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Integer Optimization

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

15
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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
Gioni Mexi
Research Area Lead
mexi (at) zib.de
Suresh Bolusani
bolusani (at) zib.de
Mohammed Ghannam
ghannam (at) zib.de
Matea Miskovic
miskovic (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
Xu Liding
liding.xu (at) zib.de
Patricia Ebert
ebert (at) zib.de
Fritz Geis
geis (at) zib.de
Jacob von Holly-Ponientzietz
von.holly-ponientzietz (at) zib.de
JoĂŁo DionĂ­sio
dionisio (at) zib.de

🔬 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
13
53

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

Conference and workshop talks

Sep 2024
The Relax-and-Cut Framework in the SCIP Optimization Solver by Suresh Bolusani
OR Conference, Munich
Sep 2024
What Is New in the SCIP Optimization Suite 9.0 by Ksenia Bestuzheva
OR Conference, Munich
Jul 2024
Solving the Optimal Design Problem with Mixed-Integer Convex Methods by Deborah Hendrych
22nd Symposium on Experimental Algorithms (SEA), Vienna
Jul 2024
Generalized Convexity Applied to Branch-and-Bound Algorithms for MINLPs by Ksenia Bestuzheva
33rd European Conference on Operational Research (EURO), Copenhagen
Jul 2024
Recent Advances in the SCIP Optimization Solver by Suresh Bolusani
33rd European Conference on Operational Research (EURO), Copenhagen
Jul 2024
Generalized Resolution Conflict Analysis in MIP Solvers by Gioni Mexi
25th International Symposium on Mathematical Programming (ISMP), Montréal
Jul 2024
Polyhedrality Made Easy. Using General Cut Operators to Determine When Cut Closures Are Polyhedral by Antonia Chmiela
25th International Symposium on Mathematical Programming (ISMP), Montréal
Jul 2024
Certifying MIP-Based Presolve Reductions for 0-1 Integer Linear Programs by Alexander Hoen
25th International Symposium on Mathematical Programming (ISMP), Montréal
May 2024
Probabilistic Lookahead Strong Branching Via a Stochastic Abstract Branching Model by Gioni Mexi
21st CPAIOR Conference, Uppsala
May 2024
Certifying MIP-Based Presolve Reductions for 0-1 Integer Linear Programs by Alexander Hoen
21st CPAIOR Conference, Uppsala
Apr 2024
Updates in SCIP 9 by Gioni Mexi
SAP-ZIB-FAU Workshop, Walldorf
Mar 2024
Efficient Relax-and-Cut Separation in a Branch-and-Cut Solver by Suresh Bolusani
INFORMS Optimization Society Conference (IOS), Houston, TX
Jan 2024
Monoidal Strengthening and Unique Lifting in MIQCPs by Antonia Chmiela
Combinatorial Optimization Workshop (Aussois), Aussois
Nov 2023
Relax-and-Cut Framework-based Lagromory Separator in SCIP by Suresh Bolusani
ZIB-Siemens Workshop, Berlin
Nov 2023
Recent Advances in SCIP Optimization Suite by Alexander Hoen
ZIB-Siemens Workshop, Berlin
Sep 2023
Lagromory Separator in SCIP by Suresh Bolusani
SAP-ZIB-FAU Workshop, Walldorf
Sep 2023
SCIP Beyond 8.0 by Ksenia Bestuzheva
7th ZIB-IMI-ISM-NUS-RIKEN-MODAL Workshop, Berlin [PDF]
Sep 2023
Recent Advances in SCIP by Alexander Hoen
SAP-ZIB-FAU Workshop, Walldorf
Sep 2023
Structured Constrained Nonlinear Optimization with Frank-Wolfe Methods by Mathieu Besançon
TES - Mathematical Optimization for ML, Berlin
Sep 2023
Product and Factor Filtering for RLT for Bilinear and Mixed-Integer Problems by Ksenia Bestuzheva
OR Conference, Hamburg [PDF]
Aug 2023
Scylla: A Matrix-free Fix-Propagate-and-Project Heuristic for Mixed Integer Optimization by Suresh Bolusani
OR Conference, Hamburg
Aug 2023
Experiments on Sparsity & Sparsification in Cutting Plane Selection by Mathieu Besançon
OR Conference, Hamburg
Aug 2023
Improving Conflict Analysis in MIP Solvers by Pseudo-Boolean Reasoning by Gioni Mexi
29th CP Conference, Toronto [PDF]
Jul 2023
Improving Conflict Analysis in MIP Solvers by Pseudo-Boolean Reasoning by Gioni Mexi
14th Pragmatics of SAT international workshop [PDF]
Jul 2023
On Performance Variability in Pseudo-Boolean Solving and the Impact of Trivial Model Simplifications by Alexander Hoen
Pragmatics of SAT 2023
Jul 2023
Modelling of Piece-wise Linear Concave Constraints in Continous Covering Problems by Liding Xu
33rd European Conference on Operational Research (EURO), Copenhagen
Jun 2023
Efficient Separation of RLT Cuts for Implicit and Explicit Bilinear Products by Ksenia Bestuzheva
24th IPCO Conference, Madison [PDF]
Jun 2023
Recent Developments in SCIP by Suresh Bolusani
SIAM conference on optimization (SIAMOP)
Jun 2023
Monoidal Strengthening and Unique Lifting in MIQCPs by Antonia Chmiela
24th IPCO Conference, Madison
May 2023
Perspective Cuts for Generalized On/Off Constraints by Ksenia Bestuzheva
20th Mixed Integer Programming European Workshop (MIP) [PDF]
May 2023
MIPcc23: The MIP Workshop 2023 Computational Competition by Suresh Bolusani
20th Mixed Integer Programming European Workshop (MIP)
May 2023
Online Learning for Scheduling MIP Heuristics by Antonia Chmiela
ZIB-Siemens Workshop, Munich
May 2023
The MIP 2023 Computational Competition by Ambros Gleixner
ZIB-Siemens Workshop, Munich
May 2023
Online Learning for Scheduling MIP Heuristics by Antonia Chmiela
20th CPAIOR Conference
Feb 2023
Cutting Plane Selection with Analytic Centers by Mathieu Besançon
ROADEF Conference, Rennes
Jan 2023
Recent Advances in SCIP by Gioni Mexi
SAP-ZIB-FAU Workshop, Walldorf
Jan 2023
Online Learning for Scheduling MIP Heuristics by Antonia Chmiela
SAP-ZIB Workshop, Walldorf
Jan 2023
Recent Advances in SCIP by Alexander Hoen
SAP-ZIB-FAU Workshop, Walldorf
Jan 2023
Cutting Plane Selection with Analytic Centers by Mathieu Besançon
Combinatorial Optimization Workshop (Aussois), Aussois
Jan 2023
Tighter SONC Bounds for Polynomial Optimization Problems with Bounded Variable Domains by Ksenia Bestuzheva
Combinatorial Optimization Workshop (Aussois), Aussois [PDF]
Jan 2023
Talk by Antonia Chmiela
Combinatorial Optimization Workshop (Aussois), Aussois
Nov 2022
Strengthening Dual Bounds in Branch-and-Bound by SONC Certificates by Ksenia Bestuzheva
Let's SCIP it! (SCIP), Berlin [PDF]
Nov 2022
Monoidal Strengthening for Intersection Cuts Using Maximal Quadratic-Free Sets by Antonia Chmiela
Let's SCIP it! (SCIP), Berlin
Sep 2022
Strengthening SONC Relaxations with Constraints Derived From Variable Bounds by Ksenia Bestuzheva
HUGO 2022 – XV. Workshop on Global Optimization (HUGO 2022), Szeged [PDF]
Jul 2022
New Developments in the SCIP Optimization Suite 8 by Ksenia Bestuzheva
32nd European Conference on Operational Research (EURO), Espoo [PDF]
Jul 2022
Feasibility Pump Using Multiple Reference Vectors And New Scaling by Gioni Mexi
32nd European Conference on Operational Research (EURO), Espoo
Oct 2021
Talk by Antonia Chmiela
INFORMS Annual Meeting (INFORMS), Anaheim
Sep 2021
Recent Developments in SCIP by Ksenia Bestuzheva
5th ZIB-IMI-ISM-NUS-RIKEN-MODAL Workshop, Berlin [PDF]
Aug 2021
Solving MINLPs with SCIP by Ksenia Bestuzheva
22nd IFORS Conference [PDF]
Jul 2021
A Computational Study Of Perspective Cuts by Ksenia Bestuzheva
31st European Conference on Operational Research (EURO), Athens [PDF]
Jul 2021
Learning to Schedule Heuristics in Branch and Bound by Antonia Chmiela
31st European Conference on Operational Research (EURO), Athens
Jun 2021
Reformulation-Linearisation Technique for Implicit Bilinear Relations by Ksenia Bestuzheva
Mixed-Integer Nonlinear Programming Workshop (MINLP) [PDF]
May 2021
On the Implementation and Strengthening of Intersection Cuts for QCQPs by Antonia Chmiela
22nd IPCO Conference, online
Sep 2020
Mixed-Integer Nonlinear Programming by Ksenia Bestuzheva
4th Computational Optimization at Work (CO@Work), Berlin [PDF]
Jun 2020
Nonlinear Constraints in SCIP by Ksenia Bestuzheva
SCIP Workshop (SCIP), Berlin [PDF]

Research seminar talks

Nov 2024
New Perspectives on Invexity and Its Algorithmic Applications by Ksenia Bestuzheva
Group seminar KTH Royal Institute of Technology, Stockholm
Nov 2024
A Reformulation-Linearization Technique Framework for Problems with Bilinear Terms by Ksenia Bestuzheva
Discrete Optimization Talks, online
Nov 2024
Solving the Optimal Design Problem with Mixed-Integer Convex Methods by Deborah Hendrych
GHOST Research Seminar (GHOST), Grenoble
Oct 2024
New Perspectives on Invexity and Its Algorithmic Applications by Ksenia Bestuzheva
Group seminar Laboratoire d'Informatique de Paris-Nord, Paris
Jul 2024
Solving the Optimal Design Problem with Mixed-Integer Convex Methods by Deborah Hendrych
NASPDE Seminar, Berlin
May 2024
Solving the Optimal Design Problem with Mixed-Integer Convex Methods by Deborah Hendrych
MATH+ Spotlight talks, Berlin
Dec 2023
Solving the Optimal Experiment Design Problems with Mixed-Integer Frank-Wolfe-based Methods by Deborah Hendrych
IOL Research Seminar (IOL), Berlin
Nov 2023
Constrained Nonlinear Optimization with Frank-Wolfe by Mathieu Besançon
Group seminar Laboratoire Jean Kuntzmann, Grenoble
Mar 2023
Cutting Plane Selection with Analytic Centers by Mathieu Besançon
Department seminar, EDGE, Institut Mathématique de Bordeaux and Inria, Bordeaux
Mar 2023
Convex Optimization Techniques for Mixed-integer Nonlinear Problems by Mathieu Besançon
Department seminar, DĂ©partement d'Informatique de Polytechnique, Palaiseau
Feb 2023
Generalized Benders' Algorithm for Mixed Integer Bilevel Linear Optimization by Suresh Bolusani
Department seminar, Industrial Engineering and Operations Research, IIT Bombay

Poster presentations

May 2024
MIP-DD. A Delta Debugger for Mixed Integer Programming Solvers by Alexander Hoen
21st CPAIOR Conference, Uppsala
Apr 2024
Convex Solver Adaptivity for Mixed-Integer Optimization by Deborah Hendrych
5th Women in Optimization 2024 (WiO), Erlangen
Feb 2023
Learning to Schedule MIP Heuristics by Antonia Chmiela
IPAM Workshop on Deep Learning and Combinatorial Optimization
May 2022
Monoidal Strengthening for Intersection Cuts Using Maximal Quadratic-Free Sets by Antonia Chmiela
19th Mixed Integer Programming European Workshop (MIP)
Dec 2021
Learning to Schedule Heuristics in Branch-and-Bound by Antonia Chmiela
conference on neural information processing systems (NeurIPS)
May 2021
Learning to Schedule Heuristics in Branch-and-Bound by Antonia Chmiela
18th Mixed Integer Programming European Workshop (MIP)

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{2024_BolusaniEtAl_Scip9,
      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{2022_BestuzhevaGleixnerAchterberg_Rltcutsbilinear,
      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. Deza, A., Pokutta, S., and Pournin, L. (2024). The Complexity of Geometric Scaling. Operations Research Letters, 52. DOI: 10.1016/j.orl.2023.11.010 [arXiv]
    [BibTeX]
    @article{2022_DezaPokuttaPournin_Geometricscaling,
      year = {2024},
      journal = {Operations Research Letters},
      volume = {52},
      doi = {10.1016/j.orl.2023.11.010},
      archiveprefix = {arXiv},
      eprint = {2205.04063},
      primaryclass = {math.OC},
      author = {Deza, Antoine and Pokutta, Sebastian and Pournin, Lionel},
      title = {The Complexity of Geometric Scaling}
    }
  4. 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{2023_BolusaniEtAl_Mipreoptimization,
      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}
    }
  5. Chmiela, A., Muñoz, G., and Serrano, F. (2024). Monoidal Strengthening and Unique Lifting in MIQCPs. Mathematical Programming B. DOI: 10.1007/s10107-024-02112-0 [URL]
    [BibTeX]
    @article{2023_ChmielaMuozSerrano_Monoidalstrengthening,
      year = {2024},
      journal = {Mathematical Programming B},
      doi = {10.1007/s10107-024-02112-0},
      url = {https://gonzalomunoz.org/monoidalforMIQCP.pdf},
      author = {Chmiela, Antonia and Muñoz, Gonzalo and Serrano, Felipe},
      title = {Monoidal Strengthening and Unique Lifting in MIQCPs}
    }
  6. 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] [arXiv]
    [BibTeX]
    @article{2023_EiflerGleixner_Gomorycuts,
      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},
      archiveprefix = {arXiv},
      eprint = {2303.12365},
      primaryclass = {math.OC},
      author = {Eifler, Leon and Gleixner, Ambros},
      title = {Safe and Verified Gomory Mixed Integer Cuts in a Rational MIP Framework}
    }
  7. Eifler, L., Nicolas-Thouvenin, J., and Gleixner, A. (2024). Combining Precision Boosting with LP Iterative Refinement for Exact Linear Optimization. INFORMS Journal on Computing. DOI: 10.1007/s10107-024-02104-0 [arXiv]
    [BibTeX]
    @article{2023_EiflerNicolasGleixner_Precisionboostinglp,
      year = {2024},
      journal = {INFORMS Journal on Computing},
      doi = {10.1007/s10107-024-02104-0},
      archiveprefix = {arXiv},
      eprint = {2311.08037},
      primaryclass = {math.OC},
      author = {Eifler, Leon and Nicolas-Thouvenin, Jules and Gleixner, Ambros},
      title = {Combining Precision Boosting with LP Iterative Refinement for Exact Linear Optimization}
    }
  8. Hendrych, D., Besançon, M., and Pokutta, S. (2024). Solving the Optimal Experiment Design Problem with Mixed-integer Convex Methods. Proceedings of the Symposium on Experimental Algorithms. DOI: 10.4230/LIPIcs.SEA.2024.16 [arXiv] [code]
    [BibTeX]
    @inproceedings{2023_HendrychBesanconPokutta_Optimalexperimentdesign,
      year = {2024},
      booktitle = {Proceedings of the 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}
    }
  9. Mexi, G., Shamsi, S., Besançon, M., and le Bodic, P. (2024). Probabilistic Lookahead Strong Branching Via a Stochastic Abstract Branching Model. Proceedings of the International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research. [arXiv]
    [BibTeX]
    @inproceedings{2023_MexiSomayehBesanconPierre_Probabilisticstrongbranching,
      year = {2024},
      booktitle = {Proceedings of the 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}
    }
  10. Göß, A., Martin, A., Pokutta, S., and Sharma, K. (2024). Norm-induced Cuts: Optimization with Lipschitzian Black-box Functions. [URL] [arXiv]
    [BibTeX]
    @misc{2024_AdrianMartinPokuttaSharma_Norminducedcuts,
      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}
    }
  11. Bolusani, S., Mexi, G., Besançon, M., and Turner, M. (2024). A Multi-Reference Relaxation Enforced Neighborhood Search Heuristic in SCIP. [arXiv]
    [BibTeX]
    @misc{2024_BolusaniMexiBesanconTurner_Multireferenceneighborhoodsearch,
      archiveprefix = {arXiv},
      eprint = {2408.00718},
      primaryclass = {math.OC},
      year = {2024},
      author = {Bolusani, Suresh and Mexi, Gioni and Besançon, Mathieu and Turner, Mark},
      title = {A Multi-Reference Relaxation Enforced Neighborhood Search Heuristic in SCIP}
    }
  12. Borst, S., Eifler, L., and Gleixner, A. (2024). Certified Constraint Propagation and Dual Proof Analysis in a Numerically Exact MIP Solver. [arXiv]
    [BibTeX]
    @misc{2024_BorstEiflerGleixner_ConstraintpropagationDualproofMip,
      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}
    }
  13. Eifler, L., Witzig, J., and Gleixner, A. (2024). Branch and Cut for Partitioning a Graph Into a Cycle of Clusters. Proceedings of the International Symposium on Combinatorial Optimization. DOI: 10.1007/978-3-031-60924-4_8 [arXiv]
    [BibTeX]
    @inproceedings{2024_EiflerWitzigGleixner_Branchcutcycleclustering,
      year = {2024},
      booktitle = {Proceedings of the 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}
    }
  14. 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{2024_GennesaretBesanconTurnerKoch_Bilevelsudokuclue,
      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.}
    }
  15. Ghannam, M., Mexi, G., Lam, E., and Gleixner, A. (2024). Branch and Price for the Length-constrained Cycle Partition Problem. Proceedings of the INFORMS Optimization Society Conference. [URL] [arXiv]
    [BibTeX]
    @inproceedings{2024_GhannamMexiLamGleixner_Branchpricelccp,
      year = {2024},
      booktitle = {Proceedings of the 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}
    }
  16. 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{2024_HalbigEtAl_Divingheuristic,
      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}
    }
  17. Hoen, A., Kamp, D., and Gleixner, A. (2024). MIP-DD: A Delta Debugger for Mixed Integer Programming Solvers. [arXiv]
    [BibTeX]
    @misc{2024_HoenKampGleixner_Mipdebugger,
      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}
    }
  18. Hoen, A., Oertel, A., Gleixner, A., and Nordström, J. (2024). Certifying MIP-based Presolve Reductions for 0-1 Integer Linear Programs. Proceedings of the 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{2024_HoenOertelGleixnerNordstrm_Mippresolvecertification,
      year = {2024},
      booktitle = {Proceedings of the 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}
    }
  19. Mexi, G., Serrano, F., Berthold, T., Gleixner, A., and Nordström, J. (2024). Cut-based Conflict Analysis in Mixed Integer Programming. [arXiv]
    [BibTeX]
    @misc{2024_MexiSerranoBertholdGleixnerNordstroem_Cutbasedconflictanalysis,
      archiveprefix = {arXiv},
      eprint = {2410.15110},
      primaryclass = {math.OC},
      year = {2024},
      author = {Mexi, Gioni and Serrano, Felipe and Berthold, Timo and Gleixner, Ambros and Nordström, Jakob},
      title = {Cut-based Conflict Analysis in Mixed Integer Programming}
    }
  20. Sharma, K., Hendrych, D., Besançon, M., and Pokutta, S. (2024). Network Design for the Traffic Assignment Problem with Mixed-Integer Frank-Wolfe. Proceedings of the INFORMS Optimization Society Conference. [arXiv]
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    @inproceedings{2024_SharmaHendrychBesanconPokutta_NetworkdesignMicoFrankwolfe,
      year = {2024},
      booktitle = {Proceedings of the 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}
    }
  21. Xu, L., and D’Ambrosio, C. (2024). Formulations of the Continuous Set-covering Problem on Networks: a Comparative Study. [arXiv]
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    @misc{2024_XuDAmbrosio_FormulationsContinuousSetCovering,
      archiveprefix = {arXiv},
      eprint = {2404.10467},
      primaryclass = {math.OC},
      year = {2024},
      author = {Xu, Liding and D'Ambrosio, Claudia},
      title = {Formulations of the Continuous Set-covering Problem on Networks: a Comparative Study}
    }
  22. Xu, L., and Liberti, L. (2024). Relaxations for Binary Polynomial Optimization Via Signed Certificates. [arXiv]
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    @misc{2024_XuLiberti_RelaxationsBinaryPolynomial,
      archiveprefix = {arXiv},
      eprint = {2405.13447},
      primaryclass = {math.OC},
      year = {2024},
      author = {Xu, Liding and Liberti, Leo},
      title = {Relaxations for Binary Polynomial Optimization Via Signed Certificates}
    }
  23. 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]
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    @article{2020_LibertiIommazzoLavorMaculan_Cycledistancegeometry_1,
      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}
    }
  24. 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]
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    @article{2021_BestuzhevaGleixnerVigerske_Perspectivecuts,
      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}
    }
  25. Bestuzheva, K., Gleixner, A., and Achterberg, T. (2023). Efficient Separation of RLT Cuts for Implicit and Explicit Bilinear Terms. Proceedings of the Conference on Integer Programming and Combinatorial Optimization, 14–28. DOI: 10.1007/978-3-031-32726-1_2 [arXiv]
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    @inproceedings{2022_BestuzhevaGleixnerAchterberg_Rltcutsbilinear:1,
      year = {2023},
      booktitle = {Proceedings of the 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 Terms}
    }
  26. 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]
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    @article{2022_GleixnerGottwaldHoen_PapiloMultiprecision,
      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}
    }
  27. Turner, M., Berthold, T., Besançon, M., and Koch, T. (2023). Cutting Plane Selection with Analytic Centers and Multiregression. Proceedings of the International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research. [arXiv]
    [BibTeX]
    @inproceedings{2022_TurnerBertholdBesanconKoch_Cuttingplanemultiregression,
      year = {2023},
      booktitle = {Proceedings of the International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research},
      archiveprefix = {arXiv},
      eprint = {2212.07231},
      primaryclass = {math.OC},
      author = {Turner, Mark and Berthold, Timo and Besançon, Mathieu and Koch, Thorsten},
      title = {Cutting Plane Selection with Analytic Centers and Multiregression}
    }
  28. 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
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    @article{2023_BertholdMexiSalvagnin_Multiplereferencevectors,
      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}
    }
  29. 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]
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    @article{2023_BestuzhevaEtAl_GlobaloptimizationScip80,
      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}
    }
  30. 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]
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    @article{2023_BestuzhevaEtAl_ResearchScip,
      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}
    }
  31. Chmiela, A., Gleixner, A., Lichocki, P., and Pokutta, S. (2023). Online Learning for Scheduling MIP Heuristics. Proceedings of the International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, 114–123. DOI: 10.1007/978-3-031-33271-5_8 [arXiv]
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    @inproceedings{2023_ChmielaGleixnerLichockiPokutta_Onlinelearningscheduling,
      year = {2023},
      booktitle = {Proceedings of the 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},
      archiveprefix = {arXiv},
      eprint = {2304.03755},
      primaryclass = {math.OC},
      author = {Chmiela, Antonia and Gleixner, Ambros and Lichocki, Pawel and Pokutta, Sebastian},
      title = {Online Learning for Scheduling MIP Heuristics}
    }
  32. Chmiela, A., Muñoz, G., and Serrano, F. (2023). Monoidal Strengthening and Unique Lifting in MIQCPs. Proceedings of the Conference on Integer Programming and Combinatorial Optimization. [URL]
    [BibTeX]
    @inproceedings{2023_ChmielaMuozSerrano_Monoidalstrengthening:1,
      year = {2023},
      booktitle = {Proceedings of the 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}
    }
  33. 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 [arXiv]
    [BibTeX]
    @article{2023_EiflerGleixner_Exactrationalmip,
      year = {2023},
      journal = {Mathematical Programming},
      volume = {197},
      pages = {793-812},
      doi = {10.1007/s10107-021-01749-5},
      archiveprefix = {arXiv},
      eprint = {2101.09141},
      primaryclass = {math.OC},
      author = {Eifler, Leon and Gleixner, Ambros},
      title = {A Computational Status Update for Exact Rational Mixed Integer Programming}
    }
  34. Ghannam, M., and Gleixner, A. (2023). Hybrid Genetic Search for Dynamic Vehicle Routing with Time Windows. Proceedings of the Conference of the Society for Operations Research in Germany. [arXiv]
    [BibTeX]
    @inproceedings{2023_GhannamGleixner_Hybridgeneticsearch,
      year = {2023},
      booktitle = {Proceedings of the 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}
    }
  35. Mexi, G., Berthold, T., Gleixner, A., and Nordström, J. (2023). Improving Conflict Analysis in MIP Solvers by Pseudo-Boolean Reasoning. Proceedings of the 29th International Conference on Principles and Practice of Constraint Programming (CP 2023), 280, 27:1–27:19, DOI: 10.4230/LIPIcs.CP.2023.27 [arXiv]
    [BibTeX]
    @inproceedings{2023_MexiBertholdGleixnerNordstrm_ConflictanalysisPseudoboolean,
      year = {2023},
      booktitle = {Proceedings of the 29th International Conference on Principles and Practice of Constraint Programming (CP 2023)},
      volume = {280},
      pages = {27:1-27:19,},
      doi = {10.4230/LIPIcs.CP.2023.27},
      archiveprefix = {arXiv},
      eprint = {2307.14166},
      primaryclass = {math.OC},
      author = {Mexi, Gioni and Berthold, Timo and Gleixner, Ambros and Nordström, Jakob},
      title = {Improving Conflict Analysis in MIP Solvers by Pseudo-Boolean Reasoning}
    }
  36. Mexi, G., Besançon, M., Bolusani, S., Chmiela, A., Hoen, A., and Gleixner, A. (2023). Scylla: a Matrix-free Fix-propagate-and-project Heuristic for Mixed-integer Optimization. Proceedings of the Conference of the Society for Operations Research in Germany. [arXiv]
    [BibTeX]
    @inproceedings{2023_MexiEtAl_Scyllaheuristic,
      year = {2023},
      booktitle = {Proceedings of the Conference of the Society for Operations Research in Germany},
      archiveprefix = {arXiv},
      eprint = {2307.03466},
      primaryclass = {math.OC},
      author = {Mexi, Gioni and Besançon, Mathieu and Bolusani, Suresh and Chmiela, Antonia and Hoen, Alexander and Gleixner, Ambros},
      title = {Scylla: a Matrix-free Fix-propagate-and-project Heuristic for Mixed-integer Optimization}
    }
  37. Turner, M., Berthold, T., Besançon, M., and Koch, T. (2023). Branching Via Cutting Plane Selection: Improving Hybrid Branching. [arXiv]
    [BibTeX]
    @misc{2023_TurnerBertholdBesanconKoch_Branchingcuttingplane,
      archiveprefix = {arXiv},
      eprint = {2306.06050},
      primaryclass = {cs.RO},
      year = {2023},
      author = {Turner, Mark and Berthold, Timo and Besançon, Mathieu and Koch, Thorsten},
      title = {Branching Via Cutting Plane Selection: Improving Hybrid Branching}
    }
  38. Turner, M., Berthold, T., and Besançon, M. (2023). A Context-Aware Cutting Plane Selection Algorithm for Mixed-Integer Programming. Proceedings of the Conference of the Society for Operations Research in Germany. [arXiv]
    [BibTeX]
    @inproceedings{2023_TurnerBertholdBesancon_Contextawarecuttingplane,
      year = {2023},
      booktitle = {Proceedings of the Conference of the Society for Operations Research in Germany},
      archiveprefix = {arXiv},
      eprint = {2307.07322},
      primaryclass = {math.OC},
      author = {Turner, Mark and Berthold, Timo and Besançon, Mathieu},
      title = {A Context-Aware Cutting Plane Selection Algorithm for Mixed-Integer Programming}
    }
  39. Turner, M., Chmiela, A., Koch, T., and Winkler, M. (2023). PySCIPOpt-ML: Embedding Trained Machine Learning Models Into Mixed-integer Programs. [arXiv]
    [BibTeX]
    @misc{2023_TurnerChmielaKochMichael_Pyscipoptml,
      archiveprefix = {arXiv},
      eprint = {2312.08074},
      primaryclass = {math.OC},
      year = {2023},
      author = {Turner, Mark and Chmiela, Antonia and Koch, Thorsten and Winkler, Michael},
      title = {PySCIPOpt-ML: Embedding Trained Machine Learning Models Into Mixed-integer Programs}
    }
  40. van Doornmalen, J., Eifler, L., Gleixner, A., and Hojny, C. (2023). A Proof System for Certifying Symmetry and Optimality Reasoning in Integer Programming. [arXiv]
    [BibTeX]
    @misc{2023_vanEiflerGleixnerHojny_Proofsystemsymmetry,
      archiveprefix = {arXiv},
      eprint = {2311.03877},
      primaryclass = {math.OC},
      year = {2023},
      author = {van Doornmalen, Jasper and Eifler, Leon and Gleixner, Ambros and Hojny, Christopher},
      title = {A Proof System for Certifying Symmetry and Optimality Reasoning in Integer Programming}
    }
  41. Gasse, M., Bowly, S., Cappart, Q., Charfreitag, J., Charlin, L., Chételat, D., Chmiela, A., Dumouchelle, J., Gleixner, A., Kazachkov, A. M., Khalil, E., Lichocki, P., Lodi, A., Lubin, M., Maddison, C. J., Christopher, M., Papageorgiou, D. J., Parjadis, A., Pokutta, S., … Kun, M. (2022). The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights. Proceedings of the Conference on Neural Information Processing Systems, 176, 220–231. [URL] [arXiv]
    [BibTeX]
    @inproceedings{2022_GasseEtAl_Ml4coinsights,
      year = {2022},
      booktitle = {Proceedings of the Conference on Neural Information Processing Systems},
      month = jun,
      volume = {176},
      pages = {220–231},
      url = {https://proceedings.mlr.press/v176/gasse22a.html},
      archiveprefix = {arXiv},
      eprint = {2203.02433},
      primaryclass = {cs.LG},
      author = {Gasse, Maxime and Bowly, Simon and Cappart, Quentin and Charfreitag, Jonas and Charlin, Laurent and Chételat, Didier and Chmiela, Antonia and Dumouchelle, Justin and Gleixner, Ambros and Kazachkov, Aleksandr M. and Khalil, Elias and Lichocki, Pawel and Lodi, Andrea and Lubin, Miles and Maddison, Chris J. and Christopher, Morris and Papageorgiou, Dimitri J. and Parjadis, Augustin and Pokutta, Sebastian and Prouvost, Antoine and Scavuzzo, Lara and Zarpellon, Giulia and Yang, Linxin and Lai, Sha and Wang, Akang and Luo, Xiaodong and Zhou, Xiang and Huang, Haohan and Shao, Shengcheng and Zhu, Yuanming and Zhang, Dong and Quan, Tao and Cao, Zixuan and Xu, Yang and Huang, Zhewei and Zhou, Shuchang and Binbin, Chen and Minggui, He and Hao, Hao and Zhiyu, Zhang and Zhiwu, An and Kun, Mao},
      title = {The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights}
    }
  42. Sofranac, B., Gleixner, A., and Pokutta, S. (2022). Accelerating Domain Propagation: An Efficient GPU-parallel Algorithm Over Sparse Matrices. Parallel Computing, 109, 102874. DOI: 10.1016/j.parco.2021.102874 [arXiv] [summary]
    [BibTeX]
    @article{2020_SofranacGleixnerPokutta_Domainpropagation,
      year = {2022},
      journal = {Parallel Computing},
      volume = {109},
      pages = {102874},
      doi = {10.1016/j.parco.2021.102874},
      archiveprefix = {arXiv},
      eprint = {2009.07785},
      primaryclass = {cs.DC},
      author = {Sofranac, Boro and Gleixner, Ambros and Pokutta, Sebastian},
      title = {Accelerating Domain Propagation: An Efficient GPU-parallel Algorithm Over Sparse Matrices},
      summary = {https://pokutta.com/blog/research/2020/09/20/gpu-prob.html}
    }
  43. Bolusani, S., and Ralphs, T. K. (2022). A Framework for Generalized Benders’ Decomposition and Its Applications to Multilevel Optimization. Mathematical Programming, 196, 389–426. DOI: 10.1007/s10107-021-01763-7 [arXiv]
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    @article{2021_BolusaniRalphs_Generalizedbendersdecomposition,
      year = {2022},
      journal = {Mathematical Programming},
      volume = {196},
      pages = {389-426},
      doi = {10.1007/s10107-021-01763-7},
      archiveprefix = {arXiv},
      eprint = {2104.06496},
      primaryclass = {math.OC},
      author = {Bolusani, Suresh and Ralphs, Ted K.},
      title = {A Framework for Generalized Benders' Decomposition and Its Applications to Multilevel Optimization}
    }
  44. Sofranac, B., Gleixner, A., and Pokutta, S. (2022). An Algorithm-independent Measure of Progress for Linear Constraint Propagation. Constraints, 27, 432–455. DOI: 10.1007/s10601-022-09338-9 [arXiv]
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    @article{2021_SofranacGleixnerPokutta_Progressmeasure,
      year = {2022},
      journal = {Constraints},
      volume = {27},
      pages = {432-455},
      doi = {10.1007/s10601-022-09338-9},
      archiveprefix = {arXiv},
      eprint = {2106.07573},
      primaryclass = {math.OC},
      author = {Sofranac, Boro and Gleixner, Ambros and Pokutta, Sebastian},
      title = {An Algorithm-independent Measure of Progress for Linear Constraint Propagation}
    }
  45. Chmiela, A., Muñoz, G., and Serrano, F. (2022). On the Implementation and Strengthening of Intersection Cuts for QCQPs. Mathematical Programming B, 197, 549–586. DOI: 10.1007/s10107-022-01808-5
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    @article{2022_ChmielaMuozSerrano_Intersectioncutsqcqps,
      year = {2022},
      journal = {Mathematical Programming B},
      volume = {197},
      pages = {549-586},
      doi = {10.1007/s10107-022-01808-5},
      author = {Chmiela, Antonia and Muñoz, Gonzalo and Serrano, Felipe},
      title = {On the Implementation and Strengthening of Intersection Cuts for QCQPs}
    }
  46. Eifler, L., Gleixner, A., and Pulaj, J. (2022). A Safe Computational Framework for Integer Programming Applied to Chvátal’s Conjecture. ACM Transactions on Mathematical Software, 48(2), 1–12. DOI: 10.1145/3485630 [arXiv]
    [BibTeX]
    @article{2022_EiflerGleixnerPulaj_Safeintegerprogramming,
      year = {2022},
      journal = {ACM Transactions on Mathematical Software},
      volume = {48},
      number = {2},
      pages = {1-12},
      doi = {10.1145/3485630},
      archiveprefix = {arXiv},
      eprint = {1809.01572},
      primaryclass = {math.CO},
      author = {Eifler, Leon and Gleixner, Ambros and Pulaj, Jonad},
      title = {A Safe Computational Framework for Integer Programming Applied to Chvátal's Conjecture}
    }
  47. Hendrych, D., Troppens, H., Besançon, M., and Pokutta, S. (2022). Convex Integer Optimization with Frank-Wolfe Methods. [arXiv] [slides] [code]
    [BibTeX]
    @misc{2022_HendrychTroppensBesanconPokutta_Convexintegerfrankwolfe,
      archiveprefix = {arXiv},
      eprint = {2208.11010},
      primaryclass = {math.OC},
      year = {2022},
      author = {Hendrych, Deborah and Troppens, Hannah and Besançon, Mathieu and Pokutta, Sebastian},
      title = {Convex Integer Optimization with Frank-Wolfe Methods},
      code = {https://github.com/ZIB-IOL/Boscia.jl},
      slides = {https://pokutta.com/slides/20220915_boscia.pdf}
    }
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    @article{2022_HoppmannEtAl_CyclepartitionUavrouting,
      year = {2022},
      journal = {Optimization Methods and Software},
      doi = {10.1080/10556788.2022.2053972},
      author = {Hoppmann-Baum, Kai and Burdakov, Oleg and Mexi, Gioni and Casselgren, Carl Johan and Koch, Thorsten},
      title = {Length-Constrained Cycle Partition with an Application to UAV Routing}
    }
  49. Iommazzo, G., D’Ambrosio, C., Frangioni, A., and Liberti, L. (2022). Algorithm Configuration Problem. In Encyclopedia of Optimization (pp. 1–8). DOI: 10.1007/978-3-030-54621-2_749-1 [arXiv]
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    @incollection{2022_IommazzoDAmbrosioFrangioniLiberti_Algorithmconfiguration,
      year = {2022},
      booktitle = {Encyclopedia of Optimization},
      pages = {1-8},
      doi = {10.1007/978-3-030-54621-2_749-1},
      archiveprefix = {arXiv},
      eprint = {2403.00898},
      primaryclass = {cs.AI},
      author = {Iommazzo, Gabriele and D'Ambrosio, Claudia and Frangioni, Antonio and Liberti, Leo},
      title = {Algorithm Configuration Problem}
    }
  50. Kossen, T., Hirzel, M. A., Madai, V. I., Boenisch, F., Hennemuth, A., Hildebrand, K., Pokutta, S., Sharma, K., Hilbert, A., Sobesky, J., Galinovic, I., Khalil, A. A., Fiebach, J. B., and Frey, D. (2022). Towards Sharing Brain Images: Differentially Private TOF-MRA Images with Segmentation Labels Using Generative Adversarial Networks. Frontiers in Artificial Intelligence. DOI: 10.3389/frai.2022.813842
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    }
  51. Bestuzheva, K., Gleixner, A., and Völker, H. (2022). Strengthening SONC Relaxations with Constraints Derived From Variable Bounds. Proceedings of the Proceedings of the Hungarian Global Optimization Workshop HUGO 2022, 41–44. [URL] [arXiv]
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      year = {2022},
      booktitle = {Proceedings of the Proceedings of the Hungarian Global Optimization Workshop HUGO 2022},
      pages = {41-44},
      url = {https://inf.u-szeged.hu/hugo/},
      archiveprefix = {arXiv},
      eprint = {2304.12145},
      primaryclass = {math.OC},
      author = {Bestuzheva, Ksenia and Gleixner, Ambros and Völker, Helena},
      title = {Strengthening SONC Relaxations with Constraints Derived From Variable Bounds}
    }
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    @techreport{2021_BestuzhevaEtAl_Scip8,
      year = {2021},
      institution = {Zuse Institute Berlin},
      type = {ZIB Report},
      month = dec,
      number = {21-41},
      url = {https://nbn-resolving.org/urn:nbn:de:0297-zib-85309},
      archiveprefix = {arXiv},
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      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 = {The SCIP Optimization Suite 8.0},
      code = {https://scipopt.org}
    }
  53. Chmiela, A., Khalil, E. B., Gleixner, A., Lodi, A., and Pokutta, S. (2021). Learning to Schedule Heuristics in Branch-and-bound. Proceedings of the Conference on Neural Information Processing Systems, 34, 24235–24246. [URL] [arXiv] [poster]
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    @inproceedings{2021_ChmielaEtAl_Heuristicscheduling,
      year = {2021},
      booktitle = {Proceedings of the Conference on Neural Information Processing Systems},
      month = mar,
      volume = {34},
      pages = {24235–24246},
      url = {https://proceedings.neurips.cc/paper_files/paper/2021/file/cb7c403aa312160380010ee3dd4bfc53-Paper.pdf},
      archiveprefix = {arXiv},
      eprint = {2103.10294},
      primaryclass = {cs.LG},
      author = {Chmiela, Antonia and Khalil, Elias B. and Gleixner, Ambros and Lodi, Andrea and Pokutta, Sebastian},
      title = {Learning to Schedule Heuristics in Branch-and-bound},
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    }
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    @misc{2021_BolusaniConiglioRalphsTahernejad_Unifiedmultistagemilp,
      archiveprefix = {arXiv},
      eprint = {2104.09003},
      primaryclass = {math.OC},
      year = {2021},
      author = {Bolusani, Suresh and Coniglio, Stefano and Ralphs, Ted K. and Tahernejad, Sahar},
      title = {A Unified Framework for Multistage Mixed Integer Linear Optimization}
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  55. Ramin, E., Bestuzheva, K., Gargalo, C., Ramin, D., Schneider, C., Ramin, P., Flores-Alsina, X., Andersen, M., and Gernaey, K. (2021). Incremental Design of Water Symbiosis Networks with Prior Knowledge: the Case of an Industrial Park in Kenya. Science of the Total Environment, 751. DOI: 10.1016/j.scitotenv.2020.141706
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    @article{2021_RaminEtAl_Incrementalwatersymbiosis,
      year = {2021},
      journal = {Science of the Total Environment},
      volume = {751},
      doi = {10.1016/j.scitotenv.2020.141706},
      author = {Ramin, Elham and Bestuzheva, Ksenia and Gargalo, Carina and Ramin, Danial and Schneider, Carina and Ramin, Pedram and Flores-Alsina, Xavier and Andersen, Maj and Gernaey, Krist},
      title = {Incremental Design of Water Symbiosis Networks with Prior Knowledge: the Case of an Industrial Park in Kenya}
    }
  56. Sofranac, B., Gleixner, A., and Pokutta, S. (2021). An Algorithm-independent Measure of Progress for Linear Constraint Propagation. Proceedings of the International Conference on Principles and Practice of Constraint Programming, 52:1–17. DOI: 10.4230/LIPIcs.CP.2021.52 [URL] [arXiv] [video]
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    @inproceedings{2021_SofranacGleixnerPokutta_Progressmeasure:1,
      year = {2021},
      booktitle = {Proceedings of the International Conference on Principles and Practice of Constraint Programming},
      pages = {52:1–17},
      doi = {10.4230/LIPIcs.CP.2021.52},
      url = {https://drops.dagstuhl.de/opus/volltexte/2021/15343/},
      archiveprefix = {arXiv},
      eprint = {2106.07573},
      primaryclass = {math.OC},
      author = {Sofranac, Boro and Gleixner, Ambros and Pokutta, Sebastian},
      title = {An Algorithm-independent Measure of Progress for Linear Constraint Propagation},
      video = {https://youtu.be/paZtGYlkBfE}
    }
  57. Chmiela, A., Muñoz, G., and Serrano, F. (2021). On the Implementation and Strengthening of Intersection Cuts for QCQPs. Proceedings of the Conference on Integer Programming and Combinatorial Optimization, 134–147. DOI: 10.1007/978-3-030-73879-2_10
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    @inproceedings{2022_ChmielaMuozSerrano_Intersectioncutsqcqps:1,
      year = {2021},
      booktitle = {Proceedings of the Conference on Integer Programming and Combinatorial Optimization},
      pages = {134-147},
      doi = {10.1007/978-3-030-73879-2_10},
      author = {Chmiela, Antonia and Muñoz, Gonzalo and Serrano, Felipe},
      title = {On the Implementation and Strengthening of Intersection Cuts for QCQPs}
    }
  58. Hoppmann-Baum, K., Mexi, G., Burdakov, O., Casselgren, C. J., and Koch, T. (2020). Minimum Cycle Partition with Length Requirements. Proceedings of the International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, 12296, 273–282. DOI: 10.1007/978-3-030-58942-4_18
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    @inproceedings{2020_HoppmannEtAl_Minimumcyclepartition,
      year = {2020},
      booktitle = {Proceedings of the International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research},
      volume = {12296},
      pages = {273-282},
      doi = {10.1007/978-3-030-58942-4_18},
      author = {Hoppmann-Baum, Kai and Mexi, Gioni and Burdakov, Oleg and Casselgren, Carl Johan and Koch, Thorsten},
      title = {Minimum Cycle Partition with Length Requirements}
    }
  59. Sofranac, B., Gleixner, A., and Pokutta, S. (2020). Accelerating Domain Propagation: An Efficient GPU-parallel Algorithm Over Sparse Matrices. Proceedings of the 10th IEEE/ACM Workshop on Irregular Applications: Architectures and Algorithms, IA3 2020, 1–11. DOI: 10.1109/IA351965.2020.00007 [arXiv] [summary] [slides] [video]
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    @inproceedings{2020_SofranacGleixnerPokutta_Domainpropagation:1,
      year = {2020},
      booktitle = {Proceedings of the 10th IEEE/ACM Workshop on Irregular Applications: Architectures and Algorithms, IA3 2020},
      pages = {1-11},
      doi = {10.1109/IA351965.2020.00007},
      archiveprefix = {arXiv},
      eprint = {2009.07785},
      primaryclass = {cs.DC},
      author = {Sofranac, Boro and Gleixner, Ambros and Pokutta, Sebastian},
      title = {Accelerating Domain Propagation: An Efficient GPU-parallel Algorithm Over Sparse Matrices},
      slides = {https://app.box.com/s/qy0pjmhtbm7shk2ypxjxlh2sj4nudvyu},
      summary = {https://pokutta.com/blog/research/2020/09/20/gpu-prob.html},
      video = {https://youtu.be/7mERPal9pVs}
    }