A Kernel Mean Embedding Approach to Reducing Conservativeness in Stochastic Programming and Control

Code

• github.com/jj-zhu/leibniz-ss-2021
  none★ 3

Abstract

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.

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Reproductions