A Class of Approximate Inverse Preconditioners Based on Krylov-Subspace Methods for Large-Scale Nonconvex Optimization-Reference-Cited by-同舟云学术

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A Class of Approximate Inverse Preconditioners Based on Krylov-Subspace Methods for Large-Scale Nonconvex Optimization

Published:2020-01 Issue:3 Volume:30 Page:1954-1979
ISSN:1052-6234
Container-title:SIAM Journal on Optimization
language:en
Short-container-title:SIAM J. Optim.

Author:

Al-Baali Mehiddin,Caliciotti Andrea,Fasano Giovanni,Roma Massimo^ORCID

Funder

INdAM-GNCS Instituto Nazionale di Alta Matematica

Gruppo Nazionale per il Calcolo Scientifico

CNR-INSEAN Marine Technology Research Institute

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Subject

Theoretical Computer Science,Software

Link

https://epubs.siam.org/doi/pdf/10.1137/19M1256907

Reference49 articles.

1. Exploiting damped techniques for nonlinear conjugate gradient methods

2. Adaptively Preconditioned GMRES Algorithms

3. A Matrix-Free Preconditioner for Sparse Symmetric Positive Definite Systems and Least-Squares Problems

4. Preconditioning Techniques for Large Linear Systems: A Survey

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Descent Four-Term Conjugate Gradient Method with Global Convergence Properties for Large-Scale Unconstrained Optimisation Problems;Mathematical Problems in Engineering;2021-08-14

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