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The SCIP Optimization Suite 8.0

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arxiv 2112.08872 v1 pith:YE5RDMGS submitted 2021-12-16 math.OC

Ksenia Bestuzheva , Mathieu Besançon , Wei-Kun Chen , Antonia Chmiela , Tim Donkiewicz , Jasper van Doornmalen , Leon Eifler , Oliver Gaul
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Gerald Gamrath Ambros Gleixner Leona Gottwald Christoph Graczyk Katrin Halbig Alexander Hoen Christopher Hojny Rolf van der Hulst Thorsten Koch Marco Lübbecke Stephen J. Maher Frederic Matter Erik Mühmer Benjamin Müller Marc E. Pfetsch Daniel Rehfeldt Steffan Schlein Franziska Schlösser Felipe Serrano Yuji Shinano Boro Sofranac Mark Turner Stefan Vigerske Fabian Wegscheider Philipp Wellner Dieter Weninger Jakob Witzig
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classification math.OC
keywords scipoptimizationsuiteaddedframeworkimprovementsmajoradditional
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. AutoPBO: LLM-powered Optimization for Local Search PBO Solvers

    cs.AI 2025-09 conditional novelty 5.0 of 10

    An LLM multi-agent framework with a structured solver refactoring automatically improves pseudo-Boolean optimization local search heuristics, outperforming the authors' NuPBO-based baseline and rivaling Gurobi on seve...

  2. STRCMP: Integrating Graph Structural Priors with Language Models for Combinatorial Optimization

    cs.LG 2025-05 reject novelty 4.0 of 10

    STRCMP's GNN-plus-LLM code search for MILP and SAT heuristics does not consistently beat AutoSAT in the paper's own reported numbers.

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