Eigenvalues versus singular values study in conjugate gradient algorithms for large-scale unconstrained optimization

DOI10.1080/10556788.2016.1225211zbMath1368.49057OpenAlexW2525623505MaRDI QIDQ5268936

Neculai Andrei

Publication date: 21 June 2017

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

Full work available at URL: https://doi.org/10.1080/10556788.2016.1225211

zbMATH Keywords

unconstrained optimizationconvergenceeigenvaluesconjugacy conditionsingular valuesWolfe conditionssufficient descent conditionconjugate gradient algorithms

Mathematics Subject Classification ID

Large-scale problems in mathematical programming (90C06) Newton-type methods (49M15) Variational methods for eigenvalues of operators (49R05) Numerical methods for variational inequalities and related problems (65K15)

Related Items

Accelerated adaptive Perry conjugate gradient algorithms based on the self-scaling memoryless BFGS update ⋮ A diagonal quasi-Newton updating method based on minimizing the measure function of Byrd and Nocedal for unconstrained optimization ⋮ A double-parameter scaling Broyden-Fletcher-Goldfarb-Shanno method based on minimizing the measure function of Byrd and Nocedal for unconstrained optimization ⋮ A double parameter scaled BFGS method for unconstrained optimization ⋮ New conjugate gradient algorithms based on self-scaling memoryless Broyden-Fletcher-Goldfarb-Shanno method ⋮ A double parameter self-scaling memoryless BFGS method for unconstrained optimization ⋮ Unnamed Item

Uses Software

• SCALCG

Cites Work

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