# Global Optimization of Mixed-Integer Nonlinear Programs with SCIP 8

arXiv: https://arxiv.org/abs/2301.00587 · HTML: https://mlsanity.com/paper/2301.00587/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jan 2023.

## Built on by (most cited first, 5 of 5)

- [Reinforcement Learning for Node Selection in Branch-and-Bound](https://mlsanity.com/paper/2310.00112/) (2023)
- [Joint Problems in Learning Multiple Dynamical Systems](https://mlsanity.com/paper/2311.02181/) (2023)
- [Cons-training Tensor Networks: Embedding and Optimization Over Discrete Linear Constraints](https://mlsanity.com/paper/2405.09005/) (2024)
- [Sparsity-Constraint Optimization via Splicing Iteration](https://mlsanity.com/paper/2406.12017/) (2024)
- [Representation Learning for Exact Preimages](https://mlsanity.com/paper/2609.32018/) (2026)

## 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.