  It has been verified that the linear programming (LP) is able to formulate
many real-life optimization problems, which can obtain the optimum by resorting
to corresponding solvers such as OptVerse, Gurobi and CPLEX. In the past
decades, a serial of traditional operation research algorithms have been
proposed to obtain the optimum of a given LP in a fewer solving time. Recently,
there is a trend of using machine learning (ML) techniques to improve the
performance of above solvers. However, almost no previous work takes advantage
of ML techniques to improve the performance of solver from the front end, i.e.,
the modeling (or formulation). In this paper, we are the first to propose a
reinforcement learning-based reformulation method for LP to improve the
performance of solving process. Using an open-source solver COIN-OR LP (CLP) as
an environment, we implement the proposed method over two public research LP
datasets and one large-scale LP dataset collected from practical production
planning scenario. The evaluation results suggest that the proposed method can
effectively reduce both the solving iteration number ($25\%\downarrow$) and the
solving time ($15\%\downarrow$) over above datasets in average, compared to
directly solving the original LP instances.
