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

Lukáš Adam (adam***at***utia.cas.cz)
Martin Branda (branda***at***karlin.mff.cuni.cz)

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 looking at a few scenarios. We modify it to handle the non-separable objective. A 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.

Keywords: Stochastic programming, Chance-constrained programming, Quantile, Stochastic gradient descent, Machine learning, Large-scale

Category 1: Stochastic Programming

Citation:

Download: [PDF]

Entry Submitted: 12/11/2018
Entry Accepted: 12/11/2018
Entry Last Modified: 05/27/2019

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