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2018•New Trends in Mathematical ScienceOpen access

New adaptive conjugate gradient methods choices for unconstrained optimization

Hawraz N. Jabbar

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Abstract

In this paper, we present two conjugate gradient methods choices for solving unconstrained optimization problems.This attempts is to find suitable choices for parameter of a nonlinear conjugate gradient method proposed by Dai and Liao based on the matrix analysis and using the memoryless BFGS updating formula.Numerical results show that the proposed method is efficient for the unconstrained problems in the CUTEr collection.

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In this paper, we present two conjugate gradient methods choices for solving unconstrained optimization problems.This attempts is to find suitable choices for parameter of a nonlinear conjugate gradient method proposed by Dai and Liao based on the matrix analysis and using the memoryless BFGS updating formula.Numerical results show that the proposed method is efficient for the unconstrained problems in the CUTEr collection.

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Available abstract

In this paper, we present two conjugate gradient methods choices for solving unconstrained optimization problems.This attempts is to find suitable choices for parameter of a nonlinear conjugate gradient method proposed by Dai and Liao based on the matrix analysis and using the memoryless BFGS updating formula.Numerical results show that the proposed method is efficient for the unconstrained problems in the CUTEr collection.

Key concepts: Broyden–Fletcher–Goldfarb–Shanno algorithm, Conjugate gradient method, Nonlinear conjugate gradient method, Derivation of the conjugate gradient method, Conjugate residual method, Gradient method, Mathematical optimization, Conjugate

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