Refine
Document Type
- ZIB-Report (8)
- Article (3)
- In Proceedings (1)
Language
- English (12)
Is part of the Bibliography
- no (12)
Keywords
- linear programming (2)
- mixed-integer semidefinite programming (2)
- Branch-and-cut (1)
- Branch-and-price (1)
- Column generation (1)
- Constraint integer programming (1)
- LP solver (1)
- Linear programming (1)
- MINLP solver (1)
- MIP solver (1)
- Mixed-integer linear programming (1)
- Mixed-integer nonlinear programming (1)
- Mixed-integer semidefinite programming (1)
- Optimization solver (1)
- Parallelization (1)
- SCIP, Mathematical optimization, Python, Modeling (1)
- SCIP-Jack (1)
- Steiner tree optimization (1)
- Steiner tree problem (1)
- Steiner tree problems (1)
- Steiner tree solver (1)
- Steiner tree variants (1)
- branch-and-cut (1)
- branch-and-price (1)
- branch-cut-and-price framework (1)
- column generation framework (1)
- constraint integer programming (1)
- generic column generation (1)
- maximum-weight connected subgraph (1)
- maximum-weight connected subgraph problem (1)
- mixed-integer linear and nonlinear programming (1)
- mixed-integer linear programming (1)
- mixed-integer nonlinear programming (1)
- modeling (1)
- multi-criteria optimization (1)
- optimization solver (1)
- parallel branch-and-bound (1)
- parallelization (1)
- prize-collecting Steiner tree (1)
- prize-collecting Steiner tree problem (1)
- reduction techniques (1)
- simplex method (1)
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.
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.