A new subspace minimization conjugate gradient method based on conic model for large-scale unconstrained optimization

DOI10.1007/s40314-022-01885-4zbMath1499.90230OpenAlexW4280537945MaRDI QIDQ2140823

Yanyan Li

Publication date: 23 May 2022

Published in: Computational and Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s40314-022-01885-4

zbMATH Keywords

global convergenceconjugate gradient methodconic modelsubspace minimization\(R\)-linear convergence

Mathematics Subject Classification ID

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

Uses Software

• CUTEr
• SifDec
• CG_DESCENT

Cites Work

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• A New Conjugate Gradient Method with Guaranteed Descent and an Efficient Line Search
• Function minimization by conjugate gradients
• CUTEr and SifDec
• The conjugate gradient method in extremal problems
• Methods of conjugate gradients for solving linear systems
• Benchmarking optimization software with performance profiles.

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