Issues on the use of a modified bunch and Kaufman decomposition for large scale Newton's equation
From MaRDI portal
Publication:2023682
DOI10.1007/s10589-020-00225-8zbMath1466.90050OpenAlexW3087285045MaRDI QIDQ2023682
Florian A. Potra, Giovanni Fasano, Andrea Caliciotti, Massimo Roma
Publication date: 3 May 2021
Published in: Computational Optimization and Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10589-020-00225-8
truncated Newton methodlarge scale optimizationBunch and Kaufman decompositiongradient-related directions
Related Items
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Preconditioning Newton-Krylov methods in nonconvex large scale optimization
- 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
- Planar methods and grossone for the conjugate gradient breakdown in nonlinear programming
- CUTEst: a constrained and unconstrained testing environment with safe threads for mathematical optimization
- An adaptive truncation criterion, for linesearch-based truncated Newton methods in large scale nonconvex optimization
- Iterative computation of negative curvature directions in large scale optimization
- On solving sparse symmetric linear systems whose definiteness is unknown
- 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
- Inexact Newton Methods
- Some Stable Methods for Calculating Inertia and Solving Symmetric Linear Systems
- Trust Region Methods
- Benchmarking optimization software with performance profiles.