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A nonmonotone hybrid conjugate gradient method is proposed, in which the technique of the nonmonotone Wolfe line search is used. Under mild assumptions, we prove the global convergence and linear convergence rate of the method. Numerical experiments are reported.
We propose a new class of trust region method with a self-adaptive trust region radius update rules. Trust region radius is automatically adjusted according to the weighted sum of the ratios between the actual reduction and the prediction reduction. At some assumptions, the global convergence are proved. Finally, numerical experiments show that the method is robust and effective.
Conjugate gradient methods are widely used for large scale unconstrained optimization. A new class of conjugate gradient trust region method is proposed, in which trust region technique is used for guaranteeing the global convergence of the algorithm, and more utilizable information on conjugate gradient vectors is used for accelerating convergence of the algorithm. The global convergence, super linear...
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