Tutte Colloquium - Jim Luedtke
Title: Data-Driven Sample-Average Approximation for Stochastic Optimization with Covariate Information
Speaker: | Jim Luedtke |
Affiliation: | University of Wisconsin-Madison |
Zoom: | Please email Emma Watson |
Abstract:
We consider optimization models for decision-making in which parameters within the optimization model are uncertain, but predictions of these parameters can be made using available covariate information. We consider a data-driven setting in which we have observations of the uncertain parameters together with concurrently-observed covariates. Given a new covariate observation, the goal is to choose a decision that minimizes the expected cost conditioned on this observation. We investigate a data-driven framework in which the outputs from a machine learning prediction model are directly used to define a stochastic programming sample average approximation (SAA).