Example of Communications on Pure and Applied Mathematics format
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Example of Communications on Pure and Applied Mathematics format Example of Communications on Pure and Applied Mathematics format Example of Communications on Pure and Applied Mathematics format Example of Communications on Pure and Applied Mathematics format Example of Communications on Pure and Applied Mathematics format Example of Communications on Pure and Applied Mathematics format Example of Communications on Pure and Applied Mathematics format Example of Communications on Pure and Applied Mathematics format Example of Communications on Pure and Applied Mathematics format Example of Communications on Pure and Applied Mathematics format Example of Communications on Pure and Applied Mathematics format Example of Communications on Pure and Applied Mathematics format
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Communications on Pure and Applied Mathematics — Template for authors

Publisher: Wiley
Categories Rank Trend in last 3 yrs
Mathematics (all) #12 of 378 down down by 7 ranks
Applied Mathematics #46 of 548 down down by 35 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 214 Published Papers | 1164 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 09/06/2020
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Related Journals

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Quality:  
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Quality:  
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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.

2.676

15% from 2018

Impact factor for Communications on Pure and Applied Mathematics from 2016 - 2019
Year Value
2019 2.676
2018 3.138
2017 3.386
2016 3.793
graph view Graph view
table view Table view

5.4

11% from 2019

CiteRatio for Communications on Pure and Applied Mathematics from 2016 - 2020
Year Value
2020 5.4
2019 6.1
2018 5.8
2017 6.7
2016 7.3
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has decreased by 15% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has decreased by 11% 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.

3.12

27% from 2019

SJR for Communications on Pure and Applied Mathematics from 2016 - 2020
Year Value
2020 3.12
2019 4.271
2018 5.268
2017 4.911
2016 4.095
graph view Graph view
table view Table view

2.243

18% from 2019

SNIP for Communications on Pure and Applied Mathematics from 2016 - 2020
Year Value
2020 2.243
2019 2.751
2018 2.793
2017 3.033
2016 3.132
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 27% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has decreased by 18% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Communications on Pure and Applied Mathematics

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Wiley

Communications on Pure and Applied Mathematics

Communications on Pure and Applied Mathematics (ISSN 0010-3640) is published monthly, one volume per year, by John Wiley & Sons, Inc. Copyright © 2001 John Wiley & Sons, Inc. All rights reserved. The journal publishes papers originating at or solicited by the Courant Institute...... Read More

Mathematics

i
Last updated on
09 Jun 2020
i
ISSN
0010-3640
i
Impact Factor
Very High - 3.28
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

open accessOpen access Journal Article DOI: 10.1002/CPA.3160410705
Orthonormal bases of compactly supported wavelets
Ingrid Daubechies1

Abstract:

We construct orthonormal bases of compactly supported wavelets, with arbitrarily high regularity. The order of regularity increases linearly with the support width. We start by reviewing the concept of multiresolution analysis as well as several algorithms in vision decomposition and reconstruction. The construction then foll... We construct orthonormal bases of compactly supported wavelets, with arbitrarily high regularity. The order of regularity increases linearly with the support width. We start by reviewing the concept of multiresolution analysis as well as several algorithms in vision decomposition and reconstruction. The construction then follows from a synthesis of these different approaches. read more read less

Topics:

Orthonormal basis (58%)58% related to the paper, Spline wavelet (58%)58% related to the paper, Legendre wavelet (57%)57% related to the paper, Biorthogonal wavelet (52%)52% related to the paper, Wavelet (51%)51% related to the paper
View PDF
8,588 Citations
open accessOpen access Journal Article DOI: 10.1002/CPA.20124
Stable signal recovery from incomplete and inaccurate measurements
Emmanuel J. Candès1, Justin Romberg1, Terence Tao2

Abstract:

Suppose we wish to recover a vector x_0 Є R^m (e.g., a digital signal or image) from incomplete and contaminated observations y = Ax_0 + e; A is an n by m matrix with far fewer rows than columns (n « m) and e is an error term. Is it possible to recover x_0 accurately based on the data y? To recover x_0, we consider the solut... Suppose we wish to recover a vector x_0 Є R^m (e.g., a digital signal or image) from incomplete and contaminated observations y = Ax_0 + e; A is an n by m matrix with far fewer rows than columns (n « m) and e is an error term. Is it possible to recover x_0 accurately based on the data y? To recover x_0, we consider the solution x^# to the l_(1-)regularization problem min ‖x‖l_1 subject to ‖Ax - y‖l(2) ≤ Є, where Є is the size of the error term e. We show that if A obeys a uniform uncertainty principle (with unit-normed columns) and if the vector x_0 is sufficiently sparse, then the solution is within the noise level ‖x^# - x_0‖l_2 ≤ C Є. As a first example, suppose that A is a Gaussian random matrix; then stable recovery occurs for almost all such A's provided that the number of nonzeros of x_0 is of about the same order as the number of observations. As a second instance, suppose one observes few Fourier samples of x_0; then stable recovery occurs for almost any set of n coefficients provided that the number of nonzeros is of the order of n/[log m]^6. In the case where the error term vanishes, the recovery is of course exact, and this work actually provides novel insights into the exact recovery phenomenon discussed in earlier papers. The methodology also explains why one can also very nearly recover approximately sparse signals. read more read less

Topics:

Regularization (mathematics) (50%)50% related to the paper
View PDF
6,727 Citations
open accessOpen access Journal Article DOI: 10.1002/CPA.3160420503
Optimal approximations by piecewise smooth functions and associated variational problems
David Mumford1, Jayant Shah2

Abstract:

: This reprint will introduce and study the most basic properties of three new variational problems which are suggested by applications to computer vision. In computer vision, a fundamental problem is to appropriately decompose the domain R of a function g (x,y) of two variables. This problem starts by describing the physical... : This reprint will introduce and study the most basic properties of three new variational problems which are suggested by applications to computer vision. In computer vision, a fundamental problem is to appropriately decompose the domain R of a function g (x,y) of two variables. This problem starts by describing the physical situation which produces images: assume that a three-dimensional world is observed by an eye or camera from some point P and that g1(rho) represents the intensity of the light in this world approaching the point sub 1 from a direction rho. If one has a lens at P focusing this light on a retina or a film-in both cases a plane domain R in which we may introduce coordinates x, y then let g(x,y) be the strength of the light signal striking R at a point with coordinates (x,y); g(x,y) is essentially the same as sub 1 (rho) -possibly after a simple transformation given by the geometry of the imaging syste. The function g(x,y) defined on the plane domain R will be called an image. What sort of function is g? The light reflected off the surfaces Si of various solid objects O sub i visible from P will strike the domain R in various open subsets R sub i. When one object O1 is partially in front of another object O2 as seen from P, but some of object O2 appears as the background to the sides of O1, then the open sets R1 and R2 will have a common boundary (the 'edge' of object O1 in the image defined on R) and one usually expects the image g(x,y) to be discontinuous along this boundary. (JHD) read more read less

Topics:

Piecewise (52%)52% related to the paper, Boundary (topology) (51%)51% related to the paper, Domain (mathematical analysis) (51%)51% related to the paper, Transformation (function) (50%)50% related to the paper, Function (mathematics) (50%)50% related to the paper
View PDF
5,516 Citations
open accessOpen access Journal Article DOI: 10.1002/CPA.20042
An Iterative Thresholding Algorithm for Linear Inverse Problems with a Sparsity Constraint
Ingrid Daubechies1, Michel Defrise2, Christine De Mol3

Abstract:

We consider linear inverse problems where the solution is assumed to have a sparse expansion on an arbitrary preassigned orthonormal basis. We prove that replacing the usual quadratic regularizing penalties by weighted p-penalties on the coefficients of such expansions, with 1 ≤ p ≤ 2, still regularizes the problem. Use of su... We consider linear inverse problems where the solution is assumed to have a sparse expansion on an arbitrary preassigned orthonormal basis. We prove that replacing the usual quadratic regularizing penalties by weighted p-penalties on the coefficients of such expansions, with 1 ≤ p ≤ 2, still regularizes the problem. Use of such p-penalized problems with p < 2 is often advocated when one expects the underlying ideal noiseless solution to have a sparse expansion with respect to the basis under consideration. To compute the corresponding regularized solutions, we analyze an iterative algorithm that amounts to a Landweber iteration with thresholding (or nonlinear shrinkage) applied at each iteration step. We prove that this algorithm converges in norm. © 2004 Wiley Periodicals, Inc. read more read less

Topics:

Landweber iteration (62%)62% related to the paper, Iterative method (56%)56% related to the paper, Orthonormal basis (56%)56% related to the paper, Thresholding (53%)53% related to the paper, Inverse problem (52%)52% related to the paper
View PDF
4,339 Citations
Journal Article DOI: 10.1002/CPA.3160120405
Estimates near the boundary for solutions of elliptic partial differential equations satisfying general boundary conditions II
Shmuel Agmon1, Avron Douglis2, Louis Nirenberg3

Topics:

Free boundary problem (71%)71% related to the paper, Boundary value problem (70%)70% related to the paper, Robin boundary condition (68%)68% related to the paper, Mixed boundary condition (67%)67% related to the paper, Poincaré–Steklov operator (66%)66% related to the paper
3,953 Citations
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Communications on Pure and Applied Mathematics format uses apa citation style.

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Frequently asked questions

1. Can I write Communications on Pure and Applied Mathematics in LaTeX?

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

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Yes, the template is compliant with the Communications on Pure and Applied Mathematics 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 Communications on Pure and Applied Mathematics?

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 Communications on Pure and Applied Mathematics citation style.

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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 Communications on Pure and Applied Mathematics.

5. Can I use a manuscript in Communications on Pure and Applied Mathematics 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 Communications on Pure and Applied Mathematics that you can download at the end.

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7. Where can I find the template for the Communications on Pure and Applied Mathematics?

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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.

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SciSpace's Communications on Pure and Applied Mathematics 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.

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After writing your paper autoformatting in Communications on Pure and Applied Mathematics, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is Communications on Pure and Applied Mathematics'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 Communications on Pure and Applied Mathematics?

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 Communications on Pure and Applied Mathematics. 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 Communications on Pure and Applied Mathematics?

The 5 most common citation types in order of usage for Communications on Pure and Applied Mathematics 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 Communications on Pure and Applied Mathematics?

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16. Can I download Communications on Pure and Applied Mathematics 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 Communications on Pure and Applied Mathematics Endnote style according to Elsevier guidelines.

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