In this paper, interior point methods are discussed and analyzed. First, general aspects of Data Envelopment Analysis (DEA) are expressed, then the concept of efficiency, CCR and BCC, BCC-CCR, CCR-BCC models, and the reference sets in DEA models and super-efficiency in ranking are presented. Ellipsoid methods that theoretically prove that linear programming problems are efficiently solvable are also used. The result is significant since when a problem is solvable, practical and efficient algorithms can be developed. The ellipsoid method is not a practical algorithm for solving linear programming problems. Finally, a new category of algorithms, known as the interior point optimization method, is introduced, both valuable and efficient. The other advantage of the process above is that the complexity of the interior point algorithm is in polynomial order. In contrast, the simplex algorithm uses exponential order in the worst cases. This is a good reason to indicate the priority of the interior point optimization algorithm compared with the simplex algorithm. Therefore, this point justifies using the interior point methods in DEA.