Distributionally Robust Optimization with Correlated Data from Vector Autoregressive Processes
Optimization and Control
2019-09-10 v1 Machine Learning
Computation
Machine Learning
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Abstract
We present a distributionally robust formulation of a stochastic optimization problem for non-i.i.d vector autoregressive data. We use the Wasserstein distance to define robustness in the space of distributions and we show, using duality theory, that the problem is equivalent to a finite convex-concave saddle point problem. The performance of the method is demonstrated on both synthetic and real data.
Cite
@article{arxiv.1909.03433,
title = {Distributionally Robust Optimization with Correlated Data from Vector Autoregressive Processes},
author = {Xialiang Dou and Mihai Anitescu},
journal= {arXiv preprint arXiv:1909.03433},
year = {2019}
}