Max-affine estimators for convex stochastic programming
Optimization and Control
2016-09-22 v1 Systems and Control
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.
Cite
@article{arxiv.1609.06331,
title = {Max-affine estimators for convex stochastic programming},
author = {Gábor Balázs and András György and Csaba Szepesvári},
journal= {arXiv preprint arXiv:1609.06331},
year = {2016}
}