An adaptive three-term conjugate gradient method based on self-scaling memoryless BFGS matrix

DOI10.1016/j.cam.2017.10.013zbMath1382.90116OpenAlexW2766109040MaRDI QIDQ1677473

Shengwei Yao, Liangshuo Ning

Publication date: 21 November 2017

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.2017.10.013

zbMATH Keywords

unconstrained optimizationglobal convergenceconjugate gradient methodself-scaling memoryless BFGS matrix

Mathematics Subject Classification ID

Numerical computation of eigenvalues and eigenvectors of matrices (65F15) Nonlinear programming (90C30) Methods of quasi-Newton type (90C53) Numerical methods based on nonlinear programming (49M37)

Related Items (10)

A note on memory-less SR1 and memory-less BFGS methods for large-scale unconstrained optimization ⋮ Global convergence of a new sufficient descent spectral three-term conjugate gradient class for large-scale optimization ⋮ A modified conjugate gradient method based on the self-scaling memoryless BFGS update ⋮ A three-term conjugate gradient method with a random parameter for large-scale unconstrained optimization and its application in regression model ⋮ An adaptive family of projection methods for constrained monotone nonlinear equations with applications ⋮ Two families of scaled three-term conjugate gradient methods with sufficient descent property for nonconvex optimization ⋮ Some three-term conjugate gradient methods with the new direction structure ⋮ A class of globally convergent three-term Dai-Liao conjugate gradient methods ⋮ A new accelerated conjugate gradient method for large-scale unconstrained optimization ⋮ A one-parameter class of three-term conjugate gradient methods with an adaptive parameter choice

Uses Software

• CGOPT
• ACGSSV

Cites Work

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