Christopher Hojny, Mathieu Besançon, Ksenia Bestuzheva, Sander Borst, João Dionísio, Johannes Ehls, Leon Eifler, Mohammed Ghannam, Ambros Gleixner, Adrian Göß, … 展开作者
Christopher Hojny, Mathieu Besançon, Ksenia Bestuzheva, Sander Borst, João Dionísio, Johannes Ehls, Leon Eifler, Mohammed Ghannam, Ambros Gleixner, Adrian Göß, Alexander Hoen, Jacob von Holly-Ponientzietz, Rolf van der Hulst, Dominik Kamp, Thorsten Koch, Kevin Kofler, Jurgen Lentz, Marco Lübbecke, Stephen J. Maher, Paul Matti Meinhold, Gioni Mexi, Til Mohr, Erik Mühmer, Krunal Kishor Patel, Marc E. Pfetsch, Sebastian Pokutta, Chantal Reinartz Groba, Felipe Serrano, Yuji Shinano, Mark Turner, Stefan Vigerske, Matthias Walter, Dieter Weninger, Liding Xu
The SCIP Optimization Suite provides a collection of software packages for mathematical optimization, centered around the constraint integer programming (CIP) framework SCIP. This report discusses the enhancements and extensions included in SCIP Optimization Suite 10.0. The updates in SCIP 10.0 include a new solving mode for exactly solving rational mixed-integer linear programs, a new presolver for detecting implied integral variables, a novel cut-based conflict analysis and separator for flower inequalities, two new heuristics, a novel tool for explaining infeasibility, a new interface for nonlinear solvers as well as improvements in symmetry handling, branching strategies, and SCIP's Benders' decomposition framework. SCIP Optimization Suite 10.0 also includes new and improved features in the the presolving library PaPILO, the parallel framework UG, and the decomposition framework GCG. Moreover, the SCIP Optimization Suite 10.0 contains MIP-DD, the first open-source delta debugger for mixed-integer programming solvers. 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.
Discrete eigenvalue optimization from entropic smoothing and first-order methods
Zuse Institute Berlin（柏林齐布研究所）；Technische Universität Berlin（柏林工业大学）；Univ. Grenoble Alpes, Inria, Laboratoire d’Informatique de Grenoble（格勒诺布尔阿尔卑斯大学、法国国家信息与自动化研究院、格勒诺布尔计算机实验室）
Cut-based Conflict Analysis in Mixed Integer Programming
Zuse Institute Berlin, Germany（柏林齐泽研究所）；COPT GmbH, Berlin, Germany（中科智算科技有限公司）；Fair Isaac Deutschland GmbH（费埃哲德国有限公司）；HTW Berlin, Germany（柏林应用技术大学）；TU Berlin, Germany（柏林工业大学）；University of Copenhagen, Denmark（哥本哈根大学）；Lund University, Sweden（隆德大学）
N2N: A Parallel Framework for Large-Scale MILP under Distributed Memory
Shenzhen Research Institute of Big Data（深圳大数据研究 institute）；Theory Lab, Central Research Institute, 2012 Labs, Huawei Technologies Co. Ltd.（华为技术有限公司理论实验室，中央研究所，2012实验室）；Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
COCO: The Large Scale Black-Box Optimization Benchmarking (bbob-largescale) Test Suite
Inria research centre Saclay（法国国家信息与自动化研究所萨克雷研究中心）；CMAP, Ecole Polytechnique（巴黎综合理工学院应用数学中心）；Hanoi National University of Education（河内国家教育大学）；Jožef Stefan Institute（约瑟夫·斯特凡研究所）
B$^3$-PWL: GPU-Batched Branch-and-Bound for Piecewise-Linear Optimization with SOS2 Constraints
Johns Hopkins University（约翰斯·霍普金斯大学）；University of North Carolina, Chapel Hill（北卡罗来纳大学教堂山分校）；City University of Hong Kong（香港城市大学）；The Hong Kong Institute of AI for Science（香港人工智能科学研究所）
