Superiorization and Perturbation Resilience of Algorithms: A Continuously Updated Bibliography

This document presents a, (mostly) chronologically ordered, bibliography of scientific publications on the superiorization methodology and perturbation resilience of algorithms which is compiled and continuously updated by us at: this http URL Since the beginings of this topic we try to trace the work that has been published about it since its inception. To the best of our knowledge this bibliography represents all available publications on this topic to date, and while the URL is continuously updated we will revise this document and bring it up to date on arXiv approximately once a year. Abstracts of the cited works, and some links and downloadable files of preprints or reprints are available on the above mentioned Internet page. If you know of a related scientific work in any form 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. The Internet page was initiated on March 7, 2015, and has been last updated on March 12, 2020.

[1]  Jinping Song,et al.  Superiorized iteration based on proximal point method and its application to XCT image reconstruction , 2016, 1608.03931.

[2]  Gabor T. Herman,et al.  Superiorization of the ML-EM Algorithm , 2014, IEEE Transactions on Nuclear Science.

[3]  Alexander J. Zaslavski,et al.  Convergence to approximate solutions and perturbation resilience of iterative algorithms , 2017 .

[4]  D. Butnariu,et al.  Stable Convergence Behavior Under Summable Perturbations of a Class of Projection Methods for Convex Feasibility and Optimization Problems , 2007, IEEE Journal of Selected Topics in Signal Processing.

[5]  Hongjin He,et al.  Perturbation resilience and superiorization methodology of averaged mappings , 2017 .

[6]  Gabor T. Herman,et al.  Fundamentals of Computerized Tomography: Image Reconstruction from Projections , 2009, Advances in Pattern Recognition.

[7]  J. Zhao,et al.  Bounded perturbation resilience of the viscosity algorithm , 2016 .

[8]  E. Sidky,et al.  Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization , 2008, Physics in medicine and biology.

[9]  Ran Davidi,et al.  Perturbation-resilient block-iterative projection methods with application to image reconstruction from projections , 2009, Int. Trans. Oper. Res..

[10]  Simeon Reich,et al.  Convergence properties of dynamic string-averaging projection methods in the presence of perturbations , 2017, Numerical Algorithms.

[11]  E. Nurminski Finite-Value Superiorization for Variational Inequality Problems , 2016, 1611.09697.

[12]  Ran Davidi,et al.  Superiorization: An optimization heuristic for medical physics , 2012, Medical physics.

[13]  Andrzej Cegielski,et al.  Superiorization with level control , 2017 .

[14]  T. Nikazad,et al.  A unified treatment of some perturbed fixed point iterative methods with an infinite pool of operators , 2017 .

[15]  T Humphries,et al.  Superiorized algorithm for reconstruction of CT images from sparse-view and limited-angle polyenergetic data , 2017, Physics in medicine and biology.

[16]  Dan Butnariu,et al.  Stable Convergence Theorems for Infinite Products and Powers of Nonexpansive Mappings , 2008 .

[17]  Yair Censor,et al.  Linear Superiorization for Infeasible Linear Programming , 2016, DOOR.

[18]  Yair Censor,et al.  Strict Fejér Monotonicity by Superiorization of Feasibility-Seeking Projection Methods , 2014, J. Optim. Theory Appl..

[19]  Robert P. Johnson,et al.  Monte Carlo simulations for the development a clinical proton CT scanner , 2012, 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC).

[20]  Aviv Gibali,et al.  Convergence of projection and contraction algorithms with outer perturbations and their applications to sparse signals recovery , 2017, Journal of Fixed Point Theory and Applications.

[21]  Ran Davidi,et al.  Perturbation resilience and superiorization of iterative algorithms , 2010, Inverse problems.

[22]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[23]  Gabor T. Herman,et al.  Total variation superiorized conjugate gradient method for image reconstruction , 2017, 1709.04912.

[24]  Touraj Nikazad,et al.  Perturbation-Resilient Iterative Methods with an Infinite Pool of Mappings , 2015, SIAM J. Numer. Anal..

[25]  Edgar Garduño,et al.  Computerized Tomography with Total Variation and with Shearlets , 2016, ArXiv.

[26]  Heinz H. Bauschke,et al.  Projection Methods: Swiss Army Knives for Solving Feasibility and Best Approximation Problems with Halfspaces , 2013, 1301.4506.

[27]  B. Min,et al.  Sparse-view proton computed tomography using modulated proton beams. , 2015, Medical physics.

[28]  Panos M. Pardalos,et al.  Discrete Optimization and Operations Research , 2016, Lecture Notes in Computer Science.

[29]  Oliver Langthaler,et al.  Incorporation of the Superiorization Methodology into Biomedical Imaging Software , 2014 .

[30]  W. Cong,et al.  Superiorization-based multi-energy CT image reconstruction , 2017, Inverse problems.

[31]  Yair Censor,et al.  Block-Iterative and String-averaging projection algorithms in proton computed tomography image reconstruction , 2010 .

[32]  Ran Davidi,et al.  Projected Subgradient Minimization Versus Superiorization , 2013, Journal of Optimization Theory and Applications.

[33]  Yair Censor,et al.  Weak and Strong Superiorization: Between Feasibility-Seeking and Minimization , 2014, 1410.0130.

[34]  Hong-Kun Xu Bounded perturbation resilience and superiorization techniques for the projected scaled gradient method , 2017 .

[35]  T. Elfving,et al.  Error minimizing relaxation strategies in Landweber and Kaczmarz type iterations , 2017 .

[36]  Yansha Guo,et al.  Perturbation resilience of proximal gradient algorithm for composite objectives , 2017 .

[37]  Jiehua Zhu,et al.  The convergence of the block cyclic projection with an overrelaxation parameter for compressed sensing based tomography , 2015, J. Comput. Appl. Math..

[38]  R. W. Schulte,et al.  Feasibility-Seeking and Superiorization Algorithms Applied to Inverse Treatment Planning in Radiation Therapy , 2014 .

[39]  I. Yamada,et al.  Nonexpansiveness of a linearized augmented Lagrangian operator for hierarchical convex optimization , 2017 .

[40]  Ming Jiang,et al.  Bounded perturbation resilience of projected scaled gradient methods , 2015, Comput. Optim. Appl..

[41]  Gabor T Herman,et al.  Data fusion in X-ray computed tomography using a superiorization approach. , 2014, The Review of scientific instruments.

[42]  K. Küfer,et al.  Speedup of lexicographic optimization by superiorization and its applications to cancer radiotherapy treatment , 2016, 1610.02894.

[43]  G. Herman,et al.  Algorithms for superiorization and their applications to image reconstruction , 2010 .

[44]  A. Zaslavski Asymptotic behavior of two algorithms for solving common fixed point problems , 2017 .

[45]  G T Herman,et al.  Image reconstruction from a small number of projections , 2008, Inverse problems.

[46]  Yair Censor,et al.  Convergence and perturbation resilience of dynamic string-averaging projection methods , 2012, Computational Optimization and Applications.

[47]  Alvaro R. De Pierro,et al.  A new convergence analysis and perturbation resilience of some accelerated proximal forward–backward algorithms with errors , 2015, ArXiv.

[48]  Yuchao Tang,et al.  Bounded perturbation resilience of extragradient-type methods and their applications , 2017, Journal of Inequalities and Applications.

[50]  A B Rosenfeld,et al.  Total variation superiorization schemes in proton computed tomography image reconstruction. , 2010, Medical physics.

[51]  Simeon Reich,et al.  A modular string averaging procedure for solving the common fixed point problem for quasi-nonexpansive mappings in Hilbert space , 2015, Numerical Algorithms.

[52]  S. Penfold,et al.  Total variation superiorization in dual-energy CT reconstruction for proton therapy treatment planning , 2017 .

[53]  G T Herman,et al.  Reconstruction from a few projections by ℓ1-minimization of the Haar transform , 2011, Inverse problems.

[54]  Yair Censor Can Linear Superiorization Be Useful for Linear Optimization Problems? , 2017, Inverse problems.

[55]  Yair Censor,et al.  Zero-convex functions, perturbation resilience, and subgradient projections for feasibility-seeking methods , 2014, Math. Program..

[56]  S. Penfold Image reconstruction and Monte Carlo simulations in the development of proton computed tomography for applications in proton radiation therapy , 2010 .

[57]  K. Schörner,et al.  Improvement of image quality in computed tomography via data fusion , 2014 .

[58]  Eduardo X. Miqueles,et al.  Superiorization of incremental optimization algorithms for statistical tomographic image reconstruction , 2016, 1608.04952.

[59]  Ming Jiang,et al.  A Heuristic Superiorization-Like Approach to Bioluminescence Tomography , 2013 .

[60]  Patrick L. Combettes,et al.  On the effectiveness of projection methods for convex feasibility problems with linear inequality constraints , 2009, Computational Optimization and Applications.

[61]  Shousheng Luo,et al.  Superiorization of EM Algorithm and Its Application in Single-Photon Emission Computed Tomography(SPECT) , 2012, 1209.6116.

[62]  G. Herman,et al.  Accelerated perturbation-resilient block-iterative projection methods with application to image reconstruction , 2012, Inverse problems.

[63]  Gabor T. Herman,et al.  Superiorization for Image Analysis , 2014, IWCIA.

[64]  P.L. Combettes On the numerical robustness of the parallel projection method in signal synthesis , 2001, IEEE Signal Processing Letters.