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

• URL: http://arxiv.org/abs/2001.10398v2
• Date: Wed, 22 Apr 2020 21:11:59 GMT
• Title: A Kernel Mean Embedding Approach to Reducing Conservativeness in Stochastic Programming and Control
• Authors: Jia-Jie Zhu, Moritz Diehl, Bernhard Sch\"olkopf
• Abstract summary: We apply kernel mean embedding methods to sample-based optimization and control. The effect of such constraint removal is improved optimality and decreased conservativeness.
• Score: 13.739881592455044
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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