On the sufficient descent condition of the Hager-Zhang conjugate gradient methods

DOI10.1007/s10288-014-0255-6zbMath1307.65084OpenAlexW2015288657MaRDI QIDQ483732

Saman Babaie-Kafaki

Publication date: 17 December 2014

Published in: 4OR (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10288-014-0255-6

zbMATH Keywords

unconstrained optimizationglobal convergenceeigenvalueconjugate gradient algorithmlarge-scale problemsufficient descent conditionHager-Zhang nonlinear conjugate gradient method

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Large-scale problems in mathematical programming (90C06) Nonlinear programming (90C30)

Related Items (10)

A class of descent four-term extension of the Dai-Liao conjugate gradient method based on the scaled memoryless BFGS update ⋮ A hybridization of the Hestenes–Stiefel and Dai–Yuan conjugate gradient methods based on a least-squares approach ⋮ Improving the Dai-Liao parameter choices using a fixed point equation ⋮ Two optimal Hager-Zhang conjugate gradient methods for solving monotone nonlinear equations ⋮ On optimality of two adaptive choices for the parameter of Dai-Liao method ⋮ A modified Dai-Liao conjugate gradient method with a new parameter for solving image restoration problems ⋮ A Conjugate Gradient Method Based on a Modified Secant Relation for Unconstrained Optimization ⋮ An efficient Dai-Liao type conjugate gradient method by reformulating the CG parameter in the search direction equation ⋮ 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 ⋮ Dai-Liao extensions of a descent hybrid nonlinear conjugate gradient method with application in signal processing

Uses Software

• L-BFGS
• CG_DESCENT

Cites Work

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• A note on the global convergence theorem of the scaled conjugate gradient algorithms proposed by Andrei
• On the limited memory BFGS method for large scale optimization
• Optimization theory and methods. Nonlinear programming
• Algorithm 851
• Updating Quasi-Newton Matrices with Limited Storage
• Global Convergence Properties of Conjugate Gradient Methods for Optimization
• Convergence Properties of Nonlinear Conjugate Gradient Methods
• A Nonlinear Conjugate Gradient Algorithm with an Optimal Property and an Improved Wolfe Line Search
• A New Conjugate Gradient Method with Guaranteed Descent and an Efficient Line Search
• The conjugate gradient method in extremal problems
• Methods of conjugate gradients for solving linear systems
• New conjugacy conditions and related nonlinear conjugate gradient methods
• New properties of a nonlinear conjugate gradient method

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