@inproceedings{WitzigBerthold2020, author = {Witzig, Jakob and Berthold, Timo}, title = {Conflict-Free Learning for Mixed Integer Programming}, booktitle = {Integration of AI and OR Techniques in Constraint Programming. CPAIOR 2020}, number = {12296}, publisher = {Springer, Cham.}, doi = {10.1007/978-3-030-58942-4_34}, pages = {521 -- 530}, year = {2020}, abstract = {Conflict learning plays an important role in solving mixed integer programs (MIPs) and is implemented in most major MIP solvers. A major step for MIP conflict learning is to aggregate the LP relaxation of an infeasible subproblem to a single globally valid constraint, the dual proof, that proves infeasibility within the local bounds. Among others, one way of learning is to add these constraints to the problem formulation for the remainder of the search. We suggest to not restrict this procedure to infeasible subproblems, but to also use global proof constraints from subproblems that are not (yet) infeasible, but can be expected to be pruned soon. As a special case, we also consider learning from integer feasible LP solutions. First experiments of this conflict-free learning strategy show promising results on the MIPLIB2017 benchmark set.}, language = {en} } @misc{HosodaMaherShinano2024, author = {Hosoda, Junko and Maher, Stephen J. and Shinano, Yuji}, title = {Combining Benders' decomposition and parallelisation to solve large-scale adaptive network supply chain management problems}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-98744}, year = {2024}, abstract = {Integrated supply chain network design—combining warehouse clustering with inter- and intra-cluster vehicle routing—is a fundamental feature of supply chain management problems. In this paper, a Benders' decomposition-based parallel heuristic approach for the integrated long-haul and local vehicle routing problem is proposed. The proposed algorithm comprises lower- and upper-bound search algorithms running in parallel to find high-quality solutions for challenging supply chain management problems. A novel Benders' decomposition approach is proposed to aid the improvement in the lower bound for large-scale problems. The results demonstrate that the Benders' decomposition approach is effective in finding strong lower bounds and is beneficial for obtaining high-quality solutions.}, language = {en} } @misc{FujiiKimKojimaetal.2022, author = {Fujii, Koichi and Kim, Sunyoung and Kojima, Masakazu and Mittelmann, Hans D. and Shinano, Yuji}, title = {The Largest Unsolved QAP Instance Tai256c Can Be Converted into A 256-dimensional Simple BQOP with A Single Cardinality Constraint}, issn = {1438-0064}, doi = {10.12752/8808}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-88086}, year = {2022}, abstract = {Tai256c is the largest unsolved quadratic assignment problem (QAP) instance in QAPLIB; a 1.48\% gap remains between the best known feasible objective value and lower bound of the unknown optimal value. This paper shows that the instance can be converted into a 256 dimensional binary quadratic optimization problem (BQOP) with a single cardinality constraint which requires the sum of the binary variables to be 92.The converted BQOP is much simpler than the original QAP tai256c and it also inherits some of the symmetry properties. However, it is still very difficult to solve. We present an efficient branch and bound method for improving the lower bound effectively. A new lower bound with 1.36\% gap is also provided.}, language = {en} } @misc{Khorsandi2021, author = {Khorsandi, Ramin}, title = {Gitram 1.0.0}, doi = {10.12752/8527}, year = {2021}, abstract = {Gitram is a software that processes various metadata and data content from GIT and exports them in the format of an XML file. The software is specifically designed for the HPO-navi project. It is also compatible with Opus4 and any other software that can receive data through the sword interface. Gitram process the metadata and the repository content and sends it to Opus4 via the sword interface. The data is taken from a zip file that is stored in the "dataBackup" folder. Users also have an option of saving the data as a document file on Opus. Metadata can also be received from GitLab via a webhook. The entire project is programmed in Python 3.8+ and can be installed on Ubuntu 16 or above. It is open-source and its functionality can be modified to the users needs.}, language = {en} } @misc{Shinano2020, author = {Shinano, Yuji}, title = {UG - Ubiquity Generator Framework v0.9.1}, doi = {10.12752/8508}, year = {2020}, abstract = {UG is a generic framework to parallelize branch-and-bound based solvers (e.g., MIP, MINLP, ExactIP) in a distributed or shared memory computing environment. It exploits the powerful performance of state-of-the-art "base solvers", such as SCIP, CPLEX, etc. without the need for base solver parallelization. UG framework, ParaSCIP(ug[SCIP,MPI]) and FiberSCIP (ug[SCIP,Pthreads]) are available as a beta version. For MIP solving, ParaSCIP and FiberSCIP are well debugged and should be stable. For MINLP solving, they are relatively stable, but not as thoroughly debugged. This release version should handle branch-and-cut approaches where subproblems are defined by variable bounds and also by constrains for ug[SCIP,*] ParaSCIP and FiberSCIP). Therefore, problem classes other than MIP or MINLP can be handled, but they have not been tested yet. v0.9.1: Update orbitope cip files.}, language = {en} } @misc{HaslerPetersKottigSchloesser2023, author = {Hasler, Tim and Peters-Kottig, Wolfgang and Schl{\"o}sser, Franziska}, title = {Software Management Plan developed in HPO-Navi}, doi = {10.12752/9250}, year = {2023}, abstract = {This publication comprises the Software Management Plan (SMP) developed in the HPO-Navi project. The project was funded in the Literature and Information System (LIS) track of the DFG with the aim of increasing the maturity level of scientific software under development, developing improved measures for the quality assurance of this software and for long-term availability. The Ubiquity Generator Framework (UG) software developed in the project provides a software infrastructure to make existing sequential implementations HPC-capable. The UG framework is of interest to developers of specialized optimization algorithms and can be used directly to solve specific problem classes in the field of scientific computing. Due to the naturally high software technology hurdles on HPC systems, UG was initially a highly specialized tool and could only be used to a limited extent without the involvement of the main developer. The central challenge of the project was to create a mechanism for the sustainable development and permanent provision and archiving of the research software. The aim was for UG to achieve the status of a "software product" with implementation of the following project content: - Documentation of the software: a description of the API and the simple connection of further basic solvers, installation instructions for various target platforms and a description of the structure and documentation of the source code as a basis for the distributed sustainable development of UG. - Development of missing user functions of UG: increased platform independence for shared memory parallelization and better logging function for the analysis and verification of results. - Development of a sample data management plan for future research projects that want to use UG and document their research results according to DFG standards for good scientific practice. - Quality assurance: by defining code guidelines, uniform concepts and criteria for the qualitative evaluation of new functions and implementing standards for code review and processes for continuous integration. A complementary goal was to improve the provision, accessibility and long-term reusability of the research software. - Use of an open Git server as a distributed development platform, provision of a download server for releases and sustainable storage of the software code and the associated meta-information. - Improved presentation and visibility through publication in publicly accessible repositories. This includes the prototypical extension of the OPUS 4 repository software widely used in Germany as a means of publishing software with a landing page that can be resolved via a DOI, and improved linking and presentation of UG in swMATH. - Digital long-term archiving: experimental inclusion of software code in an OAIS-compliant digital archiving system and evaluation of the approach in the areas of digital archiving and research data management.}, language = {en} } @misc{BestuzhevaBesanconChenetal.2021, author = {Bestuzheva, Ksenia and Besan{\c{c}}on, Mathieu and Chen, Wei-Kun and Chmiela, Antonia and Donkiewicz, Tim and van Doornmalen, Jasper and Eifler, Leon and Gaul, Oliver and Gamrath, Gerald and Gleixner, Ambros and Gottwald, Leona and Graczyk, Christoph and Halbig, Katrin and Hoen, Alexander and Hojny, Christopher and van der Hulst, Rolf and Koch, Thorsten and L{\"u}bbecke, Marco and Maher, Stephen J. and Matter, Frederic and M{\"u}hmer, Erik and M{\"u}ller, Benjamin and Pfetsch, Marc E. and Rehfeldt, Daniel and Schlein, Steffan and Schl{\"o}sser, Franziska and Serrano, Felipe and Shinano, Yuji and Sofranac, Boro and Turner, Mark and Vigerske, Stefan and Wegscheider, Fabian and Wellner, Philipp and Weninger, Dieter and Witzig, Jakob}, title = {The SCIP Optimization Suite 8.0}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-85309}, year = {2021}, abstract = {The SCIP Optimization Suite provides a collection of software packages for mathematical optimization centered around the constraint integer programming framework SCIP. This paper discusses enhancements and extensions contained in version 8.0 of the SCIP Optimization Suite. Major updates in SCIP include improvements in symmetry handling and decomposition algorithms, new cutting planes, a new plugin type for cut selection, and a complete rework of the way nonlinear constraints are handled. Additionally, SCIP 8.0 now supports interfaces for Julia as well as Matlab. Further, UG now includes a unified framework to parallelize all solvers, a utility to analyze computational experiments has been added to GCG, dual solutions can be postsolved by PaPILO, new heuristics and presolving methods were added to SCIP-SDP, and additional problem classes and major performance improvements are available in SCIP-Jack.}, language = {en} } @misc{BolusaniBesanconBestuzhevaetal.2024, author = {Bolusani, Suresh and Besan{\c{c}}on, Mathieu and Bestuzheva, Ksenia and Chmiela, Antonia and Dion{\´i}sio, Jo{\~a}o and Donkiewicz, Tim and van Doornmalen, Jasper and Eifler, Leon and Ghannam, Mohammed and Gleixner, Ambros and Graczyk, Christoph and Halbig, Katrin and Hedtke, Ivo and Hoen, Alexander and Hojny, Christopher and van der Hulst, Rolf and Kamp, Dominik and Koch, Thorsten and Kofler, Kevin and Lentz, Jurgen and Manns, Julian and Mexi, Gioni and M{\"u}hmer, Erik and E. Pfetsch, Marc and Schl{\"o}sser, Franziska and Serrano, Felipe and Shinano, Yuji and Turner, Mark and Vigerske, Stefan and Weninger, Dieter and Xu, Liding}, title = {The SCIP Optimization Suite 9.0}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-95528}, year = {2024}, abstract = {The SCIP Optimization Suite provides a collection of software packages for mathematical optimization, centered around the constraint integer programming framework SCIP. This report discusses the enhancements and extensions included in the SCIP Optimization Suite 9.0. The updates in SCIP 9.0 include improved symmetry handling, additions and improvements of nonlinear handlers and primal heuristics, a new cut generator and two new cut selection schemes, a new branching rule, a new LP interface, and several bug fixes. The SCIP Optimization Suite 9.0 also features new Rust and C++ interfaces for SCIP, new Python interface for SoPlex, along with enhancements to existing interfaces. The SCIP Optimization Suite 9.0 also includes new and improved features in the LP solver SoPlex, the presolving library PaPILO, the parallel framework UG, the decomposition framework GCG, and the SCIP extension SCIP-SDP. These additions and enhancements have resulted in an overall performance improvement of SCIP in terms of solving time, number of nodes in the branch-and-bound tree, as well as the reliability of the solver.}, language = {en} }