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2015•Journal of Information and Computational ScienceRequires access

A Class New Hybrid Conjugate Gradient Method for Unconstrained Optimization

Yunlong Lu

Open publisher page 6 citations

Abstract

In this paper, A class new parameter conjugate gradient method and a new hybrid conjugate gradient method are proposed. The global convergence of the algorithms are proved under the Wolfe line search without the descent condition. Numerical experiments show that the hybrid conjugate gradient algorithm is recommendable.

About this research paper

What this paper is about

In this paper, A class new parameter conjugate gradient method and a new hybrid conjugate gradient method are proposed. The global convergence of the algorithms are proved under the Wolfe line search without the descent condition. Numerical experiments show that the hybrid conjugate gradient algorithm is recommendable.

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OpenAlex reports 6 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

In this paper, A class new parameter conjugate gradient method and a new hybrid conjugate gradient method are proposed. The global convergence of the algorithms are proved under the Wolfe line search without the descent condition. Numerical experiments show that the hybrid conjugate gradient algorithm is recommendable.

Key concepts: Conjugate gradient method, Class (philosophy), Nonlinear conjugate gradient method, Derivation of the conjugate gradient method, Conjugate, Computer science, Gradient method, Conjugate residual method

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