BOBILib: Bilevel Optimization (Benchmark) Instance Library
Abstract In this report, we present the , a collection of more than 2600 instances of mixed integer bilevel linear optimization problems (MIBLPs). The goal of this library is to provide a large and well-curated set of test instances freely available for the research community so that new and existing algorithms in bilevel optimization can be tested and compared in a standardized way. The library is sub-divided into instances of different types and also contains different benchmark instance sets. Moreover, we present a new data format for MIBLPs that is less error-prone compared to an older format that will now be deprecated. We provide numerical results for all instances of the library using available bilevel solvers. Based on these numerical results, we select benchmark instance sets, which provide a meaningful basis for experimental comparisons of solution methods in a moderate time. The instances, together with solution files, can be downloaded at https://bobilib.org .
Authors
- Ivana Ljubić (ORCID: https://orcid.org/0000-0002-4834-6284)
- Ted K. Ralphs (ORCID: https://orcid.org/0000-0002-4306-9089)
- Johannes Thürauf
- Thomas Kleinert
- Martin Schmidt
Institutions
- École Supérieure des Sciences Économiques et Commerciales (FR)
- Lehigh University (US)
- Lutheran University of Applied Sciences Nuremberg (DE)
- CY Cergy Paris Université (FR)
- Munich Center for Quantum Science and Technology (DE)
- Universität Trier (DE)
Publication Details
- Journal
- Mathematical Programming Computation
- Published
- 2026-03-21
- DOI
- https://doi.org/10.1007/s12532-025-00294-y
- Primary Topic
- Optimization and Variational Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00