Truncated Newton method for sparse unconstrained optimization using automatic differentiation

DOI10.1007/BF00940007zbMath0632.90060OpenAlexW2007007407MaRDI QIDQ1095800

L. C. W. Dixon, R. C. Price

Publication date: 1989

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

Full work available at URL: https://doi.org/10.1007/bf00940007

zbMATH Keywords

automatic differentiationapproximate solutionsparsityHessian matrixconjugate direction methodtruncated Newton algorithmlarge complex optimization

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Nonlinear programming (90C30) Numerical methods based on nonlinear programming (49M37)

Related Items (10)

Geometric approach to Fletcher's ideal penalty function ⋮ Optimality conditions and optimization methods for quartic polynomial optimization ⋮ Jacobian-free implicit inner-iteration preconditioner for nonlinear least squares problems ⋮ Efficient solution of many instances of a simulation-based optimization problem utilizing a partition of the decision space ⋮ Unnamed Item ⋮ Numerical solution of dynamic optimization problems using parametrization and \(\text{Op}^{\text{ti}}\text{A}\) software ⋮ On interval enclosures using slope arithmetic ⋮ Automatic differentiation of large sparse systems ⋮ Automatic differentiation: Reduced gradient and reduced Hessian matrix ⋮ Effect of rounding errors on the variable metric method

Cites Work

• Automatic differentiation: techniques and applications
• Truncated-Newton algorithms for large-scale unconstrained optimization
• The Conjugate Gradient Method and Trust Regions in Large Scale Optimization
• Inexact Newton Methods

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