The role of optimization in some recent advances in data-driven decision-making
Published in Mathematical Programming, 2022
Recommended citation: Baardman, L., Cristian, R., Perakis, G., Singhvi, D., Skali Lami, O., & Thayaparan, L. (2022). The role of optimization in some recent advances in data-driven decision-making. Mathematical Programming, 1-35. https://rdcu.be/cTvux
We review some recent advances that highlight the difference that optimization can make in data-driven decision-making. We discuss some of our contributions that aim to advance both predictive and prescriptive models. First, we describe how we can optimally estimate clustered models that result in improved predictions. Next, we consider how we can optimize over objective functions that arise from tree ensemble models in order to obtain better prescriptions. Finally, we discuss how we can learn optimal solutions directly from the data allowing for prescriptions without the need for predictions.