Inverse optimization describes a process that is the “reverse” of traditional mathematical
optimization. Unlike traditional optimization, which seeks to compute optimal decisions given …

Inverse optimization—determining parameters of an optimization problem that render a
given solution optimal—has received increasing attention in recent years. Although …

We propose a flexible gradient-based framework for learning linear programs from optimal
decisions. Linear programs are often specified by hand, using prior knowledge of relevant …

Consider a problem where a set of feasible observations are provided by an expert, and a
cost function exists that characterizes which of the observations dominate the others and are …

Clinical pathways outline standardized processes in the delivery of care for a specific
disease. Patient journeys through the healthcare system, however, can deviate substantially …

Inverse linear programming (LP) has received increasing attention because of its potential to
infer efficient optimization formulations that can closely replicate the behavior of a complex …

Decision-making problems are commonly formulated as optimization problems, which are
then solved to make optimal decisions. In this work, we consider the inverse problem where …

Throughout many societies around the globe, there is growing awareness of the urgent
need for the transition towards a sustainable economy. Research shows that buying firms …

Health care delivery in China is in transition from reactive and doctor-centered to
preventative and patient-centered. The challenge for the reform is to account for the needs of …

Given a set of observations generated by an optimization process, the goal of inverse
optimization is to determine likely parameters of that process. We cast inverse optimization …