Interactive Optimization and Learning

The IOL research lab is dedicated to exploring the intersection of mathematical optimization and machine learning, with a focus on developing innovative techniques for learning and optimization. By integrating these two fields, we aim to create new approaches to solving complex problems that leverage the strengths of both optimization and machine learning.

Our group is located both at the Mathematical Optimization research group at the Technische Universität Berlin (TUB) and in the AI in Society, Science, and Technology (AIS²T) department at the Zuse Institute Berlin (ZIB). We are also part of the Berlin mathematics research center MATH+ as well as the Berlin Mathematical School (BMS).

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Zuse Institute

Takustraße 7
14195 Berlin
www.zib.de
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Technische Universität

Str. des 17. Juni 136
10587 Berlin
www.tu.berlin

Publication highlights

  1. 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}
}
  2. 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}
}
  3. Martínez-Rubio, D., Wirth, E., and Pokutta, S. (2023). Accelerated and Sparse Algorithms for Approximate Personalized PageRank and Beyond. Proceedings of Annual Workshop on Computational Learning Theory. [arXiv]
    [BibTeX]
    @inproceedings{mwp_accelrated_sparse_pagerank_23,
  year = {2023},
  booktitle = {Proceedings of Annual Workshop on Computational Learning Theory},
  archiveprefix = {arXiv},
  eprint = {2303.12875},
  primaryclass = {math.OC},
  author = {Martínez-Rubio, David and Wirth, Elias and Pokutta, Sebastian},
  title = {Accelerated and Sparse Algorithms for Approximate Personalized PageRank and Beyond}
}
  4. Parczyk, O., Pokutta, S., Spiegel, C., and Szabó, T. (2023). Fully Computer-assisted Proofs in Extremal Combinatorics. Proceedings of AAAI Conference on Artificial Intelligence. [arXiv] [slides] [code]
    [BibTeX]
    @inproceedings{ppss_ramsey_22,
  year = {2023},
  booktitle = {Proceedings of AAAI Conference on Artificial Intelligence},
  archiveprefix = {arXiv},
  eprint = {2206.04036},
  primaryclass = {math.CO},
  author = {Parczyk, Olaf and Pokutta, Sebastian and Spiegel, Christoph and Szabó, Tibor},
  title = {Fully Computer-assisted Proofs in Extremal Combinatorics},
  code = {https://zenodo.org/record/6602512#.YyvFhi8Rr5g},
  slides = {https://pokutta.com/slides/20220705_DMD22_RamseyMultiplicity.pdf}
}
  5. Braun, G., Carderera, A., Combettes, C., Hassani, H., Karbasi, A., Mokhtari, A., and Pokutta, S. (2022). Conditional Gradient Methods. [arXiv]
    [BibTeX]
    @misc{fw_survey_2022,
  archiveprefix = {arXiv},
  eprint = {2211.14103},
  primaryclass = {math.OC},
  year = {2022},
  author = {Braun, Gábor and Carderera, Alejandro and Combettes, Cyrille and Hassani, Hamed and Karbasi, Amin and Mokhtari, Aryan and Pokutta, Sebastian},
  title = {Conditional Gradient Methods}
}

Software repositories

All our publically accessible software repositories are available on GitHub. We have a list of actively maintained repositories:

We are also actively involved in the development of the SCIP Optimization Suite at ZIB and its interfaces to other programming languages: