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2010•Unpublished venueRequires access

A modified Liu-Storey conjugate gradient method and its global convergence for unconstrained optimization

Duan Fu-jian, Zhongbo Sun

Open publisher page 8 citations

Abstract

In this paper, a sufficient descent conjugate gradient method is proposed for solving unconstrained optimization problems and a new sufficient descent search direction is proposed. Similarly, this method can be generalized to other classical conjugate gradient methods. The theoretical analysis shows that the algorithm is global convergence under some suitable conditions. Numerical results show that this new modified algorithm is effective in unconstrained optimization problems.

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What this paper is about

In this paper, a sufficient descent conjugate gradient method is proposed for solving unconstrained optimization problems and a new sufficient descent search direction is proposed. Similarly, this method can be generalized to other classical conjugate gradient methods. The theoretical analysis shows that the algorithm is global convergence under some suitable conditions. Numerical results show that this new modified algorithm is effective in unconstrained optimization problems.

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OpenAlex reports 8 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 sufficient descent conjugate gradient method is proposed for solving unconstrained optimization problems and a new sufficient descent search direction is proposed. Similarly, this method can be generalized to other classical conjugate gradient methods. The theoretical analysis shows that the algorithm is global convergence under some suitable conditions. Numerical results show that this new modified algorithm is effective in unconstrained optimization problems.

Key concepts: Conjugate gradient method, Nonlinear conjugate gradient method, Convergence (economics), Gradient descent, Derivation of the conjugate gradient method, Descent (aeronautics), Conjugate residual method, Gradient method

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