Optimization with Constraint Learning: A Framework and Survey

Understand

Many real-life optimization problems frequently contain one or more constraints or objectives for which there are no explicit formulas.

• If data is however available, these data can be used to learn the constraints.
• The benefits of this approach are clearly seen, however there is a need for this process to be carried out in a structured manner.
• This paper therefore provides a framework for Optimization with Constraint Learning (OCL) which we believe will help to formalize and direct the process of learning constraints from data.