Inverse Gram Matrix Methods for Prioritization in Analytic Hierarchy Process: Explainability of Weighted Least Squares Optimization Method

Abstract

This paper proposes Inverse Gram Matrix (IGM) methods to prioritize the Pairwise Reciprocal Matrix (PRM) in the Analytic Hierarchy Process. The IGM methods include Pseudo-IGM, Normalized-IGM, and Lagrange-IGM. Interestingly, the proposed IGM methods achieves the least error of Weighted Least Squares (WLS). Since clarity, explainability, usability and verification for the close-form solutions of WLS appears to be incomplete in the literature, the comprehensive mathematical proofs, detail computational demonstration, and intensive simulation verification to extend the prior studies are offered in this study. After a simulation of 1,000,000 random PRM instances is performed to verify equivalent results of several IGM methods, another simulation of 10,000 random PRM instances are performed to verify that a IGM method is the exact closed-form solution of WLS optimization method. The proposed IGM methods on top of the WLS method may be the promising alternatives of Saaty's Eigen system method to apply to the AHP.