New Various Dai-Liao Method for Solving Optimization Problems

Abstract

This study introduces and evaluates three new conjugate gradient methods for unconstrained optimization based on Perry's conjugacy condition. These methods were compared with the classical Hestenes-Stiefel method using a variety of test functions. Numerical results demonstrated that the proposed methods significantly improved computational efficiency. Three main parameters were considered: the number of iterations, the number of function evaluations, and the computation time. These findings establish the proposed methods as competitive alternatives for large-scale optimization problems. Future research could focus on extending these methods to more complex problem domains.