Open AccessA NEW HYBRID PRP-MMSIS CONJUGATE GRADIENT METHOD AND ITS APPLICATION IN PORTOFOLIO SELECTION
Author(s) -
S. Devila,
Maulana Malik,
Wed Giyarti
Publication year - 2021
Publication title -
jurnal riset dan aplikasi matematika (jram)
Language(s) - English
Resource type - Journals
ISSN - 2581-0154
DOI - 10.26740/jram.v5n1.p47-59
Subject(s) - conjugate gradient method , line search , selection (genetic algorithm) , nonlinear conjugate gradient method , convergence (economics) , mathematical optimization , mathematics , gradient descent , descent (aeronautics) , gradient method , conjugate residual method , conjugate , descent direction , computer science , mathematical analysis , artificial intelligence , artificial neural network , engineering , economics , radius , economic growth , aerospace engineering , computer security
In this paper, we propose a new hybrid coefficient of conjugate gradient method (CG) for solving unconstrained optimization model. The new coefficient is combination of part the MMSIS (Malik et.al, 2020) and PRP (Polak, Ribi'ere \& Polyak, 1969) coefficients. Under exact line search, the search direction of new method satisfies the sufficient descent condition and based on certain assumption, we establish the global convergence properties. Using some test functions, numerical results show that the proposed method is more efficient than MMSIS method. Besides, the new method can be used to solve problem in minimizing portfolio selection risk .