  This paper surveys the trend of leveraging machine learning to solve mixed
integer programming (MIP) problems. Theoretically, MIP is an NP-hard problem,
and most of the combinatorial optimization (CO) problems can be formulated as
the MIP. Like other CO problems, the human-designed heuristic algorithms for
MIP rely on good initial solutions and cost a lot of computational resources.
Therefore, we consider applying machine learning methods to solve MIP, since
ML-enhanced approaches can provide the solution based on the typical patterns
from the historical data. In this paper, we first introduce the formulation and
preliminaries of MIP and several traditional algorithms to solve MIP. Then, we
advocate further promoting the different integration of machine learning and
MIP and introducing related learning-based methods, which can be classified
into exact algorithms and heuristic algorithms. Finally, we propose the outlook
for learning-based MIP solvers, direction towards more combinatorial
optimization problems beyond MIP, and also the mutual embrace of traditional
solvers and machine learning components.
