Core Concepts
This paper proposes a novel inverse optimization method to learn the implicit convex constraints of an optimization problem from a set of expert-accepted and rejected solutions, aiming to improve the efficiency and accuracy of future decision-making processes.
Quotes
"In the era of big data, learning from past expert decisions and their corresponding outcomes, whether good or bad, provides an invaluable opportunity for improving future decision-making processes."
"In inverse optimization, learning from both ‘good’ and ‘bad’ observed solutions can provide invaluable information about the patterns, preferences, and restrictions of the underlying forward optimization model."
"An incorrect guideline or constraint in the optimization model can lead to a significantly different feasible region and affect the possible optimal solutions that the objective function can achieve."