论文标题
学习辅助算法展开在线优化和预算限制
Learning-Assisted Algorithm Unrolling for Online Optimization with Budget Constraints
论文作者
论文摘要
在线优化具有多个预算限制是具有挑战性的，因为在短时间内的在线决策与严格的库存约束结合在一起。现有的手动设计算法无法在此设置中实现令人满意的平均性能，因为它们通常需要大量的时间步骤来收敛和/或可能违反库存约束。在本文中，我们提出了一种新的机器学习（ML）辅助展开方法，称为LAAU（学习辅助算法展开），该方法将展开在线决策管道，并利用ML模型来更新Lagrangian乘数在线。为了通过反向传播进行有效的培训，我们会随着时间的推移得出决策管道的梯度。当分别有离线培训数据可获得并在线收集时，我们还提供两种情况下的平均成本界限。最后，我们提出数值结果，以强调Laau可以胜过现有基线。
Online optimization with multiple budget constraints is challenging since the online decisions over a short time horizon are coupled together by strict inventory constraints. The existing manually-designed algorithms cannot achieve satisfactory average performance for this setting because they often need a large number of time steps for convergence and/or may violate the inventory constraints. In this paper, we propose a new machine learning (ML) assisted unrolling approach, called LAAU (Learning-Assisted Algorithm Unrolling), which unrolls the online decision pipeline and leverages an ML model for updating the Lagrangian multiplier online. For efficient training via backpropagation, we derive gradients of the decision pipeline over time. We also provide the average cost bounds for two cases when training data is available offline and collected online, respectively. Finally, we present numerical results to highlight that LAAU can outperform the existing baselines.
