Local andQ-superlinear convergence of a class of collinear scaling algorithms that extends quasi-newton methods with broyden's bounded-⊘ class of updates† ‡

DOI10.1080/02331939208843768zbMath0815.65086OpenAlexW2033623479MaRDI QIDQ4327895

Ariyawansa, K. A., D. T. M. Lau

Publication date: 30 June 1995

Published in: Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02331939208843768

zbMATH Keywords

BFGS methodquasi-Newton methodsBroyden familyDFP methodcollinear scaling algorithmslocal and q-superlinear convergenceuconstrained minimization

Mathematics Subject Classification ID

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

Related Items (8)

On Davidon's collinear scaling algorithms for optimization ⋮ A quasi-Newton trust region method with a new conic model for the unconstrained optimization ⋮ A trust region method with a conic model for nonlinearly constrained optimization ⋮ A numerical evaluation of some collinear scaling algorithms for unconstrained ⋮ A trust-region method with a conic model for unconstrained optimization ⋮ Nonmonotone adaptive trust region method based on simple conic model for unconstrained optimization ⋮ An adaptive conic trust-region method for unconstrained optimization ⋮ A conic trust-region method and its convergence properties

Cites Work

• Local convergence analysis for partitioned quasi-Newton updates
• Deriving collinear scaling algorithms as extensions of quasi-Newton methods and the local convergence of DFP- and BFGS-related collinear scaling algorithms
• Convergence Theorems for Least-Change Secant Update Methods
• Least Change Secant Updates for Quasi-Newton Methods
• Conic Approximations and Collinear Scalings for Optimizers
• The Q-Superlinear Convergence of a Collinear Scaling Algorithm for Unconstrained Optimization
• Superlinear convergence of Broyden's boundedθ-class of methods
• Quasi-Newton Methods, Motivation and Theory
• On the Local and Superlinear Convergence of Quasi-Newton Methods
• Quadratic Termination Properties of Minimization Algorithms I. Statement and Discussion of Results

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