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Example of Numerical Linear Algebra with Applications format Example of Numerical Linear Algebra with Applications format Example of Numerical Linear Algebra with Applications format Example of Numerical Linear Algebra with Applications format Example of Numerical Linear Algebra with Applications format Example of Numerical Linear Algebra with Applications format Example of Numerical Linear Algebra with Applications format Example of Numerical Linear Algebra with Applications format Example of Numerical Linear Algebra with Applications format Example of Numerical Linear Algebra with Applications format Example of Numerical Linear Algebra with Applications format Example of Numerical Linear Algebra with Applications format Example of Numerical Linear Algebra with Applications format Example of Numerical Linear Algebra with Applications format Example of Numerical Linear Algebra with Applications format Example of Numerical Linear Algebra with Applications format Example of Numerical Linear Algebra with Applications format Example of Numerical Linear Algebra with Applications format Example of Numerical Linear Algebra with Applications format
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Numerical Linear Algebra with Applications — Template for authors

Publisher: Wiley
Categories Rank Trend in last 3 yrs
Algebra and Number Theory #8 of 109 down down by 3 ranks
Applied Mathematics #134 of 548 down down by 11 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 225 Published Papers | 744 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 16/06/2020
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Related Journals

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Quality:  
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CiteRatio: 2.5
SJR: 0.685
SNIP: 1.143
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Elsevier

Quality:  
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CiteRatio: 2.7
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Elsevier

Quality:  
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CiteRatio: 2.3
SJR: 0.656
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open access Open Access

Taylor and Francis

Quality:  
High
CiteRatio: 1.4
SJR: 0.214
SNIP: 0.992

Journal Performance & Insights

Impact Factor

CiteRatio

Determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

A measure of average citations received per peer-reviewed paper published in the journal.

1.373

6% from 2018

Impact factor for Numerical Linear Algebra with Applications from 2016 - 2019
Year Value
2019 1.373
2018 1.298
2017 1.281
2016 1.303
graph view Graph view
table view Table view

3.3

22% from 2019

CiteRatio for Numerical Linear Algebra with Applications from 2016 - 2020
Year Value
2020 3.3
2019 2.7
2018 2.7
2017 2.7
2016 3.2
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has increased by 6% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 22% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

1.02

13% from 2019

SJR for Numerical Linear Algebra with Applications from 2016 - 2020
Year Value
2020 1.02
2019 0.899
2018 0.764
2017 1.104
2016 1.29
graph view Graph view
table view Table view

1.48

24% from 2019

SNIP for Numerical Linear Algebra with Applications from 2016 - 2020
Year Value
2020 1.48
2019 1.193
2018 1.266
2017 1.058
2016 1.236
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has increased by 13% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 24% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Numerical Linear Algebra with Applications

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Wiley

Numerical Linear Algebra with Applications

This journal is directed at researchers in Numerical Analysis, Computer Sciences and Natural Sciences, engineers and economists who either take part in the development of methods in Numerical Linear Algebra or use such methods in their research. Topics covered include (but are...... Read More

Mathematics

i
Last updated on
16 Jun 2020
i
ISSN
1070-5325
i
Impact Factor
High - 1.427
i
Open Access
Yes
i
Sherpa RoMEO Archiving Policy
Yellow faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
i
Bibliography Name
apa
i
Citation Type
Numbered
[25]
i
Bibliography Example
Beenakker, C.W.J. (2006) Specular andreev reflection in graphene.Phys. Rev. Lett., 97 (6), 067 007. URL 10.1103/PhysRevLett.97.067007.

Top papers written in this journal

Journal Article DOI: 10.1002/NLA.1680010405
ILUT: A dual threshold incomplete LU factorization
Yousef Saad1

Abstract:

In this paper we describe an Incomplete LU factorization technique based on a strategy which combines two heuristics. This ILUT factorization extends the usual ILU(O) factorization without using the concept of level of fill-in. There are two traditional ways of developing incomplete factorization preconditioners. The first us... In this paper we describe an Incomplete LU factorization technique based on a strategy which combines two heuristics. This ILUT factorization extends the usual ILU(O) factorization without using the concept of level of fill-in. There are two traditional ways of developing incomplete factorization preconditioners. The first uses a symbolic factorization approach in which a level of fill is attributed to each fill-in element using only the graph of the matrix. Then each fill-in that is introduced is dropped whenever its level of fill exceeds a certain threshold. The second class of methods consists of techniques derived from modifications of a given direct solver by including a dropoff rule, based on the numerical size of the fill-ins introduced, traditionally referred to as threshold preconditioners. The first type of approach may not be reliable for indefinite problems, since it does not consider numerical values. The second is often far more expensive than the standard ILU(O). The strategy we propose is a compromise between these two extremes. read more read less

Topics:

Incomplete LU factorization (72%)72% related to the paper, Incomplete Cholesky factorization (65%)65% related to the paper, Dixon's factorization method (60%)60% related to the paper, Factorization (56%)56% related to the paper
685 Citations
open accessOpen access Journal Article DOI: 10.1002/NLA.499
Recent computational developments in krylov subspace methods for linear systems
Valeria Simoncini1, Daniel B. Szyld2

Abstract:

Many advances in the development of Krylov subspace methods for the iterative solution of linear systems during the last decade and a half are reviewed. These new developments include different versions of restarted, augmented, deflated, flexible, nested, and inexact methods. Also reviewed are methods specifically tailored to... Many advances in the development of Krylov subspace methods for the iterative solution of linear systems during the last decade and a half are reviewed. These new developments include different versions of restarted, augmented, deflated, flexible, nested, and inexact methods. Also reviewed are methods specifically tailored to systems with special properties such as special forms of symmetry and those depending on one or more parameters. Copyright © 2006 John Wiley & Sons, Ltd. read more read less

Topics:

Iterative method (69%)69% related to the paper, Krylov subspace (68%)68% related to the paper, Generalized minimal residual method (68%)68% related to the paper, Conjugate residual method (67%)67% related to the paper, Linear system (53%)53% related to the paper
View PDF
408 Citations
Journal Article DOI: 10.1002/NLA.622
Numerical solution of large‐scale Lyapunov equations, Riccati equations, and linear‐quadratic optimal control problems
Peter Benner, Jing-Rebecca Li, Thilo Penzl

Abstract:

We study large-scale, continuous-time linear time-invariant control systems with a sparse or structured state matrix and a relatively small number of inputs and outputs. The main contributions of this paper are numerical algorithms for the solution of large algebraic Lyapunov and Riccati equations and linearquadratic optimal ... We study large-scale, continuous-time linear time-invariant control systems with a sparse or structured state matrix and a relatively small number of inputs and outputs. The main contributions of this paper are numerical algorithms for the solution of large algebraic Lyapunov and Riccati equations and linearquadratic optimal control problems, which arise from such systems. First, we review an alternating direction implicit iteration-based method to compute approximate low-rank Cholesky factors of the solution matrix of large-scale Lyapunov equations, and we propose a refined version of this algorithm. Second, a combination of this method with a variant of Newton's method (in this context also called Kleinman iteration) results in an algorithm for the solution of large-scale Riccati equations. Third, we describe an implicit version of this algorithm for the solution of linear-quadratic optimal control problems, which computes the feedback directly without solving the underlying algebraic Riccati equation explicitly. Our algorithms are efficient with respect to both memory and computation. In particular, they can be applied to problems of very large scale, where square, dense matrices of the system order cannot be stored in the computer memory. We study the performance of our algorithms in numerical experiments. read more read less

Topics:

Algebraic Riccati equation (70%)70% related to the paper, Linear-quadratic-Gaussian control (65%)65% related to the paper, Linear-quadratic regulator (64%)64% related to the paper, Lyapunov equation (62%)62% related to the paper, Optimal control (58%)58% related to the paper
View PDF
335 Citations
Journal Article DOI: 10.1002/NLA.691
Fast algorithms for hierarchically semiseparable matrices
Jianlin Xia1, Shivkumar Chandrasekaran2, Ming Gu3, Xiaoye S. Li4

Abstract:

Semiseparable matrices and many other rank-structured matrices have been widely used in developing new fast matrix algorithms. In this paper, we generalize the hierarchically semiseparable (HSS) matrix representations and propose some fast algorithms for HSS matrices. We represent HSS matrices in terms of general binary HSS t... Semiseparable matrices and many other rank-structured matrices have been widely used in developing new fast matrix algorithms. In this paper, we generalize the hierarchically semiseparable (HSS) matrix representations and propose some fast algorithms for HSS matrices. We represent HSS matrices in terms of general binary HSS trees and use simplified postordering notation for HSS forms. Fast HSS algorithms including new HSS structure generation and HSS form Cholesky factorization are developed. Moreover, we provide a new linear complexity explicit ULV factorization algorithm for symmetric positive definite HSS matrices with a low-rank property. The corresponding factors can be used to solve the HSS systems also in linear complexity. Numerical examples demonstrate the efficiency of the algorithms. All these algorithms have nice data locality. They are useful in developing fast-structured numerical methods for large discretized PDEs (such as elliptic equations), integral equations, eigenvalue problems, etc. Some applications are shown. Copyright q 2009 John Wiley & Sons, Ltd. read more read less

Topics:

Matrix (mathematics) (54%)54% related to the paper, Cholesky decomposition (53%)53% related to the paper, Hierarchical matrix (51%)51% related to the paper, Eigenvalues and eigenvectors (50%)50% related to the paper
View PDF
318 Citations
A scalable dual‐primal domain decomposition method
Charbel Farhat1, Michael Lesoinne1, Kendall H. Pierson1

Abstract:

We blend dual and primal domain decomposition approaches to construct a fast iterative method for the solution of large-scale systems of equations arising from the finite element discretization of second- and fourth-order partial differential equations. We show numerically that our method is scalable with respect to the mesh ... We blend dual and primal domain decomposition approaches to construct a fast iterative method for the solution of large-scale systems of equations arising from the finite element discretization of second- and fourth-order partial differential equations. We show numerically that our method is scalable with respect to the mesh size, the subdomain size, and the number of elements per subdomain. We apply it to the solution of several realistic structural mechanics problems, and report on parallel performance results obtained on an Origin 2000 system, as well as the ASCI Option Red supercomputer. Copyright © 2000 John Wiley & Sons, Ltd. read more read less

Topics:

Balancing domain decomposition method (64%)64% related to the paper, FETI-DP (62%)62% related to the paper, Domain decomposition methods (61%)61% related to the paper, Mortar methods (60%)60% related to the paper, BDDC (57%)57% related to the paper
307 Citations
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Frequently asked questions

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13. What is Sherpa RoMEO Archiving Policy for Numerical Linear Algebra with Applications?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for Numerical Linear Algebra with Applications. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In Numerical Linear Algebra with Applications?

The 5 most common citation types in order of usage for Numerical Linear Algebra with Applications are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

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