Implementing and modifying Broyden class updates for large scale optimization

DOI10.1007/s10589-020-00239-2zbMath1468.90125OpenAlexW3106192048MaRDI QIDQ2026770

Dirk Siegel, Martin D. Buhmann

Publication date: 20 May 2021

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

Full work available at URL: https://doi.org/10.1007/s10589-020-00239-2

zbMATH Keywords

nonlinear optimizationconjugate gradient methodBFGS methodBroyden classNorcedal's method

Mathematics Subject Classification ID

Large-scale problems in mathematical programming (90C06) Nonlinear programming (90C30)

Related Items (4)

A Riemannian subspace BFGS trust region method ⋮ Damped techniques for the limited memory BFGS method for large-scale optimization ⋮ A subspace version of the Powell-Yuan trust-region algorithm for equality constrained optimization ⋮ Recent advances in trust region algorithms

Uses Software

• L-BFGS
• TOLMIN

Cites Work

• On the limited memory BFGS method for large scale optimization
• Modifying the BFGS update by a new column scaling technique
• On the convergence of the DFP algorithm for unconstrained optimization when there are only two variables
• Broyden's quasi-Newton methods for a nonlinear system of equations and unconstrained optimization: a review and open problems
• The convergence of variable metric matrices in unconstrained optimization
• How bad are the BFGS and DFP methods when the objective function is quadratic?
• Updating Quasi-Newton Matrices with Limited Storage
• Self-Scaling Variable Metric (SSVM) Algorithms
• A Rapidly Convergent Descent Method for Minimization
• Convergence Conditions for Ascent Methods
• On the Convergence of the Variable Metric Algorithm
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