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

08 Jun 2020 (modified: 05 May 2023)L4DC 2020Readers: Everyone
Abstract: In this paper, 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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