Superiorization and Perturbation Resilience of Algorithms: A
Bibliography
compiled and
continuously updated by Yair Censor
This
page (at: http://math.haifa.ac.il/yair/bib-superiorization-censor.html) is a, chronologically ordered,
bibliography of scientific publications
on the superiorization methodology and perturbation
resilience of algorithms, compiled and
continuously updated by Yair Censor.
If you know of a
related work in any form (preprint, reprint, journal publication, conference
report, abstract or poster, book chapter, thesis, etc.) that should be included
here kindly write to me on: yair@math.haifa.ac.il with full bibliographic
details, a DOI if available, and a PDF copy of the work if possible.
Copyright notice: Downloads are supplied for personal
academic use only. A download is considered equivalent to a pre-print or
re-print request. Use is granted consistent with fair-use of a pre-print or
re-print. By downloading any of the following materials you are agreeing to
these terms.
Back to Yair Censor's
homepage.
Trailer:
We
replace the text that appeared in this trailer in the previous versions of
the page with a quotation of three paragraphs from the preface to the special issue: Y. Censor,
G.T. Herman and M. Jiang (Guest Editors), "Superiorization: Theory and
Applications", Special Issue of the journal Inverse Problems, Volume 33,
Number 4, April 2017 [50] (all references refer to the bibliography below), followed by some additional notes.
"The
superiorization
methodology is used for improving the efficacy of iterative algorithms whose convergence is resilient to
certain kinds of perturbations. Such perturbations are designed
to 'force' the perturbed algorithm to produce more useful results
for the intended application than the ones that are produced by
the original iterative algorithm. The perturbed algorithm is called
the 'superiorized version' of the original unperturbed algorithm.
If the original algorithm is computationally efficient and useful
in terms of the application at hand and if the perturbations are
simple and not expensive to calculate, then the advantage of this
method is that, for essentially the computational cost of the original
algorithm, we are able to get something more desirable by
steering its iterates according to the designed perturbations.
This is
a very general principle that has
been used successfully in some important practical applications, especially for
inverse problems such as image reconstruction from projections,
intensity-modulated radiation therapy and nondestructive testing, and
awaits to be implemented and tested in additional fields.
An
important case is when the original
algorithm is 'feasibility-seeking' (in the sense that it strives to find some point that is
compatible with a family of constraints) and the perturbations
that are introduced into the original iterative algorithm aim at
reducing (not necessarily minimizing) a given merit function. In
this case superiorization has a unique place in optimization theory
and practice.
Many
constrained optimization methods are
based on methods for unconstrained optimization that are adapted to deal with
constraints. Such is, for example, the class of projected gradient
methods wherein the unconstrained minimization inner step
'leads' the process and a projection onto the whole constraint set
(the feasible set) is performed after each
minimization step in
order to regain feasibility. This projection onto the constraints
set is in itself a non-trivial optimization problem and the need
to solve it in every iteration hinders projected gradient methods
and limits their efficiency only to feasible sets that are 'simple
to project onto.' Barrier or penalty methods likewise are based
on unconstrained optimization combined with various 'add-on's
that guarantee that the constraints are preserved. Regularization
methods embed the constraints into a 'regularized' objecive
function and proceed with unconstrained solution
methods for
the new regularized objective function.
In
contrast to these approaches, the
superiorization methodology can be viewed as an antipodal way of thinking. Instead of
adapting unconstrained minimization algorithms to handling constraints,
it adapts feasibility-seeking algorithms to reduce merit
function values. This is done while retaining the feasibility-seeking
nature of the algorithm and without paying a high computational
price. Furthermore, general-purpose approaches have been developed
for automatically superiorizing iterative algorithms for large
classes of constraints sets and merit functions; these provide algorithms for many application tasks." (end of
qoute.)
To a
novice on the superiorization
methodology and perturbation resilience of algorithms we recommend to read first the recent reviews in [16,
25, 39]. For a recent description of previous work that is related to superiorization
but is not included here, such as the works of Sidky and Pan, e.g.,
[6], we direct the reader to [24, section 3]. The SNARK14 software package
[42], with its in-built capability to superiorize iterative algorithms to improve
their performance, can be helpful to practitioners. Naturally there is variability
among the bibliography items below in their degree of relevance to
the superiorization methodology and perturbation resilience of algorithms.
In some, such as in, e.g., [23] below, superiorization does not appear in the
title, abstract or introduction but only inside the work, e.g., [23, Subsection 6.2.1: Optimization vs.
Superiorization].
A word
about the history. The terms and
notions "superiorization" and
"perturbation resilience" first appeared in the 2009 paper of Davidi, Herman
and Censor [7] which followed its
2007 forerunner by Butnariu, Davidi, Herman and Kazantsev [3]. The ideas have some of their roots in the 2006 and
2008 papers of Butnariu, Reich and Zaslavski [2, 4]. All these culminated in
Ran Davidi's 2010 Ph.D. dissertation [13].
The Bibliography: [1]
P.L. Combettes, On the numerical robustness of the parallel projection
method in signal synthesis, IEEE Signal Processing Letters, Vol. 8, pp.
45-47, (2001). DOI:10.1109/97.895371. [Abstract]. [2]
D. Butnariu, S. Reich and A.J. Zaslavski, Convergence to fixed points
of inexact orbits of Bregman-monotone and of nonexpansive operators in Banach
spaces, in: H.F. Nathansky, B.G. de Buen, K. Goebel, W.A. Kirk, and B. Sims (Editors), Fixed
Point Theory and its Applications, (Conference Proceedings, Guanajuato,
Mexico, 2005), Yokahama Publishers, Yokahama, Japan, pp. 11-32, 2006.
http://www.ybook.co.jp/pub/ISBN%20978-4-9465525-0.htm. [Abstract]. [3]
D. Butnariu, R. Davidi, G.T. Herman and I.G. Kazantsev, Stable convergence behavior under summable
perturbations of a class of projection methods for convex feasibility and
optimization problems, IEEE Journal of Selected Topics in Signal Processing,
Vol. 1, pp. 540-547, (2007). DOI:10.1109/JSTSP.2007.910263. [Abstract]. [4]
D. Butnariu, S. Reich and A.J. Zaslavski, Stable convergence theorems
for infinite products and powers of nonexpansive mappings, Numerical
Functional Analysis and Optimization, Vol. 29, pp. 304-323, (2008).
DOI:10.1080/01630560801998161. [Abstract]. [5]
G.T. Herman and R. Davidi, Image reconstruction from a small number of
projections, Inverse Problems, Vol. 24, 045011 (17pp), (2008). DOI:10.1088/0266-5611/24/4/045011. [Abstract]. [6]
E.Y. Sidky and X. Pan, Image reconstruction in circular cone-beam
computed tomography by constrained, total-variation minimization, Physics in Medicine and Biology, Vol. 53,
pp. 4777-4807, (2008). DOI:10.1088/0031-9155/53/17/021. [Abstract]. [7]
R. Davidi, G.T. Herman and Y. Censor,
Perturbation-resilient block-iterative projection methods with application to
image reconstruction from projections, International Transactions in
Operational Research, Vol. 16, pp. 505-524, (2009).
DOI:10.1111/j.1475-3995.2009.00695.x. Final version preprint PDF (624KB) file. E-reprint of
published paper available upon request. [8]
G.T. Herman, Fundamentals of Computerized Tomography: Image
Reconstruction from Projections, Springer-Verlag, London, UK, 2nd Edition, 2009.
DOI:10.1007/978-1-84628-723-7. [9]
S.N. Penfold, Image
Reconstruction and Monte Carlo Simulations in the Development of Proton
Computed Tomography for Applications in Proton Radiation Therapy, Ph.D.
dissertation, Centre
for Medical Radiation Physics, University of Wollongong, 2010.
http://ro.uow.edu.au/cgi/viewcontent.cgi?article=4305&context=theses. [Abstract]. [10]
S.N. Penfold, R.W. Schulte, Y. Censor, V. Bashkirov, S. McAllister,
K.E. Schubert and A.B. Rosenfeld, Block-iterative and string-averaging
projection algorithms in proton computed tomography image reconstruction, in:
Y. Censor, M. Jiang and G. Wang (Editors), Biomedical Mathematics: Promising
Directions in Imaging, Therapy Planning and Inverse Problems, Medical
Physics Publishing, Madison, WI, USA, 2010, pp. 347-367. Final version
preprint PDF (463KB) file.
E-reprint of published paper available upon request.
https://www.medicalphysics.org/SimpleCMS.php?content=reviewlist.php&isbn=9781930524484. [11]
Y. Censor, R. Davidi and G.T. Herman, Perturbation resilience and
superiorization of iterative algorithms, Inverse Problems, Vol. 26, (2010)
065008 (12pp). DOI:10.1088/0266-5611/26/6/065008. Final version preprint PDF (394KB) file.
E-reprint of published paper available upon request. [12]
S.N. Penfold, R.W. Schulte, Y. Censor and A.B. Rosenfeld, Total
variation superiorization schemes in proton computed tomography image
reconstruction, Medical Physics, Vol. 37, pp. 5887-5895, (2010).
DOI:10.1118/1.3504603. Final version preprint PDF (339KB) file. E-reprint of
published paper available upon request. [13]
R. Davidi, Algorithms for Superiorization and their Applications to
Image Reconstruction, Ph.D. dissertation, Department of Computer Science,
The City University of New York, NY, USA, 2010. http://gradworks.umi.com/34/26/3426727.html. [Abstract].
[14]
E. Garduño, G.T. Herman and R. Davidi, Reconstruction from a few
projections by ℓ1-minimization of the Haar transform, Inverse
Problems, Vol. 27, 055006, (2011). DOI:10.1088/0266-5611/27/5/055006. [Abstract].
[15]
Y. Censor, W. Chen, P.L. Combettes, R. Davidi and G.T. Herman, On the
effectiveness of projection methods for convex feasibility problems with linear
inequality constraints, Computational Optimization and Applications,
Vol. 51, pp. 1065-1088, (2012). DOI:10.1007/s10589-011-9401-7. Final
version preprint PDF (721KB) file.
E-reprint of published paper available upon request. A related (unpublished)
Technical Report: W. Chen, Data sets of very large linear feasibility problems
solved by projection methods, March 2, 2011, can be viewed here: PDF (66KB). [16]
G.T. Herman, E. Garduño, R. Davidi and Y. Censor, Superiorization: An
optimization heuristic for medical physics, Medical Physics, Vol. 39,
pp. 5532-5546, (2012). DOI:10.1118/1.4745566. Final version preprint PDF (1,193KB) file. E-reprint of
published paper available upon request. [17]
R. Davidi, R.W. Schulte, Y. Censor and L. Xing, Fast superiorization
using a dual perturbation scheme for proton computed tomography, Transactions
of the American Nuclear Society, Vol. 106, pp. 73-76, (2012). Final
version preprint PDF (591KB) file.
E-reprint of published paper available upon request. [18]
T. Nikazad, R. Davidi and G.T. Herman, Accelerated
perturbation-resilient block-iterative projection methods with application to
image reconstruction, Inverse Problems, Vol. 28, 035005 (19pp), (2012).
DOI:10.1088/0266-5611/28/3/035005. [Abstract]. [19]
D. Steinberg, V. Bashkirov, V. Feng, R.F. Hurley, R.P. Johnson, S. Macafee, T. Plautz,
H.F.-W. Sadrozinski, R. Schulte and A. Zatserklyaniy, Monte Carlo simulations for the
development a clinical proton CT scanner, Nuclear Science Symposium and Medical Imaging Conference
(NSS/MIC), 2012 IEEE, pp. 1311-1315. Oct. 27-Nov. 3, 2012, Anaheim, CA, USA.
DOI:10.1109/NSSMIC.2012.6551320. [Abstract]. [20]
W. Jin, Y. Censor and M. Jiang, A heuristic superiorization-like
approach to bioluminescence, International Federation for Medical and
Biological Engineering (IFMBE) Proceedings, Vol. 39, pp. 1026-1029, (2013).
DOI:10.1007/978-3-642-29305-4_269. Final version preprint PDF (297KB) file. E-reprint of published
paper available upon request. [21]
Y. Censor and A.J. Zaslavski, Convergence and perturbation resilience
of dynamic string-averaging projection methods, Computational Optimization
and Applications, Vol. 54, pp. 65-76, (2013).
DOI:10.1007/s10589-012-9491-x. Final version preprint PDF (209KB) file. E-reprint of published
paper available upon request. [22]
S.-S. Luo, Reconstruction Algorithms for Single-photon Emission Computed Tomography, Ph.D. dissertation, Computational Mathematics, Peking University (PKU), Beijing, P.R. China,
2013.
http://www.dissertationtopic.net/doc/2220625. [Abstract]. [23]
X. Zhang, Prior-Knowledge-Based Optimization Approaches for CT Metal Artifact Reduction, Ph.D. dissertation, Dept. of Electrical Engineering, Stanford University, Stanford, CA, USA,
2013.
http://purl.stanford.edu/ws303zb5770. [Abstract]. [24]
Y. Censor, R. Davidi, G.T. Herman, R.W. Schulte and L. Tetruashvili, Projected subgradient
minimization versus superiorization, Journal of Optimization Theory and
Applications, Vol. 160, pp. 730-747, (2014). DOI:10.1007/s10957-013-0408-3.
Final version preprint PDF
(480KB) file. E-reprint of published paper available upon request. [25]
G.T. Herman, Superiorization for image analysis, in: Combinatorial
Image Analysis, Lecture Notes in Computer Science Vol. 8466, Springer, 2014,
pp. 1-7. DOI:10.1007/978-3-319-07148-0_1. [Abstract]. [26]
S. Luo and T. Zhou, Superiorization of EM
algorithm and its application in single-photon emission computed tomography
(SPECT), Inverse Problems and Imaging, Vol. 8, pp. 223-246, (2014).
DOI:10.3934/ipi.2014.8.223. [Abstract]. [27]
M.J. Schrapp and G.T. Herman, Data fusion in
X-ray computed tomography using a superiorization approach, Review of
Scientific Instruments, Vol. 85, 053701 (9pp), (2014).
DOI:10.1063/1.4872378. [Abstract]. [28]
M. Schrapp, M. Goldammer, K. Schörner and J. Stephan,
Improvement of image quality in computed tomography via data fusion, Proceedings of the
5th International Conference on Industrial Computed
Tomography (iCT), pp. 283-289, February 2014, the University of Applied Sciences, Wels, Upper Austria.
http://www.ndt.net/article/ctc2014/papers/283.pdf. [Abstract]. [29]
E. Garduño and G.T. Herman, Superiorization
of the ML-EM algorithm, IEEE Transactions on Nuclear Science, Vol. 61,
pp. 162-172, (2014). DOI:10.1109/TNS.2013.2283529. [Abstract]. [30]
O. Langthaler, Incorporation of the Superiorization
Methodology into Biomedical Imaging Software, Marshall Plan Scholarship Report, Salzburg University of Applied
Sciences, Salzburg, Austria, and The Graduate Center of the City University of
New York, NY, USA, September
2014, (76 pages). https://static1.squarespace.com/
static/559921a3e4b02c1d7480f8f4/t/585c49d6725e25be085071c7/1482443225511/Langthaler.pdf.
[Abstract]. [31]
B. Prommegger, Verification and Evaluation of Superiorized Algorithms Used in Biomedical Imaging:
Comparison of Iterative Algorithms With and Without Superiorization for Image
Reconstruction from Projections, Marshall Plan Scholarship Report, Salzburg University
of Applied Sciences, Salzburg, Austria, and The Graduate Center of the City University of
New York, NY, USA, October 2014,
(84 pages). https://static1.squarespace.com/
static/559921a3e4b02c1d7480f8f4/t/585c49bc8419c2c4f892861b/1482443201138/Prommegger.pdf.
[Abstract]. [32]
D.C.
Hansen, Improving Ion Computed
Tomography, Ph.D. dissertation,
Aarhus University, Experimental Clinical Oncology, Aarhus, Denmark, 2014.
http://pure.au.dk//portal/files/83515131/dissertation.pdf. [Abstract]. [33]
J. Lee, C. Kim, B. Min, J. Kwak, S. Park,
S-B. Lee, S. Park and S. Cho, Sparse-view proton computed tomography using
modulated proton beams, Medical Physics, Vol. 42, pp. 1129-1137, (2015).
DOI:10.1118/1.4906133. [Abstract]. [34]
T. Nikazad and M. Abbasi, Perturbation-resilient iterative methods with
an infinite pool of mappings, SIAM Journal on Numerical Analysis, Vol. 53, pp. 390-404, (2015).
DOI:10.1137/14095724X. [Abstract]. [35]
F. Arroyo, E. Arroyo, X. Li and J. Zhu, The convergence of the block cyclic projection with an overrelaxation
parameter for compressed sensing based tomography, Journal of Computational and Applied Mathematics, Vol. 280, pp.
59-67, (2015). DOI:10.1016/j.cam.2014.11.036. [Abstract]. [36]
R. Davidi, Y. Censor, R.W. Schulte, S. Geneser
and L. Xing, Feasibility-seeking and superiorization algorithms applied to
inverse treatment planning in radiation therapy, Contemporary Mathematics, Vol. 636, pp. 83-92, (2015).
DOI:10.1090/conm/636/12729. Final version preprint PDF (454KB) file.
E-reprint of published paper available upon request. [37]
Y. Censor and D. Reem, Zero-convex functions,
perturbation resilience, and subgradient projections
for feasibility-seeking methods, Mathematical Programming, Series A, Vol. 152, pp. 339-380, (2015).
DOI:10.1007/s10107-014-0788-7. Final version preprint PDF (589KB) file. E-reprint of
published paper available upon request. [38]
Y. Censor and A.J. Zaslavski, Strict Fejér monotonicity by superiorization of feasibility-seeking projection methods, Journal
of Optimization Theory and Applications, Vol. 165, pp. 172-187, (2015).
DOI:10.1007/s10957-014-0591-x. Final version preprint PDF (216KB) file.
E-reprint of published paper available upon request. [39]
Y. Censor, Weak and strong superiorization: Between feasibility-seeking
and minimization, Analele
Stiintifice ale Universitatii
Ovidius Constanta-Seria Matematica, Vol. 23, pp. 41-54, (2015). DOI:10.1515/auom-2015-0046. Final
version preprint PDF (213KB)
file. E-reprint of published paper available upon
request. [40]
H.H. Bauschke and V.R. Koch, Projection
methods: Swiss army knives for solving feasibility and best approximation
problems with half-spaces, Contemporary Mathematics, Vol. 636, pp. 1-40,
(2015). DOI:10.1090/conm/636/12726. https://people.ok.ubc.ca/bauschke/Research/c16.pdf. [Abstract].
[41]
M.J.
Schrapp, Multi Modal Data Fusion in Industrial
X-ray Computed Tomography, Ph.D. dissertation,
Fakultät für Physik der Technischen Universität München, Munich, Germany, 2015. [Abstract]. [42]
SNARK14, A programming system for the reconstruction of 2D images from 1D projections designed to help researchers in
developing and evaluating reconstruction algorithms. In particular, SNARK14 can be used for automatic superiorization of
any iterative reconstruction algorithm. Released: 2015.
Go to SNARK14.
[43]
W. Jin, Y. Censor and M. Jiang, Bounded perturbation resilience of projected scaled gradient methods,
Computational Optimization and Applications, Vol. 63, pp. 365-392, (2016).
DOI:10.1007/s10589-015-9777-x.
Final version preprint PDF (357KB) file. E-reprint of published paper
available upon request.
[48]
C. Havas, Revised Implementation and Empirical Study of Maximum
Likelihood Expectation Maximization Algorithms with and
without Superiorization in Image Reconstruction, Marshall Plan Scholarship Report, Salzburg University
of Applied Sciences, Salzburg, Austria, and The Graduate Center of the City University of
New York, NY, USA, October 2016,
(49 pages).
https://static1.squarespace.com/static/559921a3e4b02c1d7480f8f4/t/596c97aad1758e1c6808c0fa/1500288944245/
Havas+Clemens_615.pdf. [Front Matter]. [50]
Y. Censor, G.T. Herman and M. Jiang, Guest Editors,
Superiorization: Theory and Applications, Special Issue of the journal Inverse
Problems, Volume 33, Number 4, April 2017. Read the
Preface
to the special issue on the journal's website, or here Preface. Read the titles and
abstracts of all 14 papers included in the special issue on the journal's website at: titles and abstracts or
consult items [51]-[64] below.
[112]
Esther Bonacker, Aviv Gibali and Karl-Heinz Küfer, Nesterov perturbations and projection
methods
applied to IMRT, Journal of Nonlinear and Variational Analysis, Volume 4, Issue 1,
Pages 63-86, 2020. Available online at [http://jnva.biemdas.com].
https://doi.org/10.23952/jnva.4.2020.1.06.
[113]
A. Gibali, G.T. Herman and C. Schnörr, Guest Editors,
Superiorization versus Constrained Optimization: Analysis and Applications, Special Issue of
Journal of Applied and Numerical Optimization (JANO), Volume 2, Number 1, April
2020. Read the special issue on the journal's website at: http:
//jano.biemdas.com/archives/category/volume-2-issue-1.
[114]
A. Gibali, G.T. Herman and C. Schnörr, Editorial: A special issue focused on
superiorization versus constrained optimization: analysis and applications, Journal of Applied and
Numerical Optimization (JANO), Vol. 2, pp. 1-2, 2020. Read it on the journal's
website at:
http://jano.biemdas.com/issues/JANO2020-1-1-1.pdf.
[115]
Mokhtar Abbasi and Touraj Nikazad, Superiorization of block accelerated cyclic subgradient
methods, Journal of Applied and
Numerical Optimization (JANO), Vol. 2, pp. 3-13, 2020. Read it on the journal's
website at:
http://jano.biemdas.com/issues/JANO2020-1-2.pdf.
[116]
Y. Censor, S. Petra and C. Schnörr, Superiorization vs. accelerated convex
optimization: The superiorized / regularized least-squares case, Journal of Applied and
Numerical Optimization (JANO), Vol. 2, pp. 15-62, 2020. Read it on the journal's
website at:
http://jano.biemdas.com/issues/JANO2020-1-3.pdf.
[117]
A. Gibali and M. Haltmeier, Superiorized regularization of inverse problems, Journal of
Applied and
Numerical Optimization (JANO), Vol. 2, pp. 63-70, 2020. Read it on the journal's
website at:
http://jano.biemdas.com/issues/JANO2020-1-4.pdf.
[118]
Gabor T. Herman, Problem structures in the theory and practice of superiorization, Journal of
Applied and
Numerical Optimization (JANO), Vol. 2, pp. 71-76, 2020. Read it on the journal's
website at:
http://jano.biemdas.com/issues/JANO2020-1-5.pdf.
[119]
T. Humphries, M. Loreto, B. Halter, W. O'Keeffe and L. Ramirez, Comparison of regularized and
superiorized methods for tomographic image reconstruction, Journal of
Applied and
Numerical Optimization (JANO), Vol. 2, pp. 77-99, 2020. Read it on the journal's
website at:
http://jano.biemdas.com/issues/JANO2020-1-6.pdf.
[120]
S. Reich and A.J. Zaslavski, Inexact orbits of set-valued nonexpansive mappings with summable
errors, Journal of
Applied and
Numerical Optimization (JANO), Vol. 2, pp. 101-107, 2020. Read it on the journal's
website at:
http://jano.biemdas.com/issues/JANO2020-1-7.pdf.
[121]
A.J. Zaslavski, Three extensions of Butnariu-Reich-Zaslavski theorem for inexact infinite
products of nonexpansive mappings, Journal of
Applied and
Numerical Optimization (JANO), Vol. 2, pp. 109-120, 2020. Read it on the journal's
website at:
http://jano.biemdas.com/issues/JANO2020-1-8.pdf.
[124]
Nikolai Janakiev, Superiorized Algorithms for Medical Image Processing:
Comparison of Shearlet-based Secondary Optimization
Criteria, Marshall Plan Scholarship Report, Salzburg University
of Applied Sciences, Salzburg, Austria, and The Graduate Center of the City University of
New York, NY, USA, September 2016,
(63 pages). https://static1.squarespace.com/static/
559921a3e4b02c1d7480f8f4/t/5d4d476d5cae4e000125087e/1565345658569/Janakiev+Nikolai_816.PDF.
[129]
Nuttapol Pakkaranang, Poom Kumam, Yusuf I. Suleiman and Bashir Ali, Bounded perturbation
resilience of viscosity proximal algorithm for solving split variational inclusion problems with
applications to compressed sensing and image recovery, Mathematical Methods in the Applied
Sciences, pp. 1-23, 2020. The journal's
website is at:
https://onlinelibrary.wiley.com/doi/10.1002/mma.7023.
[130]
Peichao Duan and Xubang Zheng, Bounded perturbation resilience of generalized viscosity
iterative algorithms for split variational inclusion problems, Applied Set-Valued Analysis and
Optimization, Vol. 2, pp. 49-61, 2020. DOI:10.23952/asvao.2.2020.1.04. Open Access on the
journal's website at: http://asvao.biemdas.com/archives/1051.
[131]
Jochen Fink, Renato L.G. Cavalcante and Slawomir Stanczak, Multi-group multicast beamforming by
superiorized projections onto convex sets, IEEE Transactions on Signal Processing , Vol. 69, pp.
5708-5722, 2021. https://ieeexplore.ieee.org/document/9557796.
IEEEXplore.
[132]
Idan Steinberg, Jeesu Kim, Martin K. Schneider, Dongwoon Hyun, Aimen Zlitni, Sarah M. Hooper,
Tal Klap, Geoffrey A. Sonn, Jeremy J. Dahl, Chulhong Kim, Sanjiv Sam Gambhir, Superiorized Photo-Acoustic
Non-NEgative Reconstruction (SPANNER) for Clinical Photoacoustic Imaging, IEEE Transactions on Medical
Imaging, 2021. https://ieeexplore.ieee.org/document/9383259.
[Open Access from the publisher at
IEEEXplore]. DOI:10.1109/TMI.2021.3068181.
[133]
Sebastian Meyer, Marco Pinto, Katia Parodi and Chiara Gianoli, The impact of path estimates in
iterative ion CT reconstructions for clinical-like cases, Physics in Medicine and Biology,
accepted for publication, 2021.
https://doi.org/10.1088/1361-6560/abf1ff.
[134]
Weizhe Han, Qianlong Wang and Weiwei Cai, Computed tomography imaging spectrometry based
on superiorization and guided image filtering, Optics Letters,
Vol. 46, pp. 2208-2211, 2021, https://doi.org/10.1364/OL.418355.
[The Optical Society (OSA)].
[135]
Howard Heaton, Samy Wu Fung, Aviv Gibali and Wotao Yin, Feasibility-based Fixed Point Networks,
2021. https://arxiv.org/abs/2104.14090. [Preprint from arXiv].
[137]
Chongyuan Shui, Yihong Wang, Weiwei Cai, and Bin Zhou, Linear multispectral absorption tomography
based on regularized iterative methods, Optics Express,
Vol. 29, pp. 20889-20912, 2021, https://doi.org/10.1364/OE.421817.
[Open Access from The
Optical Society (OSA)].
[141]
Yingying Li and Yaxuan Zhang, Bounded perturbation resilience of two modified relaxed CQ
algorithms for the multiple-sets split feasibility problem, Axioms, 10, 197, 2021 (22 pages).
https://doi.org/10.3390/axioms10030197. Open Access on the
journal's website at: https://www.mdpi.com/journal/axioms.
[148]
Ruiwen Xing, Thomas Humphries and Dong Si, Self-Attention Generative Adversarial Network for Iterative Reconstruction of CT Images,
2022. https://arxiv.org/abs/2112.12810. [Preprint from arXiv].
[149]
Qiao-Li Dong, Yeol Je Cho, Songnian He, Panos M. Pardalos and Themistocles M. Rassias, The inertial Krasnosel'skiĭ–Mann iteration,
In: The Krasnosel'skiĭ–Mann Iterative Method: Recent Progress and Applications, SpringerBriefs in
Optimization, Springer, Cham, Switzerland, Chapter 5 (pp.59-73),
2022. https://link.springer.com/chapter/10.1007/978-3-030-91654-1_5.
Available on SpringerLink
.
[150]
Jochen Fink, Renato L.G. Cavalcante and Slawomir Stanczak, Superiorized Adaptive Projected Subgradient
Method with Application to MIMO Detection,
2022. https://arxiv.org/abs/2203.01116. [Preprint from arXiv].
[151]
Yair Censor, Daniel Reem and Maroun Zaknoon, A generalized block-iterative projection method for the common fixed
point problem induced by cutters, Journal of Global Optimization, Vol. 84, pp. 967-987, (2022).
DOI:https://link.springer.com/article/10.1007/s10898-022-01175-7.
[Springer link: Online first]. Reprint on: Springer Nature SharedIt.
[163]
Müzeyyen Ertürk and Ahmet Salkim, Superiorization and bounded perturbation resilience of a gradient projection algorithm solving the convex minimization problem, Optimization Letters, (2023).
DOI:https://doi.org/10.1007/s11590-022-01961-y.
[Springer link: Online first]. Reprint on: Springer Nature SharedIt.
[164]
Yiji Wang, Cheng Zou, Dingzhu Wen and Yuanming Shi, Federated Learning over LEO Satellite, 2022 IEEE Globecom Workshops (GC Wkshps): Edge Learning over 5G Mobile Networks and Beyond, Rio de Janeiro, Brazil, (2022), pp. 1652-1657. DOI:10.1109/GCWkshps56602.2022.10008719. Reprint on: https://ieeexplore.ieee.org/abstract/document/10008719.
[165]
Alexander J. Zaslavski, Two Convergence Results for Inexact Infinite Products of Non-Expansive Mappings, Axioms, 12, 88 (2023), (9pp).
DOI:https://doi.org/10.3390/axioms12010088.
Open access at MDPI.com.
[167]
Adeolu Taiwo and Simeon Reich, Bounded perturbation resilience of a regularized
forward-reflected-backward splitting method for solving
variational inclusion problems with applications, Optimization, (2023).
DOI: 10.1080/02331934.2023.2187664.
Taylor and Francis Online. https://doi.org/10.1080/02331934.2023.2187664.
[168]
Shan Yu, Yair Censor, Ming Jiang and Guojie Luo, Per-RMAP: Feasibility-Seeking and Superiorization Methods
for Floorplanning with I/O Assignment, (2023). Accepted for presentation at the International Symposium of EDA (Electronics Design Automation) ISEDA-2023, Nanjing, China, May 8-11, 2023.
Available on arXiv at: https://arxiv.org/abs/2304.06698,
or here: PDF (375KB).
[170]
Simeon Reich and Alexander J. Zaslavski, Convergence Results for Inexact Iterates of Uniformly Locally Nonexpansive Mappings,
Symmetry, (2023), 15(5), 1084; Free access on
MDPI Online. https://doi.org/10.3390/sym15051084.
[171]
Anitha Gopalan, O. Vignesh, R. Anusuya, K.P. Senthilkumar, V.S. Nishok, T. Helan Vidhya and Florin Wilfred,
Reconstructing the Photoacoustic Image with High Quality using the Deep Neural Network Model, Contrast Media & Molecular Imaging,
Vol. 2023, Article ID 1172473, 13 pages, (2023).
Free access on
Hindawi Online. https://doi.org/10.1155/2023/1172473.
[176]
Mokhtar Abbasi, Mahdi Ahmadinia and Ali Ahmadinia,
A new step size selection strategy for the superiorization methodology using subgradient vectors and its application for solving convex constrained optimization problems, IMA Journal of Numerical Analysis, (2023), drad070.
Get it from
Oxford Academic. https://doi.org/10.1093/imanum/drad070.
[180]
Alexander J. Zaslavski, Convergence of inexact orbits of nonexpansive mappings in complete metric spaces, Communications in Optimization Theory,
Vol. 2024 (2024), Article ID 4, pp. 1-10.
https://cot.mathres.org/issues/COT20244.pdf.
[184]
Shousheng Luo, Zhiting Liu, Yaofei Lu and Xue-Cheng Tai,
Superiorized iteration algorithm for CT image simultaneous reconstruction and segmentation, Inverse Problems and Imaging,
DOI:10.3934/ipi.2024024, (2024).
Available at
American Institute of Mathematical Sciences. https://www.aimsciences.org//article/doi/10.3934/ipi.2024024.
[185]
Shan Yu, Yair Censor and Guojie Luo,
Floorplanning with I/O assignment via feasibility-seeking and superiorization methods, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems,
DOI:10.1109/TCAD.2024.3408106, (2024).
Early Access at: IEEEXplore. https://ieeexplore.ieee.org/abstract/document/10543062.
[187]
Touraj Nikazad, Perturbed conjugate gradient method: controlling semi-convergence, enforcing non-negativity, and accelerating iterations in tomographic imaging,
Physica Scripta, Vol. 99, 075034, (2024).
DOI:10.1088/1402-4896/ad5651. Available at: IOPscience. https://iopscience.iop.org/article/10.1088/1402-4896/ad5651/meta.
[188]
Jan Schröder, Yair Censor, Philipp Süss and Karl-Heinz Küfer, Immunity to increasing condition
numbers of linear superiorization versus linear programming,
Preprint.
Available on arXiv at: https://arxiv.org/abs/2407.18709,
or here: PDF (723KB).
[189]
Chen JingRuo, Xu ShiJie, Liu HeCong, Huang JianQing, Liu YingZheng and Cai WeiWei, Untrained neural network for linear tomographic absorption spectroscopy,
Science China Technological Sciences, Vol. 67, (2024).
https://doi.org/10.1007/s11431-023-2629-2. Available on: SpringerLink.
[190]
Jon Henshaw, Aviv Gibali and Thomas Humphries, Plug-and-play superiorization.
Available on arXiv at: https://arxiv.org/abs/2410.23401.
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