  This paper surveys the recent attempts at leveraging machine learning to
solve constrained optimization problems. It focuses on surveying the work on
integrating combinatorial solvers and optimization methods with machine
learning architectures. These approaches hold the promise to develop new hybrid
machine learning and optimization methods to predict fast, approximate,
solutions to combinatorial problems and to enable structural logical inference.
This paper presents a conceptual review of the recent advancements in this
emerging area.
