Machine learning approach to chance-constrained problems: An algorithm based on the stochastic gradient descent

Lukáš Adam; Martin Branda

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arXiv · 1905.10986

Machine learning approach to chance-constrained problems: An algorithm based on the stochastic gradient descent

Lukáš Adam·Martin Branda

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Abstract

We consider chance-constrained problems with discrete random distribution. We aim for problems with a large number of scenarios. We propose a novel method based on the stochastic gradient descent method which performs updates of the decision variable based only on considering a few scenarios. We modify it to handle the non-separable objective. Complexity analysis and a comparison with the standard (batch) gradient descent method is provided. We give three examples with non-convex data and show that our method provides a good solution fast even when the number of scenarios is large.

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BibTeX RIS

Lukáš Adam, Martin Branda. 2019-05-27. Machine learning approach to chance-constrained problems: An algorithm based on the stochastic gradient descent. https://arxiv.org/abs/1905.10986

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