Modular Proximal Optimization for Multidimensional Total-Variation Regularization
暂无分享,去创建一个
[1] S. Osher,et al. Decomposition of images by the anisotropic Rudin‐Osher‐Fatemi model , 2004 .
[2] A. Rinaldo. Properties and refinements of the fused lasso , 2008, 0805.0234.
[3] Inderjit S. Dhillon,et al. A scalable trust-region algorithm with application to mixed-norm regression , 2010, ICML.
[4] Antonin Chambolle,et al. On the ergodic convergence rates of a first-order primal–dual algorithm , 2016, Math. Program..
[5] D. Bertsekas. Projected Newton methods for optimization problems with simple constraints , 1981, 1981 20th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes.
[6] Laurent Condat,et al. A Generic Proximal Algorithm for Convex Optimization—Application to Total Variation Minimization , 2014, IEEE Signal Processing Letters.
[7] A. Chambolle,et al. A remark on accelerated block coordinate descent for computing the proximity operators of a sum of convex functions , 2015 .
[8] Julien Mairal,et al. Convex optimization with sparsity-inducing norms , 2011 .
[9] R. Tibshirani,et al. Spatial smoothing and hot spot detection for CGH data using the fused lasso. , 2008, Biostatistics.
[10] Jorge Nocedal,et al. A Limited Memory Algorithm for Bound Constrained Optimization , 1995, SIAM J. Sci. Comput..
[11] José R. Dorronsoro,et al. Finding Optimal Model Parameters by Discrete Grid Search , 2008, Innovations in Hybrid Intelligent Systems.
[12] K. Schittkowski,et al. NONLINEAR PROGRAMMING , 2022 .
[13] Suvrit Sra,et al. Fast Newton methods for the group fused lasso , 2014, UAI.
[14] B. Martinet. Brève communication. Régularisation d'inéquations variationnelles par approximations successives , 1970 .
[15] Søren Holdt Jensen,et al. Algorithms and software for total variation image reconstruction via first-order methods , 2009, Numerical Algorithms.
[16] Ryan J. Tibshirani,et al. Fast and Flexible ADMM Algorithms for Trend Filtering , 2014, ArXiv.
[17] Otmar Scherzer,et al. A Derivative-Free Approach to Total Variation Regularization , 2009 .
[18] Mingqiang Zhu,et al. An Efficient Primal-Dual Hybrid Gradient Algorithm For Total Variation Image Restoration , 2008 .
[19] D. Pinkel,et al. Regional copy number–independent deregulation of transcription in cancer , 2006, Nature Genetics.
[20] P. Davies,et al. Local Extremes, Runs, Strings and Multiresolution , 2001 .
[21] D. Gleich. TRUST REGION METHODS , 2017 .
[22] Suvrit Sra,et al. Fast Newton-type Methods for Total Variation Regularization , 2011, ICML.
[23] Laurent Condat,et al. A Direct Algorithm for 1-D Total Variation Denoising , 2013, IEEE Signal Processing Letters.
[24] Julien Mairal,et al. Network Flow Algorithms for Structured Sparsity , 2010, NIPS.
[25] R. Rockafellar. Monotone Operators and the Proximal Point Algorithm , 1976 .
[26] José M. Bioucas-Dias,et al. Total Variation-Based Image Deconvolution: a Majorization-Minimization Approach , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.
[27] Qingyang Li,et al. A Highly Scalable Parallel Algorithm for Isotropic Total Variation Models , 2014, ICML.
[28] Cordelia Schmid,et al. Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search , 2008, ECCV.
[29] Antonin Chambolle,et al. A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging , 2011, Journal of Mathematical Imaging and Vision.
[30] M. Yuan,et al. Model selection and estimation in regression with grouped variables , 2006 .
[31] D K Smith,et al. Numerical Optimization , 2001, J. Oper. Res. Soc..
[32] Xue-Cheng Tai,et al. Domain decomposition methods with graph cuts algorithms for total variation minimization , 2012, Adv. Comput. Math..
[33] Gabriele Steidl,et al. Anisotropic Smoothing Using Double Orientations , 2009, SSVM.
[34] Shuiwang Ji,et al. SLEP: Sparse Learning with Efficient Projections , 2011 .
[35] Saverio Salzo,et al. Inexact and accelerated proximal point algorithms , 2011 .
[36] Stephen J. Wright,et al. Optimization for Machine Learning , 2013 .
[37] Curtis R. Vogel,et al. Iterative Methods for Total Variation Denoising , 1996, SIAM J. Sci. Comput..
[38] Tom Goldstein,et al. The Split Bregman Method for L1-Regularized Problems , 2009, SIAM J. Imaging Sci..
[39] Wotao Yin,et al. Parametric Maximum Flow Algorithms for Fast Total Variation Minimization , 2009, SIAM J. Sci. Comput..
[40] Suvrit Sra,et al. Scalable nonconvex inexact proximal splitting , 2012, NIPS.
[41] Heinz H. Bauschke,et al. Finding best approximation pairs relative to two closed convex sets in Hilbert spaces , 2004, J. Approx. Theory.
[42] Patrick L. Combettes,et al. Proximal Splitting Methods in Signal Processing , 2009, Fixed-Point Algorithms for Inverse Problems in Science and Engineering.
[43] Le Song,et al. Estimating time-varying networks , 2008, ISMB 2008.
[44] Colin Campbell,et al. The latent process decomposition of cDNA microarray data sets , 2005, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[45] Rob Fergus,et al. Fast Image Deconvolution using Hyper-Laplacian Priors , 2009, NIPS.
[46] K. Kiwiel. Variable Fixing Algorithms for the Continuous Quadratic Knapsack Problem , 2008 .
[47] J. Mesirov,et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.
[48] Donald E. Knuth,et al. The Art of Computer Programming, Volume I: Fundamental Algorithms, 2nd Edition , 1997 .
[49] Chih-Jen Lin,et al. Newton's Method for Large Bound-Constrained Optimization Problems , 1999, SIAM J. Optim..
[50] H. D. Brunk,et al. Statistical inference under order restrictions : the theory and application of isotonic regression , 1973 .
[51] Vladimir Kolmogorov,et al. Total Variation on a Tree , 2015, SIAM J. Imaging Sci..
[52] Jean-Philippe Vert,et al. Fast detection of multiple change-points shared by many signals using group LARS , 2010, NIPS.
[53] L. Rudin,et al. Nonlinear total variation based noise removal algorithms , 1992 .
[54] Heinz H. Bauschke,et al. Convex Analysis and Monotone Operator Theory in Hilbert Spaces , 2011, CMS Books in Mathematics.
[55] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[56] Antonin Chambolle,et al. On Total Variation Minimization and Surface Evolution Using Parametric Maximum Flows , 2009, International Journal of Computer Vision.
[57] Stephen P. Boyd,et al. Proximal Algorithms , 2013, Found. Trends Optim..
[58] José M. Bioucas-Dias,et al. Fast Image Recovery Using Variable Splitting and Constrained Optimization , 2009, IEEE Transactions on Image Processing.
[60] Jieping Ye,et al. An efficient ADMM algorithm for multidimensional anisotropic total variation regularization problems , 2013, KDD.
[61] Laurent Condat,et al. A Fast Projection onto the Simplex and the l 1 Ball , 2015 .
[62] José M. Bioucas-Dias,et al. A New TwIST: Two-Step Iterative Shrinkage/Thresholding Algorithms for Image Restoration , 2007, IEEE Transactions on Image Processing.
[63] Stephen P. Boyd,et al. An ADMM Algorithm for a Class of Total Variation Regularized Estimation Problems , 2012, 1203.1828.
[64] Yuying Li,et al. A computational algorithm for minimizing total variation in image restoration , 1996, IEEE Trans. Image Process..
[65] Suvrit Sra,et al. Reflection methods for user-friendly submodular optimization , 2013, NIPS.
[66] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[67] Yaoliang Yu,et al. On Decomposing the Proximal Map , 2013, NIPS.
[68] Francis R. Bach,et al. Structured sparsity-inducing norms through submodular functions , 2010, NIPS.
[69] Nicholas A. Johnson,et al. A Dynamic Programming Algorithm for the Fused Lasso and L 0-Segmentation , 2013 .
[70] R. Tibshirani,et al. PATHWISE COORDINATE OPTIMIZATION , 2007, 0708.1485.
[71] Jorge J. Moré,et al. Computing a Trust Region Step , 1983 .
[72] Emmanuel Barillot,et al. Classification of arrayCGH data using fused SVM , 2008, ISMB.
[73] K. Kunisch,et al. An active set strategy based on the augmented Lagrangian formulation for image restoration , 1999 .
[74] B. Martinet,et al. R'egularisation d''in'equations variationnelles par approximations successives , 1970 .
[75] Laurent Condat. Fast projection onto the simplex and the l1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pmb {l}_\mathbf {1}$$\end{ , 2015, Mathematical Programming.
[76] Marc Teboulle,et al. A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems , 2009, SIAM J. Imaging Sci..
[77] U. Alon,et al. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. , 1999, Proceedings of the National Academy of Sciences of the United States of America.
[78] R. Tibshirani,et al. Sparsity and smoothness via the fused lasso , 2005 .
[79] Markus Grasmair,et al. The Equivalence of the Taut String Algorithm and BV-Regularization , 2006, Journal of Mathematical Imaging and Vision.
[80] R. Tibshirani. Adaptive piecewise polynomial estimation via trend filtering , 2013, 1304.2986.
[81] Francis R. Bach,et al. Learning with Submodular Functions: A Convex Optimization Perspective , 2011, Found. Trends Mach. Learn..
[82] Mark W. Schmidt,et al. Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization , 2011, NIPS.
[83] Emmanuel J. Candès,et al. Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.
[84] Adam M. Oberman,et al. Anisotropic Total Variation Regularized L^1-Approximation and Denoising/Deblurring of 2D Bar Codes , 2010, 1007.1035.
[85] P. L. Combettes. Iterative construction of the resolvent of a sum of maximal monotone operators , 2009 .
[86] Edward R. Dougherty,et al. Performance of feature-selection methods in the classification of high-dimension data , 2009, Pattern Recognit..
[87] Stephan Didas,et al. Relations Between Higher Order TV Regularization and Support Vector Regression , 2005, Scale-Space.
[88] Jun Liu,et al. Efficient Euclidean projections in linear time , 2009, ICML '09.
[89] Stephen P. Boyd,et al. 1 Trend Filtering , 2009, SIAM Rev..
[90] Han Liu,et al. Estimation Consistency of the Group Lasso and its Applications , 2009, AISTATS.
[91] P. Cochat,et al. Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.
[92] Jieping Ye,et al. An efficient algorithm for a class of fused lasso problems , 2010, KDD.
[93] Stephen J. Wright,et al. Sparse Reconstruction by Separable Approximation , 2008, IEEE Transactions on Signal Processing.
[94] Ed Anderson,et al. LAPACK Users' Guide , 1995 .
[95] P. Bühlmann,et al. The group lasso for logistic regression , 2008 .
[96] José R. Dorronsoro,et al. Group Fused Lasso , 2013, ICANN.
[97] J. Moreau. Fonctions convexes duales et points proximaux dans un espace hilbertien , 1962 .
[98] Y. Nesterov. Gradient methods for minimizing composite objective function , 2007 .
[99] Guy Pierra,et al. Decomposition through formalization in a product space , 1984, Math. Program..
[100] José R. Dorronsoro,et al. Finding optimal model parameters by deterministic and annealed focused grid search , 2009, Neurocomputing.
[101] Suvrit Sra,et al. Convex Optimization for Parallel Energy Minimization , 2015, ArXiv.
[102] Martin Jaggi,et al. Revisiting Frank-Wolfe: Projection-Free Sparse Convex Optimization , 2013, ICML.
[103] Z. Harchaoui,et al. Multiple Change-Point Estimation With a Total Variation Penalty , 2010 .
[104] Pablo A. Parrilo,et al. The Convex Geometry of Linear Inverse Problems , 2010, Foundations of Computational Mathematics.