A New Hybrid Conjugate Gradient Method with Guaranteed Descent for Unconstraint Optimization | Zendy

Basim A. Hassan | Zendy

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A New Hybrid Conjugate Gradient Method with Guaranteed Descent for Unconstraint Optimization

Author(s) -

Basim A. Hassan

Publication year - 2018

Publication title -

al-mustansiriyah journal of science

Language(s) - English

Resource type - Journals

eISSN - 2521-3520

pISSN - 1814-635X

DOI - 10.23851/mjs.v28i3.114

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

The conjugate gradient method an efficient technique for solving the unconstrained optimization problem. In this paper, we propose a new hybrid nonlinear conjugate gradient methods, which have the descent at every iteration and globally convergence properties under certain conditions. The numerical results show that new hybrid method are efficient for the given test problems.

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