Juniper: An Open-Source Nonlinear Branch-and-Bound Solver in Julia
| Autor: | Harsha Nagarajan, Ole Kröger, Carleton Coffrin, Hassan Hijazi |
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| Rok vydání: | 2018 |
| Předmět: |
Mathematical optimization
021103 operations research Optimization problem Branch and bound Computer science Heuristic (computer science) 020209 energy 0211 other engineering and technologies 02 engineering and technology Solver Nonlinear system Scalability 0202 electrical engineering electronic engineering information engineering Heuristics Global optimization |
| Zdroj: | Integration of Constraint Programming, Artificial Intelligence, and Operations Research ISBN: 9783319930305 CPAIOR |
| DOI: | 10.1007/978-3-319-93031-2_27 |
| Popis: | Nonconvex mixed-integer nonlinear programs (MINLPs) represent a challenging class of optimization problems that often arise in engineering and scientific applications. Because of nonconvexities, these programs are typically solved with global optimization algorithms, which have limited scalability. However, nonlinear branch-and-bound has recently been shown to be an effective heuristic for quickly finding high-quality solutions to large-scale nonconvex MINLPs, such as those arising in infrastructure network optimization. This work proposes Juniper, a Julia-based open-source solver for nonlinear branch-and-bound. Leveraging the high-level Julia programming language makes it easy to modify Juniper’s algorithm and explore extensions, such as branching heuristics, feasibility pumps, and parallelization. Detailed numerical experiments demonstrate that the initial release of Juniper is comparable with other nonlinear branch-and-bound solvers, such as Bonmin, Minotaur, and Knitro, illustrating that Juniper provides a strong foundation for further exploration in utilizing nonlinear branch-and-bound algorithms as heuristics for nonconvex MINLPs. |
| Databáze: | OpenAIRE |
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