IJPAM: Volume 99, No. 3 (2015)

TRUST REGION WITH NONLINEAR CONJUGATE
GRADIENT METHOD

Dalal Hachelfi$^1$, Yamina Laskri$^2$, Mohamed Lamine Sahari$^3$
$^{1,2,3}$Department of Mathematics
Badji Mokhtar University
P.O. Box 12, Annaba, 23000, ALGERIA
$^1$dalelhach@yahoo.fr


Abstract. Descent direction methods and trust region methods are usually used to solve the unconstrained optimization problem $\left( p\right)$ \begin{equation*} \left( P\right) \left\{ \underset{x\in \mathbb{R}^{n}}{\min }f\left( x\right) \right. . \end{equation*}

In this work, we are interested in convergence results that use trust region methods which employ the conjugate gradient method Day-Yuan version as a subprogram for each iteration. Further, we penalize the quadratic problems with constraints and convert them into series of unconstrained problems.

Received: October 29, 2014

AMS Subject Classification: 65K05, 90C30

Key Words and Phrases: unconstrained optimization, conjugate gradient method Day-Yuan version, Wolf's rule, trust region methods, penalty method

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DOI: 10.12732/ijpam.v99i3.6 How to cite this paper?

Source: International Journal of Pure and Applied Mathematics
ISSN printed version: 1311-8080
ISSN on-line version: 1314-3395
Year: 2015
Volume: 99
Issue: 3
Pages: 299 - 313


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