Extra-updates criterion for the limited memory BFGS algorithm for large scale nonlinear optimization

DOI10.1006/jcom.2001.0623zbMath1005.65063OpenAlexW1983117412MaRDI QIDQ700177

Mehiddin Al-Baali

Publication date: 30 September 2002

Published in: Journal of Complexity (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1006/jcom.2001.0623

zbMATH Keywords

numerical resultsquasi-Newton methodslimited memory BFGS methodBroyden-Fletcher-Goldfarb-Shannon methodlarge scale unconstrained optimization problems

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Large-scale problems in mathematical programming (90C06) Nonlinear programming (90C30) Methods of quasi-Newton type (90C53)

Related Items (3)

On the performance of switching BFGS/SR1 algorithms for unconstrained optimization ⋮ A limited-memory optimization method using the infinitely many times repeated BNS update and conjugate directions ⋮ Shifted limited-memory variable metric methods for large-scale unconstrained optimization

Uses Software

• L-BFGS

Cites Work

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• Some numerical experiments with variable-storage quasi-Newton algorithms
• On the limited memory BFGS method for large scale optimization
• Representations of quasi-Newton matrices and their use in limited memory methods
• Improved Hessian approximations for the limited memory BFGS method
• Numerical Experience with Limited-Memory Quasi-Newton and Truncated Newton Methods
• The convergence of variable metric matrices in unconstrained optimization
• Updating Quasi-Newton Matrices with Limited Storage
• A Numerical Study of the Limited Memory BFGS Method and the Truncated-Newton Method for Large Scale Optimization
• BFGS with Update Skipping and Varying Memory
• Convergence Properties of a Class of Rank-two Updates
• Evaluation of Large-scale Optimization Problems on Vector and Parallel Architectures
• Extra updates for the bfgs method^∗

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