@article{RiedmuellerKoch2025, author = {Riedm{\"u}ller, Stephanie and Koch, Thorsten}, title = {Exact Objective Space Contraction for the Preprocessing of Multi-objective Integer Programs}, arxiv = {http://arxiv.org/abs/2512.01535}, year = {2025}, abstract = {Solving integer optimization problems with large or widely ranged objective coefficients can lead to numerical instability and increased runtimes. When the problem also involves multiple objectives, the impact of the objective coefficients on runtimes and numerical issues multiplies. We address this issue by transforming the coefficients of linear objective functions into smaller integer coefficients. To the best of our knowledge, this problem has not been defined before. Next to a straightforward scaling heuristic, we introduce a novel exact transformation approach for the preprocessing of multi-objective binary problems. In this exact approach, the large or widely ranged integer objective coefficients are transformed into the minimal integer objective coefficients that preserve the dominance relation of the points in the objective space. The transformation problem is solved with an integer programming formulation with an exponential number of constraints. We present a cutting-plane algorithm that can efficiently handle the problem size. In a first computational study, we analyze how often and in which settings the transformation actually leads to smaller coefficients. In a second study, we evaluate how the exact transformation and a typical scaling heuristic, when used as preprocessing, affect the runtime and numerical stability of the Defining Point Algorithm.}, language = {en} } @article{SchwopeMarshParsonsetal.2025, author = {Schwope, A.D. and Marsh, T.R. and Parsons, S.G. and Vogel, J. and Dhillon, V.S.}, title = {Unveiling the white dwarf in the eclipsing polar HU Aquarii}, journal = {Astronomy \& Astrophysics}, year = {2025}, language = {en} } @article{HoelterLemkeWeinzierletal.2025, author = {H{\"o}lter, Arne and Lemke, Mathias and Weinzierl, Stefan and Stein, Lewin}, title = {Non-Reflecting Characteristic Boundary Conditions for Adjoint Time-Domain Acoustic Simulations}, journal = {Journal of Theoretical and Computational Acoustics}, year = {2025}, abstract = {Accurate acoustic simulations in the free field require non-reflective boundary conditions to suppress spurious reflections at the computational domain boundaries. Although several characteristic-based formulations for direct (forward) simulations have been proposed in recent decades, the adjoint formulations of such characteristic-based boundary conditions (CBCs) have received limited atten- tion in the literature and lack a comprehensive analysis. This paper presents the derivation and evaluation of adjoint CBCs complementing the existing direct CBCs. Both the forward and adjoint CBCs are applied to the (nonlinear) Euler equations and linear acoustic equations in time-domain simulations. In this manner, the CBCs are investigated and subsequently compared to assess their respective accuracy and consistency. The CBCs were implemented using both a single-point and a zonal approach, with the former optionally combined with a sponge layer. Both approaches yielded comparable results in direct and adjoint simulations, while the zonal CBC exhibited improved ac- curacy at lower frequencies. Across the evaluated frequency range of approximately 350 - 5600 Hz, spurious reflections were attenuated by up to -70dB in both forward and adjoint cases, demon- strating the effectiveness and consistency of the proposed boundary treatment.}, language = {en} } @misc{LindnerMehlBartoszuketal.2025, author = {Lindner, Niels and Mehl, Lukas and Bartoszuk, Karolina and Berendes, Sarah and Zittel, Janina}, title = {Demand Uncertainty in Energy Systems: Scenario Catalogs vs. Integrated Robust Optimization}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-102404}, year = {2025}, abstract = {Designing efficient energy systems is indispensable for shaping a more sustainable society. This involves making infrastructure investment decisions that must be valid for a long-term time horizon. While energy system optimization models constitute a powerful technique to support planning decisions, they need to cope with inherent uncertainty. For example, predicting future demand on a scale of decades is not only an intricate challenge in itself, but small fluctuations in such a forecast might also largely impact the layout of a complex energy system. In this paper, we compare two methodologies of capturing demand uncertainty for linear-programming based energy system optimization models. On one hand, we generate and analyze catalogs of varying demand scenarios, where each individual scenario is considered independently, so that the optimization produces scenario-specific investment pathways. On the other hand, we make use of robust linear programming to meet the demand of all scenarios at once. Since including a multitude of scenarios increases the size and complexity of the optimization model, we will show how to use warm-starting approaches to accelerate the computation process, by exploiting the similar structure of the linear program across different demand inputs. This allows to integrate a meaningful number of demand scenarios with fully-fledged energy system models. We demonstrate the practical use of our methods in a case study of the Berlin-Brandenburg area in Germany, a region that contains both a metropolitan area and its rural surroundings. As a backbone, we use the open-source framework oemof to create a sector-coupled optimization model for planning an energy system with up to 100\% reduction of greenhouse gas emissions. This model features a fine-grained temporal resolution of one hour for the full year 2050. We consider uncertainty in demand for electricity, hydrogen, natural gas, central, and decentral heat. Based on our computations, we analyze the trade-offs in terms of quality and computation time for scenario catalogs and the robust optimization approach. We further demonstrate that our procedure provides a valuable strategy for decision makers to gain insight on the robustness and sensitivity of solutions regarding demand variability.}, language = {en} } @article{GorgullaGarzaKapiletal.2025, author = {Gorgulla, Christoph and Garza, Alejandro J. and Kapil, Venkat and Fackeldey, Konstantin}, title = {QUASAR: A Flexible QM-MM Method for Biomolecular Systems based on Restraining Spheres}, journal = {Computer Physics Communications}, issn = {0010-4655}, doi = {10.1016/j.cpc.2025.109949}, year = {2025}, abstract = {Quantum mechanical models of molecules theoretically offer unprecedented accuracy in predicting values associated with these systems, including the free energy of interaction between two molecules. However, high-accuracy quantum mechanical methods are computationally too expensive to be applied to larger systems, including most biomolecular systems such as proteins. To circumvent this challenge, the hybrid quantum mechanics/molecular mechanics (QM/MM) method was developed, allowing one to treat only the most important part of the system on the quantum mechanical level and the remaining part on the classical level. To date, QM/MM simulations for biomolecular systems have been carried out almost exclusively on the electronic structure level, neglecting nuclear quantum effects (NQEs). Yet NQEs can play a major role in biomolecular systems [1]. Here, we present i-QI, a QM/MM client for the path integral molecular dynamics (PIMD) software i-PI [2, 3, 4]. i-QI allows for carrying out QM/MM simulations simultaneously, allowing for the inclusion of electronic as well as nuclear quantum effects. i-QI implements a new QM/MM scheme based on constraining potentials called QUASAR, which allows handling diffusive systems, such as biomolecules solvated in water solvent. The QUASAR method is suitable in particular when the properties of interest are equilibrium properties, such as the free energy of binding. i-QI is freely available and open source, and we demonstrate it on a test system.}, language = {en} } @misc{WeiserHubigShanmugamSubramaniam2025, author = {Weiser, Martin and Hubig, Michael and Shanmugam Subramaniam, Jayant}, title = {Reconstructing Ambient Temperature Changes in Death Time Estimation with a Bayesian Double-Exponential Approach}, journal = {Zenodo}, doi = {10.5281/zenodo.17702240}, year = {2025}, abstract = {Code and data for the reconstruction of ambient temperature drop in time of death estimation We provide Octave code and temperature measurement data for - empirircally estimating thermal sensor likelihood - estimating time and amplitude of a single sudden ambient temperature drop from temperature measurement data in two thermally different compartments.}, language = {en} } @article{KempkeRehfeldtKoch2025, author = {Kempke, Nils-Christian and Rehfeldt, Daniel and Koch, Thorsten}, title = {A Massively Parallel Interior-Point-Method for Arrowhead Linear Programs}, journal = {SIAM Journal on Scientific Computing}, arxiv = {http://arxiv.org/abs/2412.07731}, year = {2025}, language = {en} } @article{Navayazdani2025, author = {Navayazdani, Esfandiar}, title = {Ridge Regression on Riemannian Manifolds for Time-Series Prediction}, arxiv = {http://arxiv.org/abs/2411.18339}, year = {2025}, language = {en} } @article{SenguptaBartoli2025, author = {Sengupta, Agniva and Bartoli, Adrien}, title = {Convex Solutions to SfT and NRSfM under Algebraic Deformation Models}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, year = {2025}, abstract = {We present nonlinear formulations to Shape-from-Template (SfT) and Non-Rigid Structure-from-Motion (NRSfM) faithfully exploiting the isometric, conformal and equiareal deformation models. Existing work uses relaxations such as inextensibility or requires knowing the optic flow field around the correspondences, an impractical assumption. In contrast, the proposed formulations only require point correspondences and resolve all ambiguities using the notions of maximal depth and maximal isometry heuristics. We propose solution methods using Semi-Definite Programming (SDP) for all formulations. We show that straightforward SDP models conflict with the usual maximal depth heuristic and propose an adapted opposite-depth parameterisation demonstrating a lesser relaxation gap. Experimental results on many real-world benchmark datasets demonstrate superior accuracy over existing methods.}, language = {en} } @article{ClauseckerLemireSchintke2025, author = {Clausecker, Robert and Lemire, Daniel and Schintke, Florian}, title = {Faster Positional-Population Counts for AVX2, AVX-512, and ASIMD}, volume = {37}, journal = {Concurrency and Computation: Practice and Experience}, number = {27-28}, publisher = {Wiley}, issn = {1532-0626}, doi = {10.1002/cpe.70435}, year = {2025}, language = {en} }