Example of Acta Numerica format
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Example of Acta Numerica format Example of Acta Numerica format Example of Acta Numerica format Example of Acta Numerica format Example of Acta Numerica format Example of Acta Numerica format Example of Acta Numerica format Example of Acta Numerica format Example of Acta Numerica format Example of Acta Numerica format Example of Acta Numerica format Example of Acta Numerica format Example of Acta Numerica format
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Example of Acta Numerica format Example of Acta Numerica format Example of Acta Numerica format Example of Acta Numerica format Example of Acta Numerica format Example of Acta Numerica format Example of Acta Numerica format Example of Acta Numerica format Example of Acta Numerica format Example of Acta Numerica format Example of Acta Numerica format Example of Acta Numerica format Example of Acta Numerica format
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Acta Numerica — Template for authors

Categories Rank Trend in last 3 yrs
Mathematics (all) #1 of 378 -
Numerical Analysis #1 of 66 -
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 23 Published Papers | 399 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 18/06/2020
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Journal Performance & Insights

CiteRatio

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

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

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.

17.3

15% from 2019

CiteRatio for Acta Numerica from 2016 - 2020
Year Value
2020 17.3
2019 15.1
2018 17.2
2017 13.3
2016 15.4
graph view Graph view
table view Table view

3.117

28% from 2019

SJR for Acta Numerica from 2016 - 2020
Year Value
2020 3.117
2019 4.358
2018 4.843
2017 6.709
2016 6.354
graph view Graph view
table view Table view

7.052

5% from 2019

SNIP for Acta Numerica from 2016 - 2020
Year Value
2020 7.052
2019 6.736
2018 7.612
2017 6.212
2016 6.678
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has increased by 15% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

insights Insights

  • SJR of this journal has decreased by 28% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 5% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Acta Numerica

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Cambridge University Press

Acta Numerica

Acta Numerica is the top-cited journal for the last two years in MathSciNet. Its annual collection of review articles includes survey papers by leading researchers in numerical analysis and scientific computing. The papers present overviews of recent advances and provide state...... Read More

Mathematics

i
Last updated on
17 Jun 2020
i
ISSN
0962-4929
i
Impact Factor
Maximum - 6.458
i
Open Access
No
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
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Bibliography Name
unsrt
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Citation Type
Numbered
[25]
i
Bibliography Example
G E Blonder, M Tinkham, and T M Klapwijk. Transition from metallic to tunneling regimes in superconducting microconstrictions: Excess current, charge imbalance, and supercurrent conversion. Phys. Rev. B, 25(7):4515–4532, 1982. 10.1103/PhysRevB.25.4515.

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.1017/S0962492902000077
The immersed boundary method
Charles S. Peskin1
01 Jan 2002 - Acta Numerica

Abstract:

This paper is concerned with the mathematical structure of the immersed boundary (IB) method, which is intended for the computer simulation of fluid–structure interaction, especially in biological fluid dynamics. The IB formulation of such problems, derived here from the principle of least action, involves both Eulerian and L... This paper is concerned with the mathematical structure of the immersed boundary (IB) method, which is intended for the computer simulation of fluid–structure interaction, especially in biological fluid dynamics. The IB formulation of such problems, derived here from the principle of least action, involves both Eulerian and Lagrangian variables, linked by the Dirac delta function. Spatial discretization of the IB equations is based on a fixed Cartesian mesh for the Eulerian variables, and a moving curvilinear mesh for the Lagrangian variables. The two types of variables are linked by interaction equations that involve a smoothed approximation to the Dirac delta function. Eulerian/Lagrangian identities govern the transfer of data from one mesh to the other. Temporal discretization is by a second-order Runge–Kutta method. Current and future research directions are pointed out, and applications of the IB method are briefly discussed. Introduction The immersed boundary (IB) method was introduced to study flow patterns around heart valves and has evolved into a generally useful method for problems of fluid–structure interaction. The IB method is both a mathematical formulation and a numerical scheme. The mathematical formulation employs a mixture of Eulerian and Lagrangian variables. These are related by interaction equations in which the Dirac delta function plays a prominent role. In the numerical scheme motivated by the IB formulation, the Eulerian variables are defined on a fixed Cartesian mesh, and the Lagrangian variables are defined on a curvilinear mesh that moves freely through the fixed Cartesian mesh without being constrained to adapt to it in any way at all. read more read less

Topics:

Immersed boundary method (64%)64% related to the paper, Eulerian path (55%)55% related to the paper, Temporal discretization (54%)54% related to the paper, Curvilinear coordinates (54%)54% related to the paper, Discretization (53%)53% related to the paper
View PDF
4,164 Citations
open accessOpen access Journal Article DOI: 10.1017/S0962492904000212
Numerical solution of saddle point problems
Michele Benzi1, Gene H. Golub2, Jörg Liesen3
01 May 2005 - Acta Numerica

Abstract:

Large linear systems of saddle point type arise in a wide variety of applications throughout computational science and engineering. Due to their indefiniteness and often poor spectral properties, such linear systems represent a significant challenge for solver developers. In recent years there has been a surge of interest in ... Large linear systems of saddle point type arise in a wide variety of applications throughout computational science and engineering. Due to their indefiniteness and often poor spectral properties, such linear systems represent a significant challenge for solver developers. In recent years there has been a surge of interest in saddle point problems, and numerous solution techniques have been proposed for this type of system. The aim of this paper is to present and discuss a large selection of solution methods for linear systems in saddle point form, with an emphasis on iterative methods for large and sparse problems. read more read less

Topics:

Saddle point (65%)65% related to the paper, Equilibrium point (58%)58% related to the paper, Solver (53%)53% related to the paper, Linear system (53%)53% related to the paper, Iterative method (52%)52% related to the paper
View PDF
2,253 Citations
Journal Article DOI: 10.1017/S0962492900002518
Sequential Quadratic Programming
Paul T. Boggs1, Jon W. Tolle2
01 Jan 1995 - Acta Numerica

Abstract:

Since its popularization in the late 1970s, Sequential Quadratic Programming (SQP) has arguably become the most successful method for solving nonlinearly constrained optimization problems. As with most optimization methods, SQP is not a single algorithm, but rather a conceptual method from which numerous specific algorithms h... Since its popularization in the late 1970s, Sequential Quadratic Programming (SQP) has arguably become the most successful method for solving nonlinearly constrained optimization problems. As with most optimization methods, SQP is not a single algorithm, but rather a conceptual method from which numerous specific algorithms have evolved. Backed by a solid theoretical and computational foundation, both commercial and public-domain SQP algorithms have been developed and used to solve a remarkably large set of important practical problems. Recently large-scale versions have been devised and tested with promising results. read more read less

Topics:

Sequential quadratic programming (69%)69% related to the paper, Quadratic programming (69%)69% related to the paper, Quadratically constrained quadratic program (69%)69% related to the paper, Active set method (66%)66% related to the paper, Second-order cone programming (66%)66% related to the paper
1,765 Citations
Journal Article DOI: 10.1017/S0962492900002804
Monte Carlo and quasi-Monte Carlo methods
Russel E. Caflisch1
01 Jan 1998 - Acta Numerica

Abstract:

Monte Carlo is one of the most versatile and widely used numerical methods. Its convergence rate, O(N−1/2), is independent of dimension, which shows Monte Carlo to be very robust but also slow. This article presents an introduction to Monte Carlo methods for integration problems, including convergence theory, sampling methods... Monte Carlo is one of the most versatile and widely used numerical methods. Its convergence rate, O(N−1/2), is independent of dimension, which shows Monte Carlo to be very robust but also slow. This article presents an introduction to Monte Carlo methods for integration problems, including convergence theory, sampling methods and variance reduction techniques. Accelerated convergence for Monte Carlo quadrature is attained using quasi-random (also called low-discrepancy) sequences, which are a deterministic alternative to random or pseudo-random sequences. The points in a quasi-random sequence are correlated to provide greater uniformity. The resulting quadrature method, called quasi-Monte Carlo, has a convergence rate of approximately O((logN)kN−1). For quasi-Monte Carlo, both theoretical error estimates and practical limitations are presented. Although the emphasis in this article is on integration, Monte Carlo simulation of rarefied gas dynamics is also discussed. In the limit of small mean free path (that is, the fluid dynamic limit), Monte Carlo loses its effectiveness because the collisional distance is much less than the fluid dynamic length scale. Computational examples are presented throughout the text to illustrate the theory. A number of open problems are described. read more read less

Topics:

Dynamic Monte Carlo method (77%)77% related to the paper, Hybrid Monte Carlo (77%)77% related to the paper, Quasi-Monte Carlo method (77%)77% related to the paper, Monte Carlo molecular modeling (76%)76% related to the paper, Monte Carlo method (76%)76% related to the paper
View PDF
1,708 Citations
open accessOpen access Journal Article DOI: 10.1017/S0962492910000061
Inverse problems: A Bayesian perspective
Andrew M. Stuart1
01 May 2010 - Acta Numerica

Abstract:

The subject of inverse problems in differential equations is of enormous practical importance, and has also generated substantial mathematical and computational innovation. Typically some form of regularization is required to ameliorate ill-posed behaviour. In this article we review the Bayesian approach to regularization, de... The subject of inverse problems in differential equations is of enormous practical importance, and has also generated substantial mathematical and computational innovation. Typically some form of regularization is required to ameliorate ill-posed behaviour. In this article we review the Bayesian approach to regularization, developing a function space viewpoint on the subject. This approach allows for a full characterization of all possible solutions, and their relative probabilities, whilst simultaneously forcing significant modelling issues to be addressed in a clear and precise fashion. Although expensive to implement, this approach is starting to lie within the range of the available computational resources in many application areas. It also allows for the quantification of uncertainty and risk, something which is increasingly demanded by these applications. Furthermore, the approach is conceptually important for the understanding of simpler, computationally expedient approaches to inverse problems. read more read less

Topics:

Inverse problem (52%)52% related to the paper
View PDF
1,695 Citations
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Acta Numerica format uses unsrt citation style.

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Frequently asked questions

1. Can I write Acta Numerica in LaTeX?

Absolutely not! Our tool has been designed to help you focus on writing. You can write your entire paper as per the Acta Numerica guidelines and auto format it.

2. Do you follow the Acta Numerica guidelines?

Yes, the template is compliant with the Acta Numerica guidelines. Our experts at SciSpace ensure that. If there are any changes to the journal's guidelines, we'll change our algorithm accordingly.

3. Can I cite my article in multiple styles in Acta Numerica?

Of course! We support all the top citation styles, such as APA style, MLA style, Vancouver style, Harvard style, and Chicago style. For example, when you write your paper and hit autoformat, our system will automatically update your article as per the Acta Numerica citation style.

4. Can I use the Acta Numerica templates for free?

Sign up for our free trial, and you'll be able to use all our features for seven days. You'll see how helpful they are and how inexpensive they are compared to other options, Especially for Acta Numerica.

5. Can I use a manuscript in Acta Numerica that I have written in MS Word?

Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper Acta Numerica that you can download at the end.

6. How long does it usually take you to format my papers in Acta Numerica?

It only takes a matter of seconds to edit your manuscript. Besides that, our intuitive editor saves you from writing and formatting it in Acta Numerica.

7. Where can I find the template for the Acta Numerica?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Acta Numerica's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

8. Can I reformat my paper to fit the Acta Numerica's guidelines?

Of course! You can do this using our intuitive editor. It's very easy. If you need help, our support team is always ready to assist you.

9. Acta Numerica an online tool or is there a desktop version?

SciSpace's Acta Numerica is currently available as an online tool. We're developing a desktop version, too. You can request (or upvote) any features that you think would be helpful for you and other researchers in the "feature request" section of your account once you've signed up with us.

10. I cannot find my template in your gallery. Can you create it for me like Acta Numerica?

Sure. You can request any template and we'll have it setup within a few days. You can find the request box in Journal Gallery on the right side bar under the heading, "Couldn't find the format you were looking for like Acta Numerica?”

11. What is the output that I would get after using Acta Numerica?

After writing your paper autoformatting in Acta Numerica, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is Acta Numerica's impact factor high enough that I should try publishing my article there?

To be honest, the answer is no. The impact factor is one of the many elements that determine the quality of a journal. Few of these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for Acta Numerica?

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 Acta Numerica. 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 Acta Numerica?

The 5 most common citation types in order of usage for Acta Numerica are:.

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

15. How do I submit my article to the Acta Numerica?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Acta Numerica's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download Acta Numerica in Endnote format?

Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in Acta Numerica Endnote style according to Elsevier guidelines.

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