An improved Dai-Kou conjugate gradient algorithm for unconstrained optimization

DOI10.1007/s10589-019-00143-4zbMath1433.90126OpenAlexW2984664785WikidataQ126848592 ScholiaQ126848592MaRDI QIDQ2301132

Yanyan Li

Publication date: 28 February 2020

Published in: Computational Optimization and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10589-019-00143-4

zbMATH Keywords

global convergenceconjugate gradient algorithmquasi-Newton methodpreconditioned conjugate gradient algorithmlimited memory

Mathematics Subject Classification ID

Large-scale problems in mathematical programming (90C06) Nonconvex programming, global optimization (90C26) Numerical optimization and variational techniques (65K10) Methods of quasi-Newton type (90C53) Complexity and performance of numerical algorithms (65Y20)

Related Items (6)

A hybrid conjugate gradient based approach for solving unconstrained optimization and motion control problems ⋮ An inertial spectral CG projection method based on the memoryless BFGS update ⋮ A regularized limited memory subspace minimization conjugate gradient method for unconstrained optimization ⋮ A new subspace minimization conjugate gradient method for unconstrained minimization ⋮ A Five-Parameter Class of Derivative-Free Spectral Conjugate Gradient Methods for Systems of Large-Scale Nonlinear Monotone Equations ⋮ An overview of nonlinear optimization

Uses Software

• L-BFGS
• CUTEr
• SifDec
• CG_DESCENT

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