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arXiv · 1609.06331

Max-affine estimators for convex stochastic programming

Gábor Balázs·András György·Csaba Szepesvári
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Abstract

In this paper, we consider two sequential decision making problems with a convexity structure, namely an energy storage optimization task and a multi-product assembly example. We formulate these problems in the stochastic programming framework and discuss an approximate dynamic programming technique for their solutions. As the cost-to-go functions are convex in these cases, we use max-affine estimates for their approximations. To train such a max-affine estimate, we provide a new convex regression algorithm, and evaluate it empirically for these planning scenarios.

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Gábor Balázs, András György, Csaba Szepesvári. 2016-09-18. Max-affine estimators for convex stochastic programming. https://arxiv.org/abs/1609.06331

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