Race, Exchange, Improve: Finding high-quality MIP solutions quickly
Abstract
Mixed-integer programming (MIP) is a cornerstone in applied optimization, both in industry and academia. Recently, there has been increased attention to finding strong primal solutions quickly. This is reflected, for example, in the development of the NVIDIA cuOpt solver and, most recently, in the new MIPFEAS benchmark, which has a tight time limit of 600 seconds and evaluates solvers based on how quickly they find high-quality primal solutions. This article introduces a MIP portfolio parallelization scheme, focusing on efficiently exchanging information between its workers. We present two implementations of this scheme: one built directly into the open-source MIP solver SCIP, and an external one, which we call ReXi. ReXi is currently the fastest non-commercial solver in the MIPFEAS benchmark, followed by the SCIP-integrated implementation. Moreover, we present new versions of both implementations that considerably outperform their predecessors on the MIPFEAS benchmark.
Related
- Finding solutions with the Borsuk-Ulam theorem
- Finding Simple Proofs for First-Order Optimization
- Using Joint Models in Phase I Dose‐Finding Designs in Oncology: Considerations for Frequentist Approaches
- Single-call Polyak-Type Extragradient Methods for Monotone Root-Finding Problems
- Finding New Limit Points of Mahler Measure by Methods of Missing Data Restoration
BibTeX
@misc{mexi2026race,
title = {{Race, Exchange, Improve: Finding high-quality MIP solutions quickly}},
author = {Gioni Mexi and Daniel Rehfeldt},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.05954},
eprint = {2609.05954},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.05954},
}
RIS
TY - UNPB
TI - Race, Exchange, Improve: Finding high-quality MIP solutions quickly
AU - Gioni Mexi
AU - Daniel Rehfeldt
PY - 2026
DA - 2026/09/05
PB - arXiv
DO - 10.48550/arxiv.2609.05954
UR - https://arxiv.org/abs/2609.05954
AB - Mixed-integer programming (MIP) is a cornerstone in applied optimization, both in industry and academia. Recently, there has been increased attention to finding strong primal solutions quickly. This is reflected, for example, in the development of the NVIDIA cuOpt solver and, most recently, in the new MIPFEAS benchmark, which has a tight time limit of 600 seconds and evaluates solvers based on how quickly they find high-quality primal solutions. This article introduces a MIP portfolio parallelization scheme, focusing on efficiently exchanging information between its workers. We present two implementations of this scheme: one built directly into the open-source MIP solver SCIP, and an external one, which we call ReXi. ReXi is currently the fastest non-commercial solver in the MIPFEAS benchmark, followed by the SCIP-integrated implementation. Moreover, we present new versions of both implementations that considerably outperform their predecessors on the MIPFEAS benchmark.
ER -