Events items teaser

Tutte Colloquium - Jim Luedtke
2020-07-31 15:30 (America/Toronto)
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).
