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. Parczyk, O., Pokutta, S., Spiegel, C., and Szabó, T. (2024). New Ramsey Multiplicity Bounds and Search Heuristics. Foundations of Computational Mathematics. DOI: 10.1007/s10208-024-09675-6 [arXiv] [code]
    [BibTeX]
    @article{2022_ParczykPokuttaSpiegelSzabo_Ramseymultiplicityheuristics,
  year = {2024},
  journal = {Foundations of Computational Mathematics},
  doi = {10.1007/s10208-024-09675-6},
  archiveprefix = {arXiv},
  eprint = {2206.04036},
  primaryclass = {math.CO},
  author = {Parczyk, Olaf and Pokutta, Sebastian and Spiegel, Christoph and Szabó, Tibor},
  title = {New Ramsey Multiplicity Bounds and Search Heuristics},
  code = {https://zenodo.org/record/6602512#.YyvFhi8Rr5g}
}
  2. Mundinger, K., Pokutta, S., Spiegel, C., and Zimmer, M. (2024). Extending the Continuum of Six-Colorings. Geombinatorics Quarterly, XXXIV. [URL] [arXiv]
    [BibTeX]
    @article{2024_MundingerPokuttaSpiegelZimmer_SixcoloringsExpansion,
  year = {2024},
  journal = {Geombinatorics Quarterly},
  volume = {XXXIV},
  url = {https://geombina.uccs.edu/past-issues/volume-xxxiv},
  archiveprefix = {arXiv},
  eprint = {2404.05509},
  author = {Mundinger, Konrad and Pokutta, Sebastian and Spiegel, Christoph and Zimmer, Max},
  title = {Extending the Continuum of Six-Colorings}
}
  3. Mundinger, K., Zimmer, M., and Pokutta, S. (2024). Neural Parameter Regression for Explicit Representations of PDE Solution Operators. [arXiv]
    [BibTeX]
    @misc{2024_MundingerZimmerPokutta_Neuralparameterregression,
  archiveprefix = {arXiv},
  eprint = {2403.12764},
  primaryclass = {cs.LG},
  year = {2024},
  author = {Mundinger, Konrad and Zimmer, Max and Pokutta, Sebastian},
  title = {Neural Parameter Regression for Explicit Representations of PDE Solution Operators}
}
  4. Pauls, J., Zimmer, M., Kelly, U. M., Schwartz, M., Saatchi, S., Ciais, P., Pokutta, S., Brandt, M., and Gieseke, F. (2024). Estimating Canopy Height at Scale. Proceedings of International Conference on Machine Learning. [arXiv] [code]
    [BibTeX]
    @inproceedings{2024_PaulsEtAl_Canopyheightestimation,
  year = {2024},
  booktitle = {Proceedings of International Conference on Machine Learning},
  archiveprefix = {arXiv},
  eprint = {2406.01076},
  primaryclass = {cs.CV},
  author = {Pauls, Jan and Zimmer, Max and Kelly, Una M and Schwartz, Martin and Saatchi, Sassan and Ciais, Philippe and Pokutta, Sebastian and Brandt, Martin and Gieseke, Fabian},
  title = {Estimating Canopy Height at Scale},
  code = {https://github.com/AI4Forest/Global-Canopy-Height-Map}
}
  5. Martínez-Rubio, D., Wirth, E., and Pokutta, S. (2023). Accelerated and Sparse Algorithms for Approximate Personalized PageRank and Beyond. Proceedings of Annual Conference on Learning Theory. [arXiv]
    [BibTeX]
    @inproceedings{2023_MartinezrubioWirthPokutta_Sparseapproximatepagerank,
  year = {2023},
  booktitle = {Proceedings of Annual Conference on 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}
}
  6. Braun, G., Carderera, A., Combettes, C., Hassani, H., Karbasi, A., Mokhtari, A., and Pokutta, S. (2022). Conditional Gradient Methods. [arXiv]
    [BibTeX]
    @misc{2022_BraunEtAl_Conditionalgradient,
  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: