Some modified conjugate gradient methods for unconstrained optimization

DOI10.1016/j.cam.2016.04.004zbMath1335.90095OpenAlexW2316213429MaRDI QIDQ277201

Wenya Ma, Peng Zhang, Xue-Wu Du

Publication date: 4 May 2016

Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cam.2016.04.004

zbMATH Keywords

unconstrained optimizationglobal convergenceconjugate gradient methodline searchsufficient descent property

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Convex programming (90C25) Nonlinear programming (90C30)

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Uses Software

• CUTEr
• SifDec
• CG_DESCENT

Cites Work

• The convergence properties of some new conjugate gradient methods
• Efficient generalized conjugate gradient algorithms. I: Theory
• A note about WYL's conjugate gradient method and its applications
• An improved Wei-Yao-Liu nonlinear conjugate gradient method for optimization computation
• Global convergence of the Fletcher-Reeves algorithm with inexact linesearch
• The proof of the sufficient descent condition of the Wei-Yao-Liu conjugate gradient method under the strong Wolfe-Powell line search
• Further studies on the Wei-Yao-Liu nonlinear conjugate gradient method
• Convergence Properties of Algorithms for Nonlinear Optimization
• Algorithm 851
• Descent Property and Global Convergence of the Fletcher—Reeves Method with Inexact Line Search
• Global Convergence Properties of Conjugate Gradient Methods for Optimization
• Restart procedures for the conjugate gradient method
• Convergence properties of the Fletcher-Reeves method
• A Nonlinear Conjugate Gradient Method with a Strong Global Convergence Property
• A New Conjugate Gradient Method with Guaranteed Descent and an Efficient Line Search
• Function minimization by conjugate gradients
• CUTEr and SifDec
• Convergence Conditions for Ascent Methods
• Convergence Conditions for Ascent Methods. II: Some Corrections
• The conjugate gradient method in extremal problems
• Methods of conjugate gradients for solving linear systems
• Benchmarking optimization software with performance profiles.
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