论文标题
嵌入式训练的机器学习模型优化
Optimization with Trained Machine Learning Models Embedded
论文作者
论文摘要
训练有素的ML模型通常嵌入优化问题中。在许多情况下，这会导致大规模的NLP难以解决全球最优性。尽管ML模型经常导致大问题，但它们也表现出均匀的结构和重复模式（例如，ANN中的层）。因此，专门的解决方案策略可用于大型问题类别。最近，有一些有前途的著作提出了使用混合构成编程或降低空间配方的专业重新进行的。但是，需要进一步的工作来开发更有效的解决方案方法，并跟上新的ML模型体系结构的快速发展。
Trained ML models are commonly embedded in optimization problems. In many cases, this leads to large-scale NLPs that are difficult to solve to global optimality. While ML models frequently lead to large problems, they also exhibit homogeneous structures and repeating patterns (e.g., layers in ANNs). Thus, specialized solution strategies can be used for large problem classes. Recently, there have been some promising works proposing specialized reformulations using mixed-integer programming or reduced space formulations. However, further work is needed to develop more efficient solution approaches and keep up with the rapid development of new ML model architectures.
