A hybridization of the Hestenes–Stiefel and Dai–Yuan conjugate gradient methods based on a least-squares approach

DOI10.1080/10556788.2014.966825zbMath1328.65133OpenAlexW2065678922WikidataQ57952636 ScholiaQ57952636MaRDI QIDQ3458811

Saman Babaie-Kafaki, Reza Ghanbari

Publication date: 28 December 2015

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

Full work available at URL: https://doi.org/10.1080/10556788.2014.966825

zbMATH Keywords

unconstrained optimizationglobal convergenceconjugate gradient methodleast-squares

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Methods of quasi-Newton type (90C53)

Related Items (10)

A descent hybrid conjugate gradient method based on the memoryless BFGS update ⋮ A class of descent four-term extension of the Dai-Liao conjugate gradient method based on the scaled memoryless BFGS update ⋮ New nonlinear conjugate gradient methods based on optimal Dai-Liao parameters ⋮ A new subspace minimization conjugate gradient method with nonmonotone line search for unconstrained optimization ⋮ A new hybrid conjugate gradient algorithm based on the Newton direction to solve unconstrained optimization problems ⋮ A class of adaptive dai-liao conjugate gradient methods based on the scaled memoryless BFGS update ⋮ A linear hybridization of the Hestenes-Stiefel method and the memoryless BFGS technique ⋮ A Conjugate Gradient Method Based on a Modified Secant Relation for Unconstrained Optimization ⋮ Least-squares-based three-term conjugate gradient methods ⋮ Dai-Liao extensions of a descent hybrid nonlinear conjugate gradient method with application in signal processing

Cites Work

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• Two hybrid nonlinear conjugate gradient methods based on a modified secant equation
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• An efficient hybrid conjugate gradient method for unconstrained optimization

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