A Hybrid Spectral Conjugate Gradient Method with Global Convergence

Автор: Jing Li, Shujie Jing

Журнал: International Journal of Mathematical Sciences and Computing @ijmsc

Статья в выпуске: 2 vol.8, 2022 года.

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The spectral conjugate gradient (SCG) method is one of the most commonly used methods to solve large- scale nonlinear unconstrained optimization problems. It is also the research and application hot spot of optimization theorists and optimization practitioners. In this paper, a new hybrid spectral conjugate gradient method is proposed based on the classical nonlinear spectral conjugate gradient method. A new parameter is given. Under the usual assumptions, the descending direction independent of any line search is generated, and it has good convergence performance under the strong Wolfe line search condition . On a set of test problems, the numerical results show that the algorithm is effective.

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Unconstrained optimization, Strong Wolfe line search, Descending condition, Spectral conjugate gradient method, Global convergence

Короткий адрес: https://sciup.org/15018450

IDR: 15018450 | DOI: 10.5815/ijmsc.2022.02.01

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