Global Convergence of a Spectral Conjugate Gradient Method for Unconstrained Optimization | Zendy

Jinkui Liu | Zendy; Youyi Jiang | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Global Convergence of a Spectral Conjugate Gradient Method for Unconstrained Optimization

Author(s) -

Jinkui Liu,

Youyi Jiang

Publication year - 2012

Publication title -

abstract and applied analysis

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.228

H-Index - 56

eISSN - 1687-0409

pISSN - 1085-3375

DOI - 10.1155/2012/758287

Subject(s) - mathematics , conjugate gradient method , convergence (economics) , derivation of the conjugate gradient method , nonlinear conjugate gradient method , gradient method , conjugate residual method , conjugate , mathematical optimization , mathematical analysis , gradient descent , computer science , artificial neural network , economics , economic growth , machine learning

A new nonlinear spectral conjugate descent method for solving unconstrained optimization problems is proposed on the basis of the CD method and the spectral conjugate gradient method. For any line search, the new method satisfies the sufficient descent condition gkTdk<−∥gk∥2. Moreover, we prove that the new method is globally convergent under the strong Wolfe line search. The numerical results show that the new method is more effective for the given test problems from the CUTE test problem library (Bongartz et al., 1995) in contrast to the famous CD method, FR method, and PRP method

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research