A new class of efficient and globally convergent conjugate gradient methods in the Dai–Liao family

DOI10.1080/10556788.2014.1001511zbMath1328.90143OpenAlexW2094281397MaRDI QIDQ3458820

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Publication date: 28 December 2015

Published in: Optimization Methods and Software (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10556788.2014.1001511

zbMATH Keywords

unconstrained optimizationconjugate gradient methodsmodified secant equationglobal convergence property

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Nonlinear programming (90C30) Numerical methods based on nonlinear programming (49M37)

Related Items (8)

Global convergence of a new sufficient descent spectral three-term conjugate gradient class for large-scale optimization ⋮ An improved Perry conjugate gradient method with adaptive parameter choice ⋮ An accelerated descent CG algorithm with clustering the eigenvalues for large-scale nonconvex unconstrained optimization and its application in image restoration problems ⋮ A class of new three-term descent conjugate gradient algorithms for large-scale unconstrained optimization and applications to image restoration problems ⋮ A survey of gradient methods for solving nonlinear optimization ⋮ A New Dai-Liao Conjugate Gradient Method with Optimal Parameter Choice ⋮ A novel value for the parameter in the Dai-Liao-type conjugate gradient method ⋮ An optimal parameter choice for the Dai-Liao family of conjugate gradient methods by avoiding a direction of the maximum magnification by the search direction matrix

Uses Software

• CUTEr

Cites Work

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• Restart procedures for the conjugate gradient method
• Numerical Optimization
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• A Nonlinear Conjugate Gradient Algorithm with an Optimal Property and an Improved Wolfe Line Search
• Function minimization by conjugate gradients
• CUTEr and SifDec
• The conjugate gradient method in extremal problems
• Methods of conjugate gradients for solving linear systems
• New conjugacy conditions and related nonlinear conjugate gradient methods
• Properties and numerical performance of quasi-Newton methods with modified quasi-Newton equations
• Benchmarking optimization software with performance profiles.
• An efficient hybrid conjugate gradient method for unconstrained optimization

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