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A sufficient descent HS conjugate gradient method and its global convergence for unconstrained optimization

Chunling Xu, Zhongbo Sun

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Abstract

In this paper, a modified descent HS conjugate gradient method is proposed for solving unconstrained optimization problems and a new sufficient descent direction is proposed. Under some suitable conditions, theoretical analysis shows that the algorithm is global convergence. Numerical results show that this method is effective in unconstrained minimizing optimization problems.

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What this paper is about

In this paper, a modified descent HS conjugate gradient method is proposed for solving unconstrained optimization problems and a new sufficient descent direction is proposed. Under some suitable conditions, theoretical analysis shows that the algorithm is global convergence. Numerical results show that this method is effective in unconstrained minimizing optimization problems.

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OpenAlex reports 2 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 modified descent HS conjugate gradient method is proposed for solving unconstrained optimization problems and a new sufficient descent direction is proposed. Under some suitable conditions, theoretical analysis shows that the algorithm is global convergence. Numerical results show that this method is effective in unconstrained minimizing optimization problems.

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

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