An active set limited memory BFGS algorithm for large-scale bound constrained optimization

DOI10.1007/s00186-007-0199-0zbMath1145.90084OpenAlexW2074901996MaRDI QIDQ929334

Dong-hui Li, Yun-hai Xiao

Publication date: 17 June 2008

Published in: Mathematical Methods of Operations Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00186-007-0199-0

zbMATH Keywords

Global convergenceLimited memory BFGS methodBound constrained problem

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Nonlinear programming (90C30)

Related Items

Trust region algorithm with two subproblems for bound constrained problems ⋮ Limited memory BFGS algorithm for the matrix approximation problem in Frobenius norm ⋮ An inexact quasi-Newton algorithm for large-scale \(\ell_1\) optimization with box constraints ⋮ Gradient trust region algorithm with limited memory BFGS update for nonsmooth convex minimization ⋮ An active-set projected trust region algorithm for box constrained optimization problems ⋮ An active set limited memory BFGS algorithm for bound constrained optimization ⋮ Limited memory BFGS method with backtracking for symmetric nonlinear equations ⋮ Sufficient descent Polak-Ribière-Polyak conjugate gradient algorithm for large-scale box-constrained optimization ⋮ An active set quasi-Newton method with projected search for bound constrained minimization ⋮ An active-set projected trust-region algorithm with limited memory BFGS technique for box-constrained nonsmooth equations

Uses Software

• L-BFGS
• TRON
• KELLEY
• LBFGS-B

Cites Work

• 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
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• Large-scale active-set box-constrained optimization method with spectral projected gradients
• A Globally Convergent Augmented Lagrangian Algorithm for Optimization with General Constraints and Simple Bounds
• Convergence of Trust Region Algorithms for Optimization with Bounds when Strict Complementarity Does Not Hold
• Numerical methods for large-scale nonlinear optimization
• Global Convergence of a Class of Trust Region Algorithms for Optimization with Simple Bounds
• On the Identification of Active Constraints
• CUTE
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• Newton's Method for Large Bound-Constrained Optimization Problems
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• Extension of Davidon’s Variable Metric Method to Maximization Under Linear Inequality and Equality Constraints