Christoph Graczyk
My research is devoted to the intersection of optimization techniques and machine learning, aiming to distill qualitative insights from the inherent optimality guarantees of traditional global optimization methods. I am currently developing MiniMIP, an open-source standalone Mixed Integer Linear Programming Solver, to seamlessly integrate machine learning applications with MIP solvers, and vice versa.
📬 Contact
- office
- Room 3024 at ZIB
- graczyk (at) zib.de
🎓 Curriculum vitae
- since 2020
- Researcher at ZIB
- May 2020
- M.Sc. in Mathematics in Finance and Industry at TU Braunschweig
- Mar 2017
- B.Sc. in Mathematics in Finance and Industry at TU Braunschweig
📝 Publications and preprints
Preprints
- 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]
- 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. (2021). The SCIP Optimization Suite 8.0 (ZIB Report No. 21-41). Zuse Institute Berlin.
[URL]
[code]
[BibTeX]
Full articles
- 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]
🔬 Projects
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.
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.
💬 Talks and posters
Conference and workshop talks
- Nov 2022
- Orthogonality-based Cut Selection
SCIP Workshop, Berlin - Oct 2022
- Orthogonality-based Cut Selection
INFORMS Conference, Indianapolis
Poster presentations
- Mar 2023
- Workshop on Optimization and Machine Learning, Waischenfeld
- Oct 2022
- Learning Primal Heuristics and Cut Selection in MIPs
MATH+ Day, Berlin