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
bolusani (at) zib.de
ghannam (at) zib.de
miskovic (at) zib.de
hoen (at) zib.de
chmiela (at) zib.de
hendrych (at) zib.de
vigerske (at) zib.de
liding.xu (at) zib.de
ebert (at) zib.de
geis (at) zib.de
von.holly-ponientzietz (at) zib.de
dionisio (at) zib.de
🔬 Projects
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.
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.
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.
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.
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.
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.
đź’¬ Talks and posters
Poster presentations
- May 2022
- Monoidal Strengthening for Intersection Cuts Using Maximal Quadratic-Free Sets by Antonia Chmiela
MIP Workshop - Dec 2021
- Learning to Schedule Heuristics in Branch-and-Bound by Antonia Chmiela
NeurIPS Conference
đź“ť Publications and preprints
- Bolusani, S., Besançon, M., Bestuzheva, K., Chmiela, A., DionĂsio, 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., 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]
- 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]
- 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]
[BibTeX]
- 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]
- Hendrych, D., Besançon, M., and Pokutta, S. (2024). Solving the Optimal Experiment Design Problem with Mixed-integer Convex Methods. Proceedings of Symposium on Experimental Algorithms.
DOI: 10.4230/LIPIcs.SEA.2024.16
[arXiv]
[code]
[BibTeX]
- Mexi, G., Shamsi, S., Besançon, M., and le Bodic, P. (2024). Probabilistic Lookahead Strong Branching Via a Stochastic Abstract Branching Model. Proceedings of International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research.
[arXiv]
[BibTeX]
- Göß, A., Martin, A., Pokutta, S., and Sharma, K. (2024). Norm-induced Cuts: Optimization with Lipschitzian Black-box Functions.
[URL]
[arXiv]
[BibTeX]
- Borst, S., Eifler, L., and Gleixner, A. (2024). Certified Constraint Propagation and Dual Proof Analysis in a Numerically Exact MIP Solver.
[arXiv]
[BibTeX]
- Eifler, L., Witzig, J., and Gleixner, A. (2024). Branch and Cut for Partitioning a Graph Into a Cycle of Clusters. Proceedings of International Symposium on Combinatorial Optimization.
DOI: 10.1007/978-3-031-60924-4_8
[arXiv]
[BibTeX]
- 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]
- Ghannam, M., Mexi, G., Lam, E., and Gleixner, A. (2024). Branch and Price for the Length-constrained Cycle Partition Problem. Proceedings of INFORMS Optimization Society Conference.
[URL]
[arXiv]
[BibTeX]
- 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]
- Hoen, A., Kamp, D., and Gleixner, A. (2024). MIP-DD: A Delta Debugger for Mixed Integer Programming Solvers.
[arXiv]
[BibTeX]
- Hoen, A., Oertel, A., Gleixner, A., and Nordström, J. (2024). Certifying MIP-based Presolve Reductions for 0-1 Integer Linear Programs. Proceedings of 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]
- Sharma, K., Hendrych, D., Besançon, M., and Pokutta, S. (2024). Network Design for the Traffic Assignment Problem with Mixed-Integer Frank-Wolfe. Proceedings of INFORMS Optimization Society Conference.
[arXiv]
[BibTeX]
- 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]
[BibTeX]
- 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]
[BibTeX]
- 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]
- 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]
[BibTeX]
- Turner, M., Berthold, T., Besançon, M., and Koch, T. (2023). Cutting Plane Selection with Analytic Centers and Multiregression. Proceedings of International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research.
[BibTeX]
- 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
[BibTeX]
- 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]
[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. (2023). Enabling Research Through the SCIP Optimization Suite 8.0. ACM Transactions on Mathematical Software.
DOI: 10.1145/3585516
[arXiv]
[BibTeX]
- 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]
- Chmiela, A., Muñoz, G., and Serrano, F. (2023). Monoidal Strengthening and Unique Lifting in MIQCPs. Proceedings of Conference on Integer Programming and Combinatorial Optimization.
[URL]
[BibTeX]
- 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
[BibTeX]
- Ghannam, M., and Gleixner, A. (2023). Hybrid Genetic Search for Dynamic Vehicle Routing with Time Windows. Proceedings of Conference of the Society for Operations Research in Germany.
[arXiv]
[BibTeX]
- Mexi, G., Berthold, T., Gleixner, A., and Nordström, J. (2023). Improving Conflict Analysis in MIP Solvers by Pseudo-Boolean Reasoning. Proceedings of 29th International Conference on Principles and Practice of Constraint Programming (CP 2023), 280, 27:1–27:19,
DOI: 10.4230/LIPIcs.CP.2023.27
[BibTeX]
- 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 Conference of the Society for Operations Research in Germany.
[arXiv]
[BibTeX]
- Turner, M., Berthold, T., and Besançon, M. (2023). A Context-Aware Cutting Plane Selection Algorithm for Mixed-Integer Programming. Proceedings of Conference of the Society for Operations Research in Germany.
[arXiv]
[BibTeX]
- Turner, M., Chmiela, A., Koch, T., and Winkler, M. (2023). PySCIPOpt-ML: Embedding Trained Machine Learning Models Into Mixed-integer Programs.
[arXiv]
[BibTeX]
- 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]
- 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 Conference on Neural Information Processing Systems, 176, 220–231.
[URL]
[arXiv]
[BibTeX]
- 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]
- 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]
[BibTeX]
- 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]
[BibTeX]
- 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
[BibTeX]
- 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
[BibTeX]
- Hendrych, D., Troppens, H., Besançon, M., and Pokutta, S. (2022). Convex Integer Optimization with Frank-Wolfe Methods.
[arXiv]
[slides]
[code]
[BibTeX]
- Hoppmann-Baum, K., Burdakov, O., Mexi, G., Casselgren, C. J., and Koch, T. (2022). Length-Constrained Cycle Partition with an Application to UAV Routing. Optimization Methods and Software.
DOI: 10.1080/10556788.2022.2053972
[BibTeX]
- 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
[BibTeX]
- 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
[BibTeX]
- Bestuzheva, K., Gleixner, A., and Völker, H. (2022). Strengthening SONC Relaxations with Constraints Derived From Variable Bounds. Proceedings of Proceedings of the Hungarian Global Optimization Workshop HUGO 2022, 41–44.
[URL]
[arXiv]
[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]
- Chmiela, A., Khalil, E. B., Gleixner, A., Lodi, A., and Pokutta, S. (2021). Learning to Schedule Heuristics in Branch-and-bound. Proceedings of Conference on Neural Information Processing Systems, 34, 24235–24246.
[URL]
[arXiv]
[poster]
[BibTeX]
- Bolusani, S., Coniglio, S., Ralphs, T. K., and Tahernejad, S. (2021). A Unified Framework for Multistage Mixed Integer Linear Optimization.
[arXiv]
[BibTeX]
- 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
[BibTeX]
- Sofranac, B., Gleixner, A., and Pokutta, S. (2021). An Algorithm-independent Measure of Progress for Linear Constraint Propagation. Proceedings of International Conference on Principles and Practice of Constraint Programming, 52:1–17.
DOI: 10.4230/LIPIcs.CP.2021.52
[URL]
[arXiv]
[video]
[BibTeX]
- Chmiela, A., Muñoz, G., and Serrano, F. (2021). On the Implementation and Strengthening of Intersection Cuts for QCQPs. Proceedings of Integer Programming and Combinatorial Optimization: 22nd International Conference, IPCO 2021, 134–147.
DOI: 10.1007/978-3-030-73879-2_10
[BibTeX]
- Hoppmann-Baum, K., Mexi, G., Burdakov, O., Casselgren, C. J., and Koch, T. (2020). Minimum Cycle Partition with Length Requirements. Proceedings of 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
[BibTeX]
- Sofranac, B., Gleixner, A., and Pokutta, S. (2020). Accelerating Domain Propagation: An Efficient GPU-parallel Algorithm Over Sparse Matrices. Proceedings of 10th IEEE/ACM Workshop on Irregular Applications: Architectures and Algorithms, IA3 2020, 1–11.
DOI: 10.1109/IA351965.2020.00007
[arXiv]
[summary]
[slides]
[video]
[BibTeX]
- Turner, M., Berthold, T., Besançon, M., and Koch, T. Branching Via Cutting Plane Selection: Improving Hybrid Branching.
[arXiv]
[BibTeX]
- Bolusani, S., Mexi, G., Besançon, M., and Turner, M. A Multi-Reference Relaxation Enforced Neighborhood Search Heuristic in SCIP.
[arXiv]
[BibTeX]