A survey of contextual optimization methods for decision-making under uncertainty

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)…

• CR
  Canada Research Chairs
• CF
  Canada First Research Excellence Fund
• NS
  Natural Sciences and Engineering Research Council of Canada
• FD
  Fonds de recherche du Québec – Nature et technologies