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A New Nonlinear Conjugate Gradient Method for Unconstrained Optimization Problems

Jinkui Liu

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Abstract

In this paper,an efficient conjugate gradient method is given to solve the general unconstrained optimization problems,which can guarantee the sufficient descent property and the global convergence with the strong Wolfe line search conditions.Numerical results show that the new method is efficient and stationary by comparing with PRP+ method,so it can be widely used in scientific computation.

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What this paper is about

In this paper,an efficient conjugate gradient method is given to solve the general unconstrained optimization problems,which can guarantee the sufficient descent property and the global convergence with the strong Wolfe line search conditions.Numerical results show that the new method is efficient and stationary by comparing with PRP+ method,so it can be widely used in scientific computation.

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Available abstract

In this paper,an efficient conjugate gradient method is given to solve the general unconstrained optimization problems,which can guarantee the sufficient descent property and the global convergence with the strong Wolfe line search conditions.Numerical results show that the new method is efficient and stationary by comparing with PRP+ method,so it can be widely used in scientific computation.

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

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