Perspectives on self-scaling variable metric algorithms

DOI10.1007/BF00934764zbMath0458.90059MaRDI QIDQ1151823

S. H. Smith

Publication date: 1982

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

zbMATH Keywords

unconstrained optimizationquasi-Newton methodsmetric methodsperformance characteristicsL-function modelnonquadratic modelsself-scaling variable

Mathematics Subject Classification ID

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

Related Items (6)

Hessian initialization strategies for \(\ell \)-BFGS solving non-linear inverse problems ⋮ Convergence analysis of the self-dual optimally conditioned ssvm method of oren-spedicato ⋮ Analysis of a self-scaling quasi-Newton method ⋮ A new family of conjugate gradient methods for unconstrained optimization ⋮ The revised DFP algorithm without exact line search ⋮ A CLASS OF DFP ALGORITHMS WITH REVISED SEARCH DIRECTION

Cites Work

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• A variable metric-method for function minimization derived from invariancy to nonlinear scaling
• On the selection of parameters in Self Scaling Variable Metric Algorithms
• Self-Scaling Variable Metric Algorithms without Line Search for Unconstrained Minimization
• Self-Scaling Variable Metric (SSVM) Algorithms
• Self-Scaling Variable Metric (SSVM) Algorithms
• Optimal conditioning of self-scaling variable Metric algorithms
• Matrix conditioning and nonlinear optimization
• An assessment of two approaches to variable metric methods
• The Convergence of a Class of Double-rank Minimization Algorithms
• A new approach to variable metric algorithms
• Minimization Algorithms Making Use of Non-quadratic Properties of the Objective Function

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