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2011•Unpublished venueRequires access

A conjugate gradient method with sufficient descent and global convergence for unconstrained nonlinear optimization.

Haohan Liu, Sui Sun Cheng, Xiaoyong Li

Open publisher page 4 citations

Abstract

In this paper a new conjugate gradient method for unconstrained optimization is introudced, which is sufficient descent and globally convergent and which can also be used with the Dai-Yuan method to form a hybrid algorithm. Our methods do not require the strong convexity condition on the objective function. Numerical evidence shows that this new conjugate gradient algorithm may be considered as one of the competitive conjugate gradient methods.

About this research paper

What this paper is about

In this paper a new conjugate gradient method for unconstrained optimization is introudced, which is sufficient descent and globally convergent and which can also be used with the Dai-Yuan method to form a hybrid algorithm. Our methods do not require the strong convexity condition on the objective function. Numerical evidence shows that this new conjugate gradient algorithm may be considered as one of the competitive conjugate gradient methods.

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OpenAlex reports 4 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 new conjugate gradient method for unconstrained optimization is introudced, which is sufficient descent and globally convergent and which can also be used with the Dai-Yuan method to form a hybrid algorithm. Our methods do not require the strong convexity condition on the objective function. Numerical evidence shows that this new conjugate gradient algorithm may be considered as one of the competitive conjugate gradient methods.

Key concepts: Nonlinear conjugate gradient method, Conjugate gradient method, Convergence (economics), Gradient descent, Mathematics, Nonlinear system, Mathematical optimization, Descent (aeronautics)

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A conjugate gradient method with sufficient descent and global convergence for unconstrained nonlinear optimization. — Research Paper | ScholarLens