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Juniper: An Open-Source Nonlinear Branch-and-Bound Solver in Julia

机译:Juniper:Julia中的开源非线性分支定界求解器

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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.
机译:非凸混合整数非线性程序(MINLP)代表一类具有挑战性的优化问题,通常在工程和科学应用中出现。由于不存在凸凹性,因此通常使用全局优化算法来解决这些程序,该算法的可扩展性有限。但是,非线性分支定界法最近已被证明是一种有效的启发式方法,可以快速找到大规模非凸型MINLP的高质量解决方案,例如基础结构网络优化中产生的解决方案。这项工作提出了瞻博网络,Juniper是基于Julia的用于非线性分支定界的开源求解器。利用高级Julia编程语言,可以轻松地修改Juniper的算法并探索扩展,例如分支启发法,可行性分析和并行化。详细的数值实验表明,瞻博网络的初始发行版可与Bonmin,Minotaur和Knitro等其他非线性分支定界求解器相提并论,这说明瞻博网络为进一步探索非线性分支定界算法提供了坚实的基础。作为非凸型MINLP的启发式方法。

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