Preconditioning Newton-Krylov methods in nonconvex large scale optimization

DOI10.1007/s10589-013-9563-6zbMath1314.90063OpenAlexW1976213004MaRDI QIDQ377728

Massimo Roma, Giovanni Fasano

Publication date: 7 November 2013

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

Full work available at URL: https://doi.org/10.1007/s10589-013-9563-6

zbMATH Keywords

preconditioningNewton-Krylov methodsnonconvex problemsKrylov subspace methodslarge scale optimization

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Large-scale problems in mathematical programming (90C06) Nonconvex programming, global optimization (90C26)

Related Items

Efficient tridiagonal preconditioner for the matrix-free truncated Newton method, A novel class of approximate inverse preconditioners for large positive definite linear systems in optimization, Updating Constraint Preconditioners for KKT Systems in Quadratic Programming Via Low-Rank Corrections, An adaptive truncation criterion, for linesearch-based truncated Newton methods in large scale nonconvex optimization, Limited memory preconditioners for symmetric indefinite problems with application to structural mechanics, Preconditioned nonlinear conjugate gradient methods based on a modified secant equation, Conjugate direction methods and polarity for quadratic hypersurfaces, An Improvement of the Pivoting Strategy in the Bunch and Kaufman Decomposition, Within Truncated Newton Methods, A Class of Approximate Inverse Preconditioners Based on Krylov-Subspace Methods for Large-Scale Nonconvex Optimization, Exploiting damped techniques for nonlinear conjugate gradient methods, Novel preconditioners based on quasi-Newton updates for nonlinear conjugate gradient methods, Issues on the use of a modified bunch and Kaufman decomposition for large scale Newton's equation, Large-scale unconstrained optimization using separable cubic modeling and matrix-free subspace minimization, A framework of conjugate direction methods for symmetric linear systems in optimization

Uses Software

• PREQN
• CUTEr
• SifDec
• KELLEY
• tn

Cites Work

• Adaptive preconditioners for nonlinear systems of equations
• Assessing a search direction within a truncated Newton method
• Lanczos conjugate-gradient method and pseudoinverse computation on indefinite and singular systems
• A truncated Newton method with non-monotone line search for unconstrained optimization
• A survey of truncated-Newton methods
• Restarted GMRES preconditioned by deflation
• Iterative computation of negative curvature directions in large scale optimization
• Planar conjugate gradient algorithm for large-scale unconstrained optimization. I: Theory
• Planar conjugate gradient algorithm for large-scale unconstrained optimization. II: Application
• Truncated-Newton algorithms for large-scale unconstrained optimization
• On A Class of Limited Memory Preconditioners For Large Scale Linear Systems With Multiple Right-Hand Sides
• Dynamic scaling based preconditioning for truncated Newton methods in large scale unconstrained optimization
• Preconditioning of Truncated-Newton Methods
• Preconditioners for Indefinite Systems Arising in Optimization
• Adaptively Preconditioned GMRES Algorithms
• Trust Region Methods
• Automatic Preconditioning by Limited Memory Quasi-Newton Updating
• Exploiting negative curvature directions in linesearch methods for unconstrained optimization
• Algorithm 809: PREQN
• CUTEr and SifDec
• On the Sensitivity of Some Spectral Preconditioners
• Benchmarking optimization software with performance profiles.
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item