Example of Inverse Problems format
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Example of Inverse Problems format Example of Inverse Problems format Example of Inverse Problems format Example of Inverse Problems format Example of Inverse Problems format Example of Inverse Problems format
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Example of Inverse Problems format Example of Inverse Problems format Example of Inverse Problems format Example of Inverse Problems format Example of Inverse Problems format Example of Inverse Problems format
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This content is only for preview purposes. The original open access content can be found here.
open access Open Access

Inverse Problems — Template for authors

Publisher: IOP Publishing
Categories Rank Trend in last 3 yrs
Mathematical Physics #11 of 67 down down by 2 ranks
Applied Mathematics #107 of 548 down down by 19 ranks
Theoretical Computer Science #39 of 120 down down by 5 ranks
Computer Science Applications #245 of 693 down down by 50 ranks
Signal Processing #48 of 108 down down by 10 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 733 Published Papers | 2731 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 22/07/2020
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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.

3.7

12% from 2019

CiteRatio for Inverse Problems from 2016 - 2020
Year Value
2020 3.7
2019 3.3
2018 3.1
2017 3.1
2016 3.5
graph view Graph view
table view Table view

1.003

18% from 2019

SJR for Inverse Problems from 2016 - 2020
Year Value
2020 1.003
2019 1.23
2018 1.046
2017 1.209
2016 1.49
graph view Graph view
table view Table view

1.394

2% from 2019

SNIP for Inverse Problems from 2016 - 2020
Year Value
2020 1.394
2019 1.364
2018 1.362
2017 1.463
2016 1.449
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has increased by 12% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

insights Insights

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

insights Insights

  • SNIP of this journal has increased by 2% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Inverse Problems

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IOP Publishing

Inverse Problems

An interdisciplinary journal combining mathematical and experimental papers on inverse problems with theoretical, numerical and practical approaches to their solution. As well as applied mathematicians, physical scientists and engineers the readership includes those working in...... Read More

Mathematics

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Last updated on
22 Jul 2020
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ISSN
0266-5611
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Impact Factor
High - 1.491
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Acceptance Rate
Not provided
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Frequency
Not provided
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Open Access
No
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Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
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Endnote Style
Download Available
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Bibliography Name
iopart-num
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Citation Type
Numbered
[25]
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Bibliography Example
Beenakker C W J 2006 Phys. Rev. Lett. 97 067007 URL 10.1103/PhysRevLett.97.067007

Top papers written in this journal

Journal Article DOI: 10.1088/0266-5611/15/2/022
Optical tomography in medical imaging
Simon R. Arridge1
01 Apr 1999 - Inverse Problems

Abstract:

We present a review of methods for the forward and inverse problems in optical tomography. We limit ourselves to the highly scattering case found in applications in medical imaging, and to the problem of absorption and scattering reconstruction. We discuss the derivation of the diffusion approximation and other simplification... We present a review of methods for the forward and inverse problems in optical tomography. We limit ourselves to the highly scattering case found in applications in medical imaging, and to the problem of absorption and scattering reconstruction. We discuss the derivation of the diffusion approximation and other simplifications of the full transport problem. We develop sensitivity relations in both the continuous and discrete case with special concentration on the use of the finite element method. A classification of algorithms is presented, and some suggestions for open problems to be addressed in future research are made. read more read less

Topics:

Optical tomography (60%)60% related to the paper, Diffuse optical imaging (55%)55% related to the paper, Inverse problem (55%)55% related to the paper, Reconstruction algorithm (54%)54% related to the paper, Medical imaging (53%)53% related to the paper
View PDF
2,609 Citations
open accessOpen access Journal Article DOI: 10.1088/0266-5611/23/3/008
Sparsity and incoherence in compressive sampling
Emmanuel J. Candès1, Justin Romberg2
10 Apr 2007 - Inverse Problems

Abstract:

We consider the problem of reconstructing a sparse signal x 0 2 R n from a limited number of linear measurements. Given m randomly selected samples of Ux 0 , where U is an orthonormal matrix, we show that ‘1 minimization recovers x 0 exactly when the number of measurements exceeds m Const ·µ 2 (U) ·S · logn, where S is the nu... We consider the problem of reconstructing a sparse signal x 0 2 R n from a limited number of linear measurements. Given m randomly selected samples of Ux 0 , where U is an orthonormal matrix, we show that ‘1 minimization recovers x 0 exactly when the number of measurements exceeds m Const ·µ 2 (U) ·S · logn, where S is the number of nonzero components in x 0 , and µ is the largest entry in U properly normalized: µ(U) = p n · maxk,j |Uk,j|. The smaller µ, the fewer samples needed. The result holds for “most” sparse signals x 0 supported on a fixed (but arbitrary) set T. Given T, if the sign of x 0 for each nonzero entry on T and the observed values of Ux 0 are drawn at random, the signal is recovered with overwhelming probability. Moreover, there is a sense in which this is nearly optimal since any method succeeding with the same probability would require just about this many samples. read more read less
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2,187 Citations
Journal Article DOI: 10.1088/0266-5611/14/4/001
Synthetic aperture radar interferometry
Richard Bamler1, Philipp Hartl2
01 Aug 1998 - Inverse Problems

Abstract:

Synthetic aperture radar (SAR) is a coherent active microwave imaging method. In remote sensing it is used for mapping the scattering properties of the Earth's surface in the respective wavelength domain. Many physical and geometric parameters of the imaged scene contribute to the grey value of a SAR image pixel. Scene invers... Synthetic aperture radar (SAR) is a coherent active microwave imaging method. In remote sensing it is used for mapping the scattering properties of the Earth's surface in the respective wavelength domain. Many physical and geometric parameters of the imaged scene contribute to the grey value of a SAR image pixel. Scene inversion suffers from this high ambiguity and requires SAR data taken at different wavelength, polarization, time, incidence angle, etc. Interferometric SAR (InSAR) exploits the phase differences of at least two complex-valued SAR images acquired from different orbit positions and/or at different times. The information derived from these interferometric data sets can be used to measure several geophysical quantities, such as topography, deformations (volcanoes, earthquakes, ice fields), glacier flows, ocean currents, vegetation properties, etc. This paper reviews the technology and the signal theoretical aspects of InSAR. Emphasis is given to mathematical imaging models and the statistical properties of the involved quantities. Coherence is shown to be a useful concept for system description and for interferogram quality assessment. As a key step in InSAR signal processing two-dimensional phase unwrapping is discussed in detail. Several interferometric configurations are described and illustrated by real-world examples. A compilation of past, current and future InSAR systems concludes the paper. read more read less

Topics:

Synthetic aperture radar (64%)64% related to the paper, Interferometric synthetic aperture radar (61%)61% related to the paper, Inverse synthetic aperture radar (60%)60% related to the paper, Microwave imaging (57%)57% related to the paper, Interferometry (51%)51% related to the paper
View PDF
1,563 Citations
Journal Article DOI: 10.1088/0266-5611/20/1/006
A unified treatment of some iterative algorithms in signal processing and image reconstruction
Charles L. Byrne1
01 Feb 2004 - Inverse Problems

Abstract:

Let T be a (possibly nonlinear) continuous operator on Hilbert space . If, for some starting vector x, the orbit sequence {Tkx,k = 0,1,...} converges, then the limit z is a fixed point of T; that is, Tz = z. An operator N on a Hilbert space is nonexpansive?(ne) if, for each x and y in , Even when N has fixed points the orbit ... Let T be a (possibly nonlinear) continuous operator on Hilbert space . If, for some starting vector x, the orbit sequence {Tkx,k = 0,1,...} converges, then the limit z is a fixed point of T; that is, Tz = z. An operator N on a Hilbert space is nonexpansive?(ne) if, for each x and y in , Even when N has fixed points the orbit sequence {Nkx} need not converge; consider the example N = ?I, where I denotes the identity operator. However, for any the iterative procedure defined by converges (weakly) to a fixed point of N whenever such points exist. This is the Krasnoselskii?Mann (KM) approach to finding fixed points of ne operators. A wide variety of iterative procedures used in signal processing and image reconstruction and elsewhere are special cases of the KM iterative procedure, for particular choices of the ne operator N. These include the Gerchberg?Papoulis method for bandlimited extrapolation, the SART algorithm of Anderson and Kak, the Landweber and projected Landweber algorithms, simultaneous and sequential methods for solving the convex feasibility problem, the ART and Cimmino methods for solving linear systems of equations, the CQ algorithm for solving the split feasibility problem and Dolidze's procedure for the variational inequality problem for monotone operators. read more read less

Topics:

Fixed point (57%)57% related to the paper, Hilbert space (55%)55% related to the paper, Operator (computer programming) (54%)54% related to the paper, Iterative reconstruction (53%)53% related to the paper, Variational inequality (53%)53% related to the paper
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1,100 Citations
Journal Article DOI: 10.1088/0266-5611/18/2/310
Iterative oblique projection onto convex sets and the split feasibility problem
Charles L. Byrne1
07 Mar 2002 - Inverse Problems

Abstract:

Let C and Q be nonempty closed convex sets in R N and R M , respectively, and A an M by N real matrix. The split feasibility problem (SFP) is to find x ∈ C with Ax ∈ Q ,i f suchx exist. An iterative method for solving the SFP, called the CQ algorithm, has the following iterative step: x k+1 = PC (x k + γ A T (PQ − I )Ax k ), ... Let C and Q be nonempty closed convex sets in R N and R M , respectively, and A an M by N real matrix. The split feasibility problem (SFP) is to find x ∈ C with Ax ∈ Q ,i f suchx exist. An iterative method for solving the SFP, called the CQ algorithm, has the following iterative step: x k+1 = PC (x k + γ A T (PQ − I )Ax k ), where γ ∈ (0, 2/L) with L the largest eigenvalue of the matrix A T A and PC and PQ denote the orthogonal projections onto C and Q, respectively; that is, PC x minimizesc − x� ,o ver allc ∈ C.T heCQ algorithm converges to a solution of the SFP, or, more generally, to a minimizer ofPQ Ac − Acover c in C, whenever such exist. The CQ algorithm involves only the orthogonal projections onto C and Q, which we shall assume are easily calculated, and involves no matrix inverses. If A is normalized so that each row has length one, then L does not exceed the maximum number of nonzero entries in any column of A, which provides a helpful estimate of L for sparse matrices. Particular cases of the CQ algorithm are the Landweber and projected Landweber methods for obtaining exact or approximate solutions of the linear equations Ax = b ;t healgebraic reconstruction technique of Gordon, Bender and Herman is a particular case of a block-iterative version of the CQ algorithm. One application of the CQ algorithm that is the subject of ongoing work is dynamic emission tomographic image reconstruction, in which the vector x is the concatenation of several images corresponding to successive discrete times. The matrix A and the set Q can then be selected to impose constraints on the behaviour over time of the intensities at fixed voxels, as well as to require consistency (or near consistency) with measured data. read more read less

Topics:

Eigenvalues and eigenvectors (52%)52% related to the paper, Matrix (mathematics) (52%)52% related to the paper, Iterative method (50%)50% related to the paper
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884 Citations
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Frequently asked questions

1. Can I write Inverse Problems in LaTeX?

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

2. Do you follow the Inverse Problems guidelines?

Yes, the template is compliant with the Inverse Problems 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 Inverse Problems?

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 Inverse Problems citation style.

4. Can I use the Inverse Problems 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 Inverse Problems.

5. Can I use a manuscript in Inverse Problems 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 Inverse Problems that you can download at the end.

6. How long does it usually take you to format my papers in Inverse Problems?

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

7. Where can I find the template for the Inverse Problems?

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 Inverse Problems'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 Inverse Problems'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. Inverse Problems an online tool or is there a desktop version?

SciSpace's Inverse Problems 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 Inverse Problems?

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 Inverse Problems?”

11. What is the output that I would get after using Inverse Problems?

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

12. Is Inverse Problems'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 Inverse Problems?

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 Inverse Problems. 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 Inverse Problems?

The 5 most common citation types in order of usage for Inverse Problems 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 Inverse Problems?

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 Inverse Problems's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download Inverse Problems 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 Inverse Problems Endnote style according to Elsevier guidelines.

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