@misc{PedersenLindnerRehfeldtetal.2025, author = {Pedersen, Jaap and Lindner, Niels and Rehfeldt, Daniel and Koch, Thorsten}, title = {Comparing Branching Rules for the Quota Steiner Tree Problem with Interference}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-101250}, year = {2025}, abstract = {Branching decisions play a crucial role in branch-and-bound algorithms for solving combinatorial optimization problems. In this paper, we investigate several branching rules applied to the Quota Steiner Tree Problem with Interference (QSTPI). The Quota Steiner Tree Problem (QSTP) generalizes the classical Steiner Tree Problem (STP) in graphs by seeking a minimum-cost tree that connects a subset of profit-associated vertices to meet a given quota. The extended version, QSTPI, introduces interference among vertices: Selecting certain vertices simultaneously reduces their individual contributions to the overall profit. This problem arises, for example, in positioning and connecting wind turbines, where turbines possibly shadow other turbines, reducing their energy yield. While exact solvers for standard STP-related problems often rely heavily on reduction techniques and cutting-plane methods - rarely generating large branch-and-bound trees - experiments reveal that large instances of QSTPI require significantly more branching to compute provably optimal solutions. In contrast to branching on variables, we utilize the combinatorial structure of the QSTPI by branching on the graph's vertices. We adapt classical and problem-specific branching rules and present a comprehensive computational study comparing the effectiveness of these branching strategies.}, language = {en} } @misc{RehfeldtKoch2020, author = {Rehfeldt, Daniel and Koch, Thorsten}, title = {On the exact solution of prize-collecting Steiner tree problems}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-78174}, year = {2020}, language = {en} } @article{RehfeldtKoch2023, author = {Rehfeldt, Daniel and Koch, Thorsten}, title = {Implications, Conflicts, and Reductions for Steiner Trees}, volume = {197}, journal = {Mathematical Programming}, publisher = {Springer}, doi = {10.1007/s10107-021-01757-5}, pages = {903 -- 966}, year = {2023}, language = {en} } @misc{KempkeRehfeldtKoch2024, author = {Kempke, Nils-Christian and Rehfeldt, Daniel and Koch, Thorsten}, title = {A Massively Parallel Interior-Point-Method for Arrowhead Linear Programs}, issn = {1438-0064}, arxiv = {http://arxiv.org/abs/2412.07731}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-98829}, year = {2024}, abstract = {In practice, non-specialized interior point algorithms often cannot utilize the massively parallel compute resources offered by modern many- and multi-core compute platforms. However, efficient distributed solution techniques are required, especially for large-scale linear programs. This article describes a new decomposition technique for systems of linear equations implemented in the parallel interior-point solver PIPS-IPM++. The algorithm exploits a matrix structure commonly found in optimization problems: a doubly-bordered block-diagonal or arrowhead structure. This structure is preserved in the linear KKT systems solved during each iteration of the interior-point method. We present a hierarchical Schur complement decomposition that distributes and solves the linear optimization problem; it is designed for high-performance architectures and scales well with the availability of additional computing resources. The decomposition approach uses the border constraints' locality to decouple the factorization process. Our approach is motivated by large-scale unit-commitment problems. We demonstrate the performance of our method on a set of mid-to large-scale instances, some of which have more than 10^9 nonzeros in their constraint matrix.}, language = {en} } @article{PedersenWeinandSyranidouetal.2024, author = {Pedersen, Jaap and Weinand, Jann Michael and Syranidou, Chloi and Rehfeldt, Daniel}, title = {An efficient solver for large-scale onshore wind farm siting including cable routing}, volume = {317}, journal = {European Journal of Operational Research}, number = {2}, arxiv = {http://arxiv.org/abs/2304.09658}, doi = {10.1016/j.ejor.2024.04.026}, pages = {616 -- 630}, year = {2024}, abstract = {Existing planning approaches for onshore wind farm siting and grid integration often do not meet minimum cost solutions or social and environmental considerations. In this paper, we develop an exact approach for the integrated layout and cable routing problem of onshore wind farm planning using the Quota Steiner tree problem. Applying a novel transformation on a known directed cut formulation, reduction techniques, and heuristics, we design an exact solver that makes large problem instances solvable and outperforms generic MIP solvers. In selected regions of Germany, the trade-offs between minimizing costs and landscape impact of onshore wind farm siting are investigated. Although our case studies show large trade-offs between the objective criteria of cost and landscape impact, small burdens on one criterion can significantly improve the other criteria. In addition, we demonstrate that contrary to many approaches for exclusive turbine siting, grid integration must be simultaneously optimized to avoid excessive costs or landscape impacts in the course of a wind farm project. Our novel problem formulation and the developed solver can assist planners in decision-making and help optimize wind farms in large regions in the future.}, language = {en} } @article{RehfeldtHobbieSchoenheitetal.2022, author = {Rehfeldt, Daniel and Hobbie, Hannes and Sch{\"o}nheit, David and Koch, Thorsten and M{\"o}st, Dominik and Gleixner, Ambros}, title = {A massively parallel interior-point solver for LPs with generalized arrowhead structure, and applications to energy system models}, volume = {296}, journal = {European Journal of Operational Research}, number = {1}, doi = {10.1016/j.ejor.2021.06.063}, pages = {60 -- 71}, year = {2022}, abstract = {Linear energy system models are a crucial component of energy system design and operations, as well as energy policy consulting. If detailed enough, such models lead to large-scale linear programs, which can be intractable even for the best state-of-the-art solvers. This article introduces an interior-point solver that exploits common structures of energy system models to efficiently run in parallel on distributed-memory systems. The solver is designed for linear programs with doubly-bordered block-diagonal constraint matrix and makes use of a Schur complement based decomposition. In order to handle the large number of linking constraints and variables commonly observed in energy system models, a distributed Schur complement preconditioner is used. In addition, the solver features a number of more generic techniques such as parallel matrix scaling and structure-preserving presolving. The implementation is based on the solver PIPS-IPM. We evaluate the computational performance on energy system models with up to four billion nonzero entries in the constraint matrix—and up to one billion columns and one billion rows. This article mainly concentrates on the energy system model ELMOD, which is a linear optimization model representing the European electricity markets by the use of a nodal pricing market-clearing. It has been widely applied in the literature on energy system analyses in recent years. However, it will be demonstrated that the new solver is also applicable to other energy system models.}, language = {en} } @inproceedings{RehfeldtKoch2021, author = {Rehfeldt, Daniel and Koch, Thorsten}, title = {Implications, conflicts, and reductions for Steiner trees}, booktitle = {Integer Programming and Combinatorial Optimization: 22th International Conference, IPCO 2021}, doi = {10.1007/978-3-030-73879-2_33}, pages = {473 -- 487}, year = {2021}, language = {en} } @article{RehfeldtKoch2021, author = {Rehfeldt, Daniel and Koch, Thorsten}, title = {On the exact solution of prize-collecting Steiner tree problems}, journal = {INFORMS Journal on Computing}, doi = {10.1287/ijoc.2021.1087}, year = {2021}, language = {en} } @article{BreuerBussieckFiandetal.2019, author = {Breuer, Thomas and Bussieck, Michael and Fiand, Frederik and Cao, Karl-Ki{\^e}n and Gils, Hans Christian and Wetzel, Manuel and Gleixner, Ambros and Koch, Thorsten and Rehfeldt, Daniel and Khabi, Dmitry}, title = {BEAM-ME: Ein interdisziplin{\"a}rer Beitrag zur Erreichung der Klimaziele}, journal = {OR-News : das Magazin der GOR}, number = {66}, pages = {6 -- 8}, year = {2019}, language = {de} } @inproceedings{GleixnerKempkeKochetal.2020, author = {Gleixner, Ambros and Kempke, Nils-Christian and Koch, Thorsten and Rehfeldt, Daniel and Uslu, Svenja}, title = {First Experiments with Structure-Aware Presolving for a Parallel Interior-Point Method}, booktitle = {Operations Research Proceedings 2019}, edition = {1}, publisher = {Springer International Publishing}, doi = {10.1007/978-3-030-48439-2_13}, pages = {105 -- 111}, year = {2020}, abstract = {In linear optimization, matrix structure can often be exploited algorithmically. However, beneficial presolving reductions sometimes destroy the special structure of a given problem. In this article, we discuss structure-aware implementations of presolving as part of a parallel interior-point method to solve linear programs with block-diagonal structure, including both linking variables and linking constraints. While presolving reductions are often mathematically simple, their implementation in a high-performance computing environment is a complex endeavor. We report results on impact, performance, and scalability of the resulting presolving routines on real-world energy system models with up to 700 million nonzero entries in the constraint matrix.}, language = {en} }