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Relevant bibliographies by topics / Bregman algorithms

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Academic literature on the topic 'Bregman algorithms'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Bregman algorithms"

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Bauschke, Heinz H., Jonathan M. Borwein, and Patrick L. Combettes. "Bregman Monotone Optimization Algorithms." SIAM Journal on Control and Optimization 42, no. 2 (2003): 596–636. http://dx.doi.org/10.1137/s0363012902407120.

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Chen, Jiawei, Zhongping Wan, Liuyang Yuan, and Yue Zheng. "Approximation of Fixed Points of Weak Bregman Relatively Nonexpansive Mappings in Banach Spaces." International Journal of Mathematics and Mathematical Sciences 2011 (2011): 1–23. http://dx.doi.org/10.1155/2011/420192.

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We introduce a concept of weak Bregman relatively nonexpansive mapping which is distinct from Bregman relatively nonexpansive mapping. By using projection techniques, we construct several modification of Mann type iterative algorithms with errors and Halpern-type iterative algorithms with errors to find fixed points of weak Bregman relatively nonexpansive mappings and Bregman relatively nonexpansive mappings in Banach spaces. The strong convergence theorems for weak Bregman relatively nonexpansive mappings and Bregman relatively nonexpansive mappings are derived under some suitable assumptions

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Wu, Qiang, Jinchao Feng, Kebin Jia, and Xiangyu Wang. "Improved Reconstruction Quality of Bioluminescent Images by Combining SP3Equations and Bregman Iteration Method." Computational and Mathematical Methods in Medicine 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/767296.

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Bioluminescence tomography (BLT) has a great potential to provide a powerful tool for tumor detection, monitoring tumor therapy progress, and drug development; developing new reconstruction algorithms will advance the technique to practical applications. In the paper, we propose a BLT reconstruction algorithm by combining SP3equations and Bregman iteration method to improve the quality of reconstructed sources. The numerical results for homogeneous and heterogeneous phantoms are very encouraging and give significant improvement over the algorithms without the use of SP3equations and Bregman it

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Zhu, Wei, Shi Shu, and Lizhi Cheng. "An Efficient Proximity Point Algorithm for Total-Variation-Based Image Restoration." Advances in Applied Mathematics and Mechanics 6, no. 2 (2014): 145–64. http://dx.doi.org/10.4208/aamm.2013.m175.

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AbstractIn this paper, we propose a fast proximity point algorithm and apply it to total variation (TV) based image restoration. The novel method is derived from the idea of establishing a general proximity point operator framework based on which new first-order schemes for total variation (TV) based image restoration have been proposed. Many current algorithms for TV-based image restoration, such as Chambolle’s projection algorithm, the split Bregman algorithm, the Bermúdez-Moreno algorithm, the Jia-Zhao denoising algorithm, and the fixed point algorithm, can be viewed as special cases of the

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Jolaoso, Lateef Olakunle, Maggie Aphane, and Safeer Hussain Khan. "Two Bregman Projection Methods for Solving Variational Inequality Problems in Hilbert Spaces with Applications to Signal Processing." Symmetry 12, no. 12 (2020): 2007. http://dx.doi.org/10.3390/sym12122007.

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Studying Bregman distance iterative methods for solving optimization problems has become an important and very interesting topic because of the numerous applications of the Bregman distance techniques. These applications are based on the type of convex functions associated with the Bregman distance. In this paper, two different extragraident methods were proposed for studying pseudomonotone variational inequality problems using Bregman distance in real Hilbert spaces. The first algorithm uses a fixed stepsize which depends on a prior estimate of the Lipschitz constant of the cost operator. The

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ABDULLAH, AMIRALI, JOHN MOELLER, and SURESH VENKATASUBRAMANIAN. "APPROXIMATE BREGMAN NEAR NEIGHBORS IN SUBLINEAR TIME: BEYOND THE TRIANGLE INEQUALITY." International Journal of Computational Geometry & Applications 23, no. 04n05 (2013): 253–301. http://dx.doi.org/10.1142/s0218195913600066.

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Bregman divergences are important distance measures that are used extensively in data-driven applications such as computer vision, text mining, and speech processing, and are a key focus of interest in machine learning. Answering nearest neighbor (NN) queries under these measures is very important in these applications and has been the subject of extensive study, but is problematic because these distance measures lack metric properties like symmetry and the triangle inequality. In this paper, we present the first provably approximate nearest-neighbor (ANN) algorithms for a broad sub-class of B

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Kuo, Li-Wei, and D. R. Sahu. "Bregman Distance and Strong Convergence of Proximal-Type Algorithms." Abstract and Applied Analysis 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/590519.

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The purpose of this paper is to discuss some fundamental properties of Bregman distance, generalized projection operators, firmly nonexpansive mappings, and resolvent operators of set-valued monotone operators corresponding to a functionalΦ(∥·∥). We further study some proximal point algorithms for finding zeros of monotone operators and solving generalized mixed equilibrium problems in Banach spaces. Our results improve and extend some recent results concerning generalized projection operators corresponding to Bregman distance.

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Hidalgo-Silva, Hugo, and E. Gómez-Treviño. "Bregman iterative algorithms for 2D geosounding inversion." Inverse Problems in Science and Engineering 23, no. 6 (2014): 1085–99. http://dx.doi.org/10.1080/17415977.2014.991729.

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Censor, Yair, and Gabor T. Herman. "Block-Iterative Algorithms with Underrelaxed Bregman Projections." SIAM Journal on Optimization 13, no. 1 (2002): 283–97. http://dx.doi.org/10.1137/s1052623401389439.

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Xiong, Zhao, Shi, and Wang. "A Convex Optimization Algorithm for Compressed Sensing in a Complex Domain: The Complex-Valued Split Bregman Method." Sensors 19, no. 20 (2019): 4540. http://dx.doi.org/10.3390/s19204540.

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The Split Bregman method (SBM), a popular and universal CS reconstruction algorithm for inverse problems with both l1-norm and TV-norm regularization, has been extensively applied in complex domains through the complex-to-real transforming technique, e.g., MRI imaging and radar. However, SBM still has great potential in complex applications due to the following two points; Bregman Iteration (BI), employed in SBM, may not make good use of the phase information for complex variables. In addition, the converting technique may consume more time. To address that, this paper presents the complex-val

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Dissertations / Theses on the topic "Bregman algorithms"

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Sun, Jiang. "Extending the metric multidimensional scaling with bregman divergences." Thesis, University of the West of Scotland, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.556070.

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Rousseau, Sylvain. "Détection de points d'intérêts dans une image multi ou hyperspectral par acquisition compressée." Thesis, Poitiers, 2013. http://www.theses.fr/2013POIT2269/document.

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Les capteurs multi- et hyper-spectraux génèrent un énorme flot de données. Un moyende contourner cette difficulté est de pratiquer une acquisition compressée de l'objet multi- ethyper-spectral. Les données sont alors directement compressées et l'objet est reconstruitlorsqu'on en a besoin. L'étape suivante consiste à éviter cette reconstruction et à travaillerdirectement avec les données compressées pour réaliser un traitement classique sur un objetde cette nature. Après avoir introduit une première approche qui utilise des outils riemannienspour effectuer une détection de contours dans une ima

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Silveti, Falls Antonio. "First-order noneuclidean splitting methods for large-scale optimization : deterministic and stochastic algorithms." Thesis, Normandie, 2021. http://www.theses.fr/2021NORMC204.

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Dans ce travail, nous développons et examinons deux nouveaux algorithmes d'éclatement du premier ordre pour résoudre des problèmes d'optimisation composites à grande échelle dans des espaces à dimensions infinies. Ces problèmes sont au coeur de nombres de domaines scientifiques et d'ingénierie, en particulier la science des données et l'imagerie. Notre travail est axé sur l'assouplissement des hypothèses de régularité de Lipschitz généralement requises par les algorithmes de fractionnement du premier ordre en remplaçant l'énergie euclidienne par une divergence de Bregman. Ces développements pe

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Nguyen, Van Quang. "Méthodes d'éclatement basées sur les distances de Bregman pour les inclusions monotones composites et l'optimisation." Thesis, Paris 6, 2015. http://www.theses.fr/2015PA066183/document.

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Le but de cette thèse est d'élaborer des méthodes d'éclatement basées sur les distances de Bregman pour la résolution d'inclusions monotones composites dans les espaces de Banach réels réflexifs. Ces résultats nous permettent d'étendre de nombreuses techniques, jusqu'alors limitées aux espaces hilbertiens. De plus, même dans le cadre restreint d'espaces euclidiens, ils donnent lieu à de nouvelles méthodes de décomposition qui peuvent s'avérer plus avantageuses numériquement que les méthodes classiques basées sur la distance euclidienne. Des applications numériques en traitement de l'image sont

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Park, Yonggi. "PARAMETER SELECTION RULES FOR ILL-POSED PROBLEMS." Kent State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=kent1574079328985475.

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Ruiz, Garrido Natalia Soledad Karen. "Contributions to the convergence theory and computational implementation of interior optimization methods for convex problems." Tesis, Universidad de Chile, 2016. http://repositorio.uchile.cl/handle/2250/140605.

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Doctor en Ciencias de la Ingeniería, Mención Modelación Matemática<br>En esta tesis doctoral se estudian algoritmos para resolver problemas de optimización convexa con estructura separable, y problemas de equilibrio económico de Walras. Así, la tesis se divide en dos partes. La primera parte corresponde al estudio teórico y numérico de un método de direcciones alternantes de multiplicadores el cual usa un término proximal interior. La segunda parte está dedicada al estudio numérico de algoritmos de segundo orden para resolver problemas de maximización de utilidades que aparecen en problem

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Nenna, Luca. "Numerical Methods for Multi-Marginal Optimal Transportation." Thesis, Paris Sciences et Lettres (ComUE), 2016. http://www.theses.fr/2016PSLED017/document.

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Dans cette thèse, notre but est de donner un cadre numérique général pour approcher les solutions des problèmes du transport optimal (TO). L’idée générale est d’introduire une régularisation entropique du problème initial. Le problème régularisé correspond à minimiser une entropie relative par rapport à une mesure de référence donnée. En effet, cela équivaut à trouver la projection d’un couplage par rapport à la divergence de Kullback-Leibler. Cela nous permet d’utiliser l’algorithme de Bregman/Dykstra et de résoudre plusieurs problèmes variationnels liés au TO. Nous nous intéressons particuli

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Moreira, Neto Alvaro. "Método do Ponto proximal usando distâncias generalizadas separáveis - reescala e seleção do comprimento do passo." Universidade Federal de Goiás, 2008. http://repositorio.bc.ufg.br/tede/handle/tde/2909.

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Submitted by Luciana Ferreira (lucgeral@gmail.com) on 2014-08-19T11:27:00Z No. of bitstreams: 2 license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Dissertacao Alvaro Moreira Neto.pdf: 1177417 bytes, checksum: 8eec9ca4c85a0eebfd029a0c50df7cbe (MD5)<br>Made available in DSpace on 2014-08-19T11:27:00Z (GMT). No. of bitstreams: 2 license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Dissertacao Alvaro Moreira Neto.pdf: 1177417 bytes, checksum: 8eec9ca4c85a0eebfd029a0c50df7cbe (MD5) Previous issue date: 2008-05-29<br>In this work....<br>Nessa dis

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Papoutsellis, Evangelos. "First-order gradient regularisation methods for image restoration : reconstruction of tomographic images with thin structures and denoising piecewise affine images." Thesis, University of Cambridge, 2016. https://www.repository.cam.ac.uk/handle/1810/256216.

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The focus of this thesis is variational image restoration techniques that involve novel non-smooth first-order gradient regularisers: Total Variation (TV) regularisation in image and data space for reconstruction of thin structures from PET data and regularisers given by an infimal-convolution of TV and $L^p$ seminorms for denoising images with piecewise affine structures. In the first part of this thesis, we present a novel variational model for PET reconstruction. During a PET scan, we encounter two different spaces: the sinogram space that consists of all the PET data collected from the det

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VELÁSQUEZ, Marco Antonio Lázaro. "Trajetória central, métodos de ponto proximal generalizado e trajetória de Cauchy em variedades Riemannianas." Universidade Federal de Campina Grande, 2007. http://dspace.sti.ufcg.edu.br:8080/jspui/handle/riufcg/1153.

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Submitted by Johnny Rodrigues (johnnyrodrigues@ufcg.edu.br) on 2018-07-11T21:15:21Z No. of bitstreams: 1 MARCO ANTONIO LÁZARO VELÁSQUEZ - DISSERTAÇÃO PPGMAT 2007..pdf: 704392 bytes, checksum: 65d621d0e292ed7ae65f9c1d129b6200 (MD5)<br>Made available in DSpace on 2018-07-11T21:15:21Z (GMT). No. of bitstreams: 1 MARCO ANTONIO LÁZARO VELÁSQUEZ - DISSERTAÇÃO PPGMAT 2007..pdf: 704392 bytes, checksum: 65d621d0e292ed7ae65f9c1d129b6200 (MD5) Previous issue date: 2007-03<br>Capes<br>Em problemas de otimização convexa e, de maneira geral, em problemas de inequações variacionais aparecem os conceitos d

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Book chapters on the topic "Bregman algorithms"

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Manthey, Bodo, and Heiko Röglin. "Worst-Case and Smoothed Analysis of k-Means Clustering with Bregman Divergences." In Algorithms and Computation. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10631-6_103.

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Srivastava, Kunal, Angelia Nedić, and Dušan Stipanović. "Distributed Bregman-Distance Algorithms for Min-Max Optimization." In Studies in Computational Intelligence. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34097-0_7.

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Mukkamala, Mahesh Chandra, Felix Westerkamp, Emanuel Laude, Daniel Cremers, and Peter Ochs. "Bregman Proximal Gradient Algorithms for Deep Matrix Factorization." In Lecture Notes in Computer Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75549-2_17.

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Güler, Osman. "Ergodic Convergence in Proximal Point Algorithms with Bregman Functions." In Nonconvex Optimization and Its Applications. Springer US, 1994. http://dx.doi.org/10.1007/978-1-4613-3629-7_7.

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Serrano, Estefania, Tom Vander Aa, Roel Wuyts, Javier Garcia Blas, Jesus Carretero, and Monica Abella. "Exploring a Distributed Iterative Reconstructor Based on Split Bregman Using PETSc." In Algorithms and Architectures for Parallel Processing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49956-7_15.

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Benning, Martin, and Erlend Skaldehaug Riis. "Bregman Methods for Large-Scale Optimisation with Applications in Imaging." In Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-03009-4_62-1.

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Petra, Stefania, Christoph Schnörr, Florian Becker, and Frank Lenzen. "B-SMART: Bregman-Based First-Order Algorithms for Non-negative Compressed Sensing Problems." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38267-3_10.

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Tian, Xiaoyun, Dachuan Xu, Longkun Guo, and Dan Wu. "An Improved Bregman k-means++ Algorithm via Local Search." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58150-3_43.

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Setzer, Simon. "Split Bregman Algorithm, Douglas-Rachford Splitting and Frame Shrinkage." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02256-2_39.

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Duan, Jinming, Wenqi Lu, Guodong Wang, Zhenkuan Pan, and Li Bai. "Second Order Variational Model for Image Decomposition Using Split Bregman Algorithm." In Lecture Notes in Computer Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23989-7_63.

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Conference papers on the topic "Bregman algorithms"

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Ackermann, Marcel R., and Johannes Blömer. "Coresets and Approximate Clustering for Bregman Divergences." In Proceedings of the Twentieth Annual ACM-SIAM Symposium on Discrete Algorithms. Society for Industrial and Applied Mathematics, 2009. http://dx.doi.org/10.1137/1.9781611973068.118.

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Zou, Jian, Yuli Fu, Qiheng Zhang, and Haifeng Li. "Split Bregman algorithms for block-sparse reconstruction." In 2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI). IEEE, 2012. http://dx.doi.org/10.1109/icaci.2012.6463349.

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Barak, Boaz, Moritz Hardt, and Satyen Kale. "The Uniform Hardcore Lemma via Approximate Bregman Projections." In Proceedings of the Twentieth Annual ACM-SIAM Symposium on Discrete Algorithms. Society for Industrial and Applied Mathematics, 2009. http://dx.doi.org/10.1137/1.9781611973068.129.

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Campagna, Rosanna, Serena Crisci, Salvatore Cuomo, Ardelio Galletti, and Livia Marcellino. "A second order derivative scheme based on Bregman algorithm class." In NUMERICAL COMPUTATIONS: THEORY AND ALGORITHMS (NUMTA–2016): Proceedings of the 2nd International Conference “Numerical Computations: Theory and Algorithms”. Author(s), 2016. http://dx.doi.org/10.1063/1.4965319.

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Wang, Zi-Ming, and Airong Wei. "Hybrid block Bregman projection algorithms for a finite family of Bregman quasi-strict pseudo-contractions." In 2017 Chinese Automation Congress (CAC). IEEE, 2017. http://dx.doi.org/10.1109/cac.2017.8243368.

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Zou, Jian, and Shugang Song. "Split bregman algorithms for joint sparse recovery with analysis prior." In 2014 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC). IEEE, 2014. http://dx.doi.org/10.1109/icspcc.2014.6986231.

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Zhao, Zaixin, and Lizhi Cheng. "Fast algorithms for image decomposition based on split Bregman technology." In 2010 3rd International Congress on Image and Signal Processing (CISP). IEEE, 2010. http://dx.doi.org/10.1109/cisp.2010.5647215.

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Lunglmayr, Michael, and Mario Huemer. "Sparsity-Enabled Step Width Adaption For Linearized Bregman Based Algorithms." In 2018 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2018. http://dx.doi.org/10.1109/ssp.2018.8450706.

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Gogna, A., A. Shukla, H. K. Agarwal, and A. Majumdar. "Split Bregman algorithms for sparse / joint-sparse and low-rank signal recovery: Application in compressive hyperspectral imaging." In 2014 IEEE International Conference on Image Processing (ICIP). IEEE, 2014. http://dx.doi.org/10.1109/icip.2014.7025260.

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Pan, Zhenkuan, Cuiping Wang, Weibo Wei, and Chao Lu. "The General Variation Models of Additive and Multiplicative Noise Removal of Color Images and Their Split Bregman Algorithms." In 2011 9th International Conference on Software Engineering Research, Management and Applications (SERA). IEEE, 2011. http://dx.doi.org/10.1109/sera.2011.13.

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Reports on the topic "Bregman algorithms"

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Warren, Russell, Stanley Osher, and Richard Vanderbeek. Multiple Aerosol Unmixing by the Split Bregman Algorithm. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada555738.

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