Open AccessNew hybrid BFGS-CG method for solving unconstrained optimization
Author(s) -
Wan Farah Hanan Wan Osman,
Mustafa Mamat,
Mohd Asrul Hery Ibrahim
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1874/1/012080
Subject(s) - broyden–fletcher–goldfarb–shanno algorithm , conjugate gradient method , quasi newton method , nonlinear conjugate gradient method , computer science , gradient method , hybrid algorithm (constraint satisfaction) , mathematical optimization , newton's method , algorithm , mathematics , gradient descent , artificial intelligence , physics , artificial neural network , computer network , asynchronous communication , nonlinear system , quantum mechanics , stochastic programming , constraint programming , constraint logic programming
Conjugate gradient method and quasi-Newton (QN) method are both well known solvers for solving unconstrained optimization problems. In this paper, we proposed a new conjugate gradient method denoted as Wan, Asrul and Mustafa (WAM) method. This WAM method is then combined with the QN method to produce a new hybrid search direction which is QN-WAM. Based on numerical results, the proposed hybrid method proved to be more efficient compared to the original quasi-Newton method and other hybrid methods.