2009
DOI: 10.1137/070710469
|View full text |Cite
|
Sign up to set email alerts
|

The Quasi-Newton Least Squares Method: A New and Fast Secant Method Analyzed for Linear Systems

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
35
0

Year Published

2010
2010
2022
2022

Publication Types

Select...
9
1

Relationship

6
4

Authors

Journals

citations
Cited by 49 publications
(35 citation statements)
references
References 18 publications
0
35
0
Order By: Relevance
“…It is therefore more advantageous to approximate the inverse of the Jacobian by applying the least-squares technique introduced by [26] on a particular set of vectors, as will be explained below. This technique can also be used to solve linear systems as demonstrated in [37].…”
Section: Interface Quasi-newton Coupling Algorithmmentioning
confidence: 99%
“…It is therefore more advantageous to approximate the inverse of the Jacobian by applying the least-squares technique introduced by [26] on a particular set of vectors, as will be explained below. This technique can also be used to solve linear systems as demonstrated in [37].…”
Section: Interface Quasi-newton Coupling Algorithmmentioning
confidence: 99%
“…Because the inverse of the Jacobian is approximated, one avoids that a linear system with as dimension the number of degrees-of-freedom in the interface's position has to be solved in every quasi-Newton iteration. The approximation is constructed with the least-squares technique developed for nonlinear systems by Vierendeels et al [8] and applied to linear systems by Haelterman et al [40]. A matrix-free implementation of the least-squares technique is described in this work.…”
Section: Introductionmentioning
confidence: 99%
“…The analysis of piecewise linear regression with the breakpoint estimation was calculated to explain the time-relation changes of the parameters g s , C i , P N , and E. The time intervals were estimated by nonlinear methods according to Quasi-Newton and the lost function based on the least squares were proceeded (Haelterman et al, 2009). The relationships between the parameters were computed using the simple coefficient of correlation (r by Pearson).…”
Section: Discussionmentioning
confidence: 99%