【Abstract】The alternating-direction-method-of-multipliers-based (ADMM-based) interior point method, or ABIP method, is a hybrid algorithm that effectively combines interior point method (IPM) and first-order methods to achieve a performance boost in large-scale linear optimization. Different from traditional IPM that relies on computationally intensive Newton steps, the ABIP method applies ADMM to approximately solve the barrier penalized problem. However, similar to other first-order methods, this technique remains sensitive to condition number and inverse precision. In this paper, we provide an enhanced ABIP method with multiple improvements. First, we develop an ABIP method to solve the general linear conic optimization and establish the associated iteration complexity. Second, inspired by some existing methods, we develop different implementation strategies for the ABIP method, which substantially improve its performance in linear optimization. Finally, we conduct extensive numerical experiments in both synthetic and real-world data sets to demonstrate the empirical advantage of our developments. In particular, the enhanced ABIP method achieves a 5.8× reduction in the geometric mean of run time on 105 selected linear optimization instances from Netlib, and it exhibits advantages in certain structured problems, such as support vector machine and PageRank. However, the enhanced ABIP method still falls behind commercial solvers in many benchmarks, especially when high accuracy is desired. We posit that it can serve as a complementary tool alongside well-established solvers.