Abstract
Conjugate gradient methods are an important class of methods for unconstrained optimization, especially for large-scale problems. Recently, they have been much studied. In this paper, a new family of conjugate gradient method is proposed for unconstrained optimization. This method includes the already existing two practical nonlinear conjugate gradient methods, which produces a descent search direction at every iteration and converges globally provided that the line search satisfies the Wolfe conditions. The numerical experiments are done to test the efficiency of the new method, which implies the new method is promising. In addition the methods related to this family are uniformly discussed.
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Acknowledgments
We would like to thank to Professor Paul Armand (University of Limoges, France), who has always been generous with his time and advice.
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Sellami, B., Chaib, Y. A new family of globally convergent conjugate gradient methods. Ann Oper Res 241, 497–513 (2016). https://doi.org/10.1007/s10479-016-2120-9
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DOI: https://doi.org/10.1007/s10479-016-2120-9