Shifted limited-memory variable metric methods for large-scale unconstrained optimization

DOI10.1016/j.cam.2005.02.010zbMath1080.65050OpenAlexW2078606285MaRDI QIDQ2573468

Ladislav Lukšan, Jan Vlček

Publication date: 22 November 2005

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

Full work available at URL: https://doi.org/10.1016/j.cam.2005.02.010

zbMATH Keywords

quasi-Newton methodsGlobal convergenceNumerical resultsUnconstrained minimizationLimited-memory methodsVariable metric methods

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)

A new class of supermemory gradient methods ⋮ Parameter estimation with a novel gradient-based optimization method for biological lattice-gas cellular automaton models ⋮ A subspace SQP method for equality constrained optimization

Uses Software

• L-BFGS

Cites Work

• Unnamed Item
• Extra-updates criterion for the limited memory BFGS algorithm for large scale nonlinear optimization
• On the limited memory BFGS method for large scale optimization
• Representations of quasi-Newton matrices and their use in limited memory methods
• Variable metric methods for unconstrained optimization and nonlinear least squares
• Updating Quasi-Newton Matrices with Limited Storage
• Global Convergence Properties of Conjugate Gradient Methods for Optimization
• Self-Scaling Variable Metric (SSVM) Algorithms
• Matrix conditioning and nonlinear optimization
• Limited-Memory Reduced-Hessian Methods for Large-Scale Unconstrained Optimization
• Minimization Algorithms Making Use of Non-quadratic Properties of the Objective Function
• Benchmarking optimization software with performance profiles.

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