论文标题
内核平均嵌入方法可降低随机编程和控制中的保守性
A Kernel Mean Embedding Approach to Reducing Conservativeness in Stochastic Programming and Control
论文作者
论文摘要
我们将内核平均嵌入方法应用于基于样本的随机优化和控制。具体而言，我们使用简化的扩展方法作为丢弃采样场景的一种方式。这种限制去除的效果改善了最佳性和降低的保守性。这是通过解决分布距离调节的优化问题来实现的。我们证明了这种优化公式在理论上有充分的动力，在计算上可触及且在数值算法中有效。
We apply kernel mean embedding methods to sample-based stochastic optimization and control. Specifically, we use the reduced-set expansion method as a way to discard sampled scenarios. The effect of such constraint removal is improved optimality and decreased conservativeness. This is achieved by solving a distributional-distance-regularized optimization problem. We demonstrated this optimization formulation is well-motivated in theory, computationally tractable and effective in numerical algorithms.
