibility Pump}
}
  17. Bestuzheva, K., Besançon, M., Chen, W.-K., Chmiela, A., Donkiewicz, T., van Doornmalen, J., Eifler, L., Gaul, O., Gamrath, G., Gleixner, A., Gottwald, L., Graczyk, C., Halbig, K., Hoen, A., Hojny, C., van der Hulst, R., Koch, T., Lübbecke, M., Maher, S. J., … Witzig, J. (2023). Enabling Research Through the SCIP Optimization Suite 8.0. ACM Transactions on Mathematical Software. DOI: 10.1145/3585516 [arXiv]
    [BibTeX]
    @article{BestuzhevaBesanconEtal2023,
  year = {2023},
  journal = {ACM Transactions on Mathematical Software},
  doi = {10.1145/3585516},
  archiveprefix = {arXiv},
  eprint = {2303.07101},
  primaryclass = {math.OC},
  author = {Bestuzheva, Ksenia and Besançon, Mathieu and Chen, Wei-Kun and Chmiela, Antonia and Donkiewicz, Tim and van Doornmalen, Jasper and Eifler, Leon and Gaul, Oliver and Gamrath, Gerald and Gleixner, Ambros and Gottwald, Leona and Graczyk, Christoph and Halbig, Katrin and Hoen, Alexander and Hojny, Christopher and van der Hulst, Rolf and Koch, Thorsten and Lübbecke, Marco and Maher, Stephen J. and Matter, Frederic and Mühmer, Erik and Müller, Benjamin and Pfetsch, Marc and Rehfeldt, Daniel and Schlein, Steffan and Schlösser, Franziska and Serrano, Felipe and Shinano, Yuji and Sofranac, Boro and Turner, Mark and Vigerske, Stefan and Wegscheider, Fabian and Wellner, Philipp and Weninger, Dieter and Witzig, Jakob},
  title = {Enabling Research Through the SCIP Optimization Suite 8.0}
}
  18. Bestuzheva, K., Chmiela, A., MĂĽller, B., Serrano, F., Vigerske, S., and Wegscheider, F. (2023). Global Optimization of Mixed-integer Nonlinear Programs with SCIP 8.0. Journal of Global Optimization. DOI: 10.1007/s10898-023-01345-1 [URL] [arXiv]
    [BibTeX]
    @article{BestuzhevaChmielaMueller2023_GlobalOptimizationOfMixedInteger,
  year = {2023},
  journal = {Journal of Global Optimization},
  doi = {10.1007/s10898-023-01345-1},
  note = {ZIB report 23-01},
  url = {https://nbn-resolving.org/urn:nbn:de:0297-zib-89348},
  archiveprefix = {arXiv},
  eprint = {2301.00587},
  primaryclass = {math.OC},
  author = {Bestuzheva, Ksenia and Chmiela, Antonia and MĂĽller, Benjamin and Serrano, Felipe and Vigerske, Stefan and Wegscheider, Fabian},
  title = {Global Optimization of Mixed-integer Nonlinear Programs with SCIP 8.0}
}
  19. Bestuzheva, K., Gleixner, A., and Achterberg, T. (2023). Efficient Separation of RLT Cuts for Implicit and Explicit Bilinear Products. Proceedings of Conference on Integer Programming and Combinatorial Optimization, 14–28. DOI: 10.1007/978-3-031-32726-1_2 [arXiv]
    [BibTeX]
    @inproceedings{BestuzhevaGleixnerAchterberg2023_EfficientSeparationOfRLT,
  year = {2023},
  booktitle = {Proceedings of Conference on Integer Programming and Combinatorial Optimization},
  pages = {14-28},
  doi = {10.1007/978-3-031-32726-1_2},
  archiveprefix = {arXiv},
  eprint = {2211.13545},
  primaryclass = {math.OC},
  author = {Bestuzheva, Ksenia and Gleixner, Ambros and Achterberg, Tobias},
  title = {Efficient Separation of RLT Cuts for Implicit and Explicit Bilinear Products}
}
  20. Bestuzheva, K., Gleixner, A., and Vigerske, S. (2023). A Computational Study of Perspective Cuts. Mathematical Programming Computation, 15, 703–731. DOI: 10.1007/s12532-023-00246-4 [URL] [arXiv]
    [BibTeX]
    @article{BestuzhevaGleixnerVigerske2023,
  year = {2023},
  journal = {Mathematical Programming Computation},
  volume = {15},
  pages = {703-731},
  doi = {10.1007/s12532-023-00246-4},
  note = {ZIB report 21-07},
  url = {https://nbn-resolving.org/urn:nbn:de:0297-zib-81821},
  archiveprefix = {arXiv},
  eprint = {2103.09573},
  primaryclass = {math.OC},
  author = {Bestuzheva, Ksenia and Gleixner, Ambros and Vigerske, Stefan},
  title = {A Computational Study of Perspective Cuts}
}
  21. Chmiela, A., Gleixner, A., Lichocki, P., and Pokutta, S. (2023). Online Learning for Scheduling MIP Heuristics. Proceedings of International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, 114–123. DOI: 10.1007/978-3-031-33271-5_8
    [BibTeX]
    @inproceedings{ChmielaGleixnerLichockiPokutta2023_OnlineLearning,
  year = {2023},
  booktitle = {Proceedings of International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research},
  pages = {114-123},
  doi = {10.1007/978-3-031-33271-5_8},
  author = {Chmiela, Antonia and Gleixner, Ambros and Lichocki, Pawel and Pokutta, Sebastian},
  title = {Online Learning for Scheduling MIP Heuristics}
}
  22. Chmiela, A., Muñoz, G., and Serrano, F. (2023). Monoidal Strengthening and Unique Lifting in MIQCPs. Proceedings of Conference on Integer Programming and Combinatorial Optimization. [URL]
    [BibTeX]
    @inproceedings{ChmielaMunozSerrano2023_Monoidal,
  year = {2023},
  booktitle = {Proceedings of Conference on Integer Programming and Combinatorial Optimization},
  url = {https://gonzalomunoz.org/monoidalforMIQCP.pdf},
  author = {Chmiela, Antonia and Muñoz, Gonzalo and Serrano, Felipe},
  title = {Monoidal Strengthening and Unique Lifting in MIQCPs}
}
  23. Eifler, L., and Gleixner, A. (2023). A Computational Status Update for Exact Rational Mixed Integer Programming. Mathematical Programming, 197, 793–812. DOI: 10.1007/s10107-021-01749-5
    [BibTeX]
    @article{EiflerGleixner2023_Acomputational,
  year = {2023},
  journal = {Mathematical Programming},
  volume = {197},
  pages = {793-812},
  doi = {10.1007/s10107-021-01749-5},
  author = {Eifler, Leon and Gleixner, Ambros},
  title = {A Computational Status Update for Exact Rational Mixed Integer Programming}
}
  24. Ghannam, M., and Gleixner, A. (2023). Hybrid Genetic Search for Dynamic Vehicle Routing with Time Windows. Proceedings of Conference of the Society for Operations Research in Germany. [arXiv]
    [BibTeX]
    @inproceedings{HGSforDVRPTW,
  year = {2023},
  booktitle = {Proceedings of Conference of the Society for Operations Research in Germany},
  archiveprefix = {arXiv},
  eprint = {2307.11800},
  primaryclass = {cs.NE},
  author = {Ghannam, Mohammed and Gleixner, Ambros},
  title = {Hybrid Genetic Search for Dynamic Vehicle Routing with Time Windows}
}
  25. Gleixner, A., Gottwald, L., and Hoen, A. (2023). PaPILO: a Parallel Presolving Library for Integer and Linear Programming with Multiprecision Support. INFORMS Journal on Computing. DOI: 10.1287/ijoc.2022.0171 [arXiv]
    [BibTeX]
    @article{HoenGottwaldGleixner2023_PaPILO,
  year = {2023},
  journal = {INFORMS Journal on Computing},
  doi = {10.1287/ijoc.2022.0171},
  archiveprefix = {arXiv},
  eprint = {2206.10709},
  primaryclass = {math.OC},
  author = {Gleixner, Ambros and Gottwald, Leona and Hoen, Alexander},
  title = {PaPILO: a Parallel Presolving Library for Integer and Linear Programming with Multiprecision Support}
}
  26. Liberti, L., Iommazzo, G., Lavor, C., and Maculan, N. (2023). Cycle-based Formulations in Distance Geometry. Open Journal of Mathematical Optimization, 4(1). DOI: 10.5802/ojmo.18 [arXiv]
    [BibTeX]
    @article{LIL+23,
  year = {2023},
  journal = {Open Journal of Mathematical Optimization},
  volume = {4},
  number = {1},
  doi = {10.5802/ojmo.18},
  archiveprefix = {arXiv},
  eprint = {2006.11523},
  primaryclass = {math.OC},
  author = {Liberti, Leo and Iommazzo, Gabriele and Lavor, Carlile and Maculan, Nelson},
  title = {Cycle-based Formulations in Distance Geometry}
}
  27. Turner, M., Chmiela, A., Koch, T., and Winkler, M. (2023). PySCIPOpt-ML: Embedding Trained Machine Learning Models Into Mixed-integer Programs. [arXiv]
    [BibTeX]
    @misc{TurnerChmielaKochWinkler2023_EmbeddingMLinMIPs,
  archiveprefix = {arXiv},
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