Interactive Optimization and Learning
The IOL research lab is led by Sebastian Pokutta and 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).
Upcoming seminar talks
Christophe Roux
(ZIB)
Bounding geometric penalties in Riemannian optimization
@ ZIB, Room 2006 (Seminar Room)
Ye-Chao Liu
(ZIB)
Entanglement detection via Frank-Wolfe algorithms
@ ZIB, Room 2006 (Seminar Room)
Jan Pauls
(Universität Münster)
Advancing Climate Strategies - High-Resolution Canopy Height Estimation from Space
@ ZIB, Room 2006 (Seminar Room)
Sebastian Knebel
(ZIB)
Koopman von Neumann mechanics
@ ZIB, Room 2006 (Seminar Room)
Publication highlights
- 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}, date = {2024-08-06}, 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} }
- 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}, date = {2024-07-01}, volume = {XXXIV}, url = {https://geombina.uccs.edu/past-issues/volume-xxxiv}, archiveprefix = {arXiv}, eprint = {2404.05509}, primaryclass = {math.CO}, author = {Mundinger, Konrad and Pokutta, Sebastian and Spiegel, Christoph and Zimmer, Max}, title = {Extending the Continuum of Six-Colorings} }
- 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}, date = {2024-03-19} }
- Pauls, J., Zimmer, M., Kelly, U. M., Schwartz, M., Saatchi, S., Ciais, P., Pokutta, S., Brandt, M., and Gieseke, F. (2024, June 3). Estimating Canopy Height at Scale. Proceedings of the International Conference on Machine Learning.
[arXiv]
[code]
[BibTeX]
@inproceedings{2024_PaulsEtAl_Canopyheightestimation, year = {2024}, booktitle = {Proceedings of the 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}, date = {2024-06-03} }
- Martínez-Rubio, D., Wirth, E., and Pokutta, S. (2023, March 22). Accelerated and Sparse Algorithms for Approximate Personalized PageRank and Beyond. Proceedings of the Conference on Learning Theory.
[arXiv]
[BibTeX]
@inproceedings{2023_MartinezrubioWirthPokutta_Sparseapproximatepagerank, year = {2023}, booktitle = {Proceedings of the 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}, date = {2023-03-22} }
- 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}, date = {2022-11-25} }
Software repositories
All our publically accessible software repositories are available on GitHub. We have a list of actively maintained repositories:
- FrankeWolfe.jl, a toolbox for Frank-Wolfe and conditional gradients algorithms
- Boscia.jl, a package for Branch-and-Bound on top of Frank-Wolfe methods
- BellPolytopes.jl, a package that addresses the membership problem for local polytopes
We are also actively involved in the development of the SCIP Optimization Suite at ZIB and its interfaces to other programming languages:
- SCIP, one of the fastest academically developed solvers for mixed integer programming (MIP) and mixed integer nonlinear programming (MINLP)
- SoPlex, an optimization package for solving linear programming problems.
- PaPILO parallel presolve routines for (mixed integer) linear programming problems
- PySCIPOpt, a Python interface for SCIP
- SCIP.jl, a Julia interface for SCIP
- JSCIPOpt, a Java interface for SCIP
- russcip, a Rust interface for SCIP