The Lanczos and Conjugate Gradient Algorithms: From Theory to Finite Precision Computations

By G. Meurant. 2006

168 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computational Theory and Mathematics, Mathematical Physics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Mariana Raykova
2. Borja Balle
3. Annick Sartenaer
4. Jean Tshimanga
5. Serge Gratton
6. A. T. Weaver

In Computational Theory and Mathematics

1. David E. Evans
2. Adrià Gascón
3. Serge Gratton
4. Samee Zahur
5. Mariana Raykova
6. Phillipp Schoppmann
7. Borja Balle
8. Jack Doerner

In Mathematical Physics

1. Annick Sartenaer
2. Jean Tshimanga
3. Serge Gratton
4. A. T. Weaver

Citations in the corpus

• Privacy-Preserving Distributed Linear Regression on High-Dimensional Data (2017, background): “For FP-CGD, we give the running time for 5, 10, 15, and 20 iterations.”
• Secure Linear Regression on Vertically Partitioned Datasets (2016, background): “See [31] for a thorough discussion on how to implement CGD with finite-precision arithmetic.”
• Limited‐memory preconditioners, with application to incremental four‐dimensional variational data assimilation (2008, methodology): “These vectors consist of descent directions and Ritz pairs, where the latter are derived from the equivalence of CG with the Lanczos method (Cullum and Willoughby, 2002; Golub and van Loan, 1996; Meurant, 2006).”