Tim Donkiewicz

RWTH Aachen University

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Thorsten Koch

Zuse Institute Berlin

Christopher Hojny

Eindhoven University of Technology

Leon Eifler

Zuse Institute Berlin

Ksenia Bestuzheva

Zuse Institute Berlin

Katrin Halbig

Friedrich-Alexander-Universität Erlangen-Nürnberg

Stefan Vigerske

Zuse Institute Berlin

Mark Turner

Technische Universität Berlin

Erik Mühmer

RWTH Aachen University

Alexander Hoen

Zuse Institute Berlin

Mathieu Besançon

Laboratoire d'Informatique de Grenoble

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Zuse Institute Berlin

Technische Universität Berlin

Technical University of Darmstadt

RWTH Aachen University

Center for Discrete Mathematics and Theoretical Computer Science

Friedrich-Alexander-Universität Erlangen-Nürnberg

John Wiley & Sons (United States)

Universität Trier

Eindhoven University of Technology

Institut national de recherche en informatique et en automatique

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Enabling Research through the SCIP Optimization Suite 8.0

ACM Transactions on Mathematical Software (2023)

Ksenia Bestuzheva Mathieu Besançon Wei‐Kun Chen Antonia Chmiela Tim Donkiewicz

The SCIP Optimization Suite provides a collection of software packages for mathematical optimization centered around the constraint integer programming framework SCIP . The focus of this article is on the role of the SCIP Optimization Suite in supporting research. SCIP ’s main design principles are discussed, followed by a presentation of the latest performance improvements and developments in version 8.0, which serve both as examples of SCIP ’s application as a research tool and as a platform for further developments. Furthermore, this article gives an overview of interfaces to other programming and modeling languages, new features that expand the possibilities for user interaction with the framework, and the latest developments in several extensions built upon SCIP .

Presentation (obstetrics)

10.1145/3585516

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Citations (33)

The SCIP Optimization Suite 9.0

arXiv (Cornell University) (2024)

Suresh Bolusani Mathieu Besançon Ksenia Bestuzheva Antonia Chmiela João Dionísio

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 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.

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Heuristics

Python

10.48550/arxiv.2402.17702

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Citations (90)

The SCIP Optimization Suite 8.0

arXiv (Cornell University) (2021)

Ksenia Bestuzheva Mathieu Besançon Wei‐Kun Chen Antonia Chmiela Tim Donkiewicz

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.

Heuristics

Plug-in

10.48550/arxiv.2112.08872

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Citations (10)

SCIP8: rethinking nonlinear constraint representation and recent progress

HAL (Le Centre pour la Communication Scientifique Directe) (2022)

Mathieu Besançon Ksenia Bestuzheva Wei‐Kun Chen Antonia Chmiela Tim Donkiewicz

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