Global Optimization of Mixed-Integer Nonlinear Programs with SCIP 8
Not on the map. Why?
Abstract (arXiv)
For over ten years, the constraint integer programming framework SCIP has been extended by capabilities for the solution of convex and nonconvex mixed-integer nonlinear programs (MINLPs). With the recently published version 8.0, these capabilities have been largely reworked and extended. This paper discusses the motivations for recent changes and provides an overview of features that are particular to MINLP solving in SCIP. Further, difficulties in benchmarking global MINLP solvers are discussed and a comparison with several state-of-the-art global MINLP solvers is provided.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Jan 2023.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2023: 2; 2024: 2; 2025: 0; 2026: 1.
5 papers.
- 2023Reinforcement Learning for Node Selection in Branch-and-Boundcited by 4
- 2023Joint Problems in Learning Multiple Dynamical Systemscited by 1
- 2024Cons-training Tensor Networks: Embedding and Optimization Over Discrete Linear Constraintscited by 1
- 2024Sparsity-Constraint Optimization via Splicing Iterationcited by 0
- 2026Representation Learning for Exact Preimagescited by 0
- 2026Representation Learning for Exact Preimages25 Sep 2026
- 2024Sparsity-Constraint Optimization via Splicing Iteration17 Jun 2024
- 2024Cons-training Tensor Networks: Embedding and Optimization Over Discrete Linear Constraints15 May 2024
- 2023Joint Problems in Learning Multiple Dynamical Systems3 Nov 2023
- 2023Reinforcement Learning for Node Selection in Branch-and-Bound29 Sep 2023