An Introduction to Contextual (Stochastic) Optimization

Information Systems and Operations Management

Speaker: Erick Delage (HEC Montreal)

Room Bernard Ramanantsoa 

Abstract

Utsav Sadana, Department of Computer Science and Operations Research, Université de Montréal

Abhilash Chenreddy, GERAD & Department of Decision Sciences, HEC Montréal

Erick Delage, presenter, GERAD & Department of Decision Sciences, HEC Montréal

Alexandre Forel, CIRRELT & SCALE-AI Chair in Data-Driven Supply Chains, Department of Mathematical and Industrial Engineering, Polytechnique Montréal

Emma Frejinger, CIRRELT & Department of Computer Science and Operations Research, Université de Montréal

Thibaut Vidal, CIRRELT & SCALE-AI Chair in Data-Driven Supply Chains, Department of Mathematical and Industrial Engineering, Polytechnique Montréal

Recently there has been a surge of interest in operations research (OR) and the machine learning (ML) community in combining prediction algorithms and optimization techniques to solve decision-making problems in the face of uncertainty. This gave rise to the field of contextual optimization, under which data-driven procedures are developed to prescribe actions to the decision-maker that make the best use of the most recently updated information. A large variety of models and methods have been presented in both OR and ML literature under a variety of names, including data-driven optimization, prescriptive optimization, predictive stochastic programming, policy optimization, (smart) predict/estimate-then-optimize, decision-focused learning, (task-based) end-to-end learning/forecasting/optimization, etc. Focusing on single and two-stage stochastic programming problems, this tutorial identifies three main frameworks for learning policies from data and discusses their strengths and limitations. We present the existing models and methods under a uniform notation and terminology and classify them according to the three main frameworks identified. Our objective with this survey is to both strengthen the general understanding of this active field of research and stimulate further theoretical and algorithmic advancements in integrating ML and stochastic programming.