We discuss the issue of finding a good mathematical programming solver configuration for a particular instance of a given problem, and we propose a two-phase approach to solve it. In the first phase we learn the relationships between the instance, the configuration and the performance of the configured solver on the given instance. A specific difficulty of learning a good solver configuration is that parameter settings may not all be independent; this requires enforcing (hard) constraints, something that many widely used supervised learning methods cannot natively achieve. We tackle this issue in the second phase of our approach, where we use the learnt information to construct and solve an optimization problem having an explicit representation of the dependency/consistency constraints on the configuration parameter settings. We discuss computational results for two different instantiations of this approach on a unit commitment problem arising in the short-term planning of hydro valleys. We use logistic regression as the supervised learning methodology and consider CPLEX as the solver of interest.

我们讨论寻找适合特定问题实例的数学规划求解器配置的问题，并提出了一个两阶段的解决方法。在第一阶段，我们学习了问题实例、配置以及规划求解器在给定实例上的性能之间的关系。学习一个好的求解器配置的一个特定困难是参数设置可能并不都是独立的；这需要施加硬约束，而许多广泛使用的监督学习方法不能本身实现。我们在我们方法的第二阶段处理这个问题，利用所学到的信息来构建和解决一个优化问题，该问题在配置参数设置上具有显式的依赖性/一致性约束的表示。我们讨论了在水力谷的短期计划中出现的两个不同实例上该方法的计算结果。我们使用逻辑回归作为监督学习方法，并考虑CPLEX作为感兴趣的求解器。

通过数学规划学习配置数学规划求解器