└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # Sparse Optimization 2 | I am currently working on nonconvex optimization problems involving L0 norm instead of convex/nonconvex surrogates. 3 | - [Surveys](#Surveys) 4 | - [Constrained Methods](#Constrained_Methods) 5 | - [Regularized Methods](#Regularized_Methods) 6 | - [Learning Methods](#Learning_Methods) 7 | - [Applications](#Applications) 8 | - [Links](#Links) 9 | 10 | 11 | > [!IMPORTANT] 12 | > **Last Update: 2024/02/05 (No longer updated!)** 13 | 14 | 15 | 16 | 17 | 18 | 19 | ### Surveys 20 | - [2023] Deep Learning Meets Sparse Regularization: A Signal Processing Perspective, IEEE SPM [[Paper](https://ieeexplore.ieee.org/document/10243466)] 21 | - [2022] High-Dimensional Data Analysis with Low-Dimensional Models: Principles, Computation, and Applications, Cambridge University Press [[Book](https://book-wright-ma.github.io/)] 22 | - [2022] Foundations of Computational Imaging: A Model-Based Approach, SIAM [[Book](https://epubs.siam.org/doi/book/10.1137/1.9781611977134)] 23 | - [2022] 稀疏优化二阶算法研究进展, 数值计算与计算机应用 [[Paper](https://computmath.cjoe.ac.cn/szjs/CN/10.12288/szjs.s2021-0759)] 24 | - [2020] 稀疏优化理论与算法若干新进展, 运筹学学报 [[Paper](https://www.ort.shu.edu.cn/CN/10.15960/j.cnki.issn.1007-6093.2020.04.001)] 25 | - [2020] Statistical Foundations of Data Science, CRC Press [[Book](https://www.taylorfrancis.com/books/mono/10.1201/9780429096280/statistical-foundations-data-science-jianqing-fan-runze-li-cun-hui-zhang-hui-zou)] 26 | - [2018] Optimization Methods for Large-Scale Machine Learning, SIAM Review [[Paper](https://epubs.siam.org/doi/abs/10.1137/16M1080173)] 27 | - [2018] Sparse Optimization Theory and Methods, CRC Press [[Book](https://www.taylorfrancis.com/books/mono/10.1201/9781315113142/sparse-optimization-theory-methods-yun-bin-zhao)] 28 | - [2017] Feature Selection Based on Structured Sparsity: A Comprehensive Study [[Paper](https://ieeexplore.ieee.org/document/7458185)] [[Matlab](https://github.com/guijiejie/Feature-selection/tree/master/Feature%20selection%20based%20on%20structured%20sparsity)] 29 | - [2017] Non-convex Optimization for Machine Learning, Foundations and Trends in Machine Learning [[Paper](https://www.nowpublishers.com/article/Details/MAL-058)] 30 | - [2015] Statistical Learning with Sparsity: The Lasso and Generalizations, CRC Press [[book](https://hastie.su.domains/StatLearnSparsity_files/SLS_corrected_1.4.16.pdf)] 31 | - [2014] Sparse Modeling: Theory, Algorithms, and Applications, CRC Press [[Book](https://www.taylorfrancis.com/books/mono/10.1201/b17758/sparse-modeling-irina-rish-genady-grabarnik)] 32 | - [2012] Optimization with Sparsity-Inducing Penalties, Foundations and Trends in Machine Learning [[Paper](https://www.nowpublishers.com/article/Details/MAL-015)] 33 | - [2012] 压缩感知和稀疏优化简介, 运筹学学报 [[Paper](https://www.ort.shu.edu.cn/CN/Y2012/V16/I3/49)] 34 | - [2012] 压缩感知, 中国科学 [[Paper](https://dds.sciengine.com/cfs/files/pdfs/view/1674-7216/bevBnqMiAzjDxRHki.pdf)] 35 | 36 | 37 | 38 | 39 | 40 | ## Constrained Methods 41 | 42 | ### First-Order Algorithms 43 | - [2017] Gradient Hard Thresholding Pursuit, Journal of Machine Learning Research [[Paper](https://www.jmlr.org/papers/volume18/14-415/14-415.pdf)] 44 | - [2017] A Convergent Iterative Hard Thresholding for Nonnegative Sparsity Optimization, Pacific Journal of Optimization [[Paper](http://www.yokohamapublishers.jp/online2/oppjo/vol13/p325.html)] [[Matlab](https://github.com/ShenglongZhou/IIHT)] 45 | - [2016] On the Minimization over Sparse Symmetric Sets: Projections, Optimality Conditions, and Algorithms, Mathematics of Operations Research [[Paper](https://pubsonline.informs.org/doi/abs/10.1287/moor.2015.0722)] 46 | - [2015] On Solutions of Sparsity Constrained Optimization, Journal of the Operations Research Society of China [[Paper](https://link.springer.com/article/10.1007/s40305-015-0101-3)] 47 | - [2013] Greedy Sparsity-Constrained Optimization, Journal of Machine Learning Research [[paper](https://www.jmlr.org/papers/volume14/bahmani13a/bahmani13a.pdf)] 48 | - [2013] Sparsity Constrained Nonlinear Optimization: Optimality Conditions and Algorithms, SIAM Journal on Optimization [[Paper](https://epubs.siam.org/doi/abs/10.1137/120869778)] 49 | - [2012] Accelerated Iterative Hard Thresholding, Signal Processing [[Paper](https://www.sciencedirect.com/science/article/abs/pii/S0165168411003197)] 50 | - [2011] Hard Thresholding Pursuit: An Algorithm for Compressive Sensing, SIAM Journal on Numerical Analysis [[Paper](https://epubs.siam.org/doi/abs/10.1137/100806278)] 51 | - [2010] Normalized Iterative Hard Thresholding: Guaranteed Stability and Performance, IEEE JSTSP [[Paper](https://ieeexplore.ieee.org/abstract/document/5419091)] 52 | - [2009] CoSaMP: Iterative Signal Recovery from Incomplete and Inaccurate Samples, Applied and Computational Harmonic Analysis [[Paper](https://www.sciencedirect.com/science/article/pii/S1063520308000638)] [[Matlab](https://ww2.mathworks.cn/matlabcentral/fileexchange/32402-cosamp-and-omp-for-sparse-recovery)] 53 | - [2007] Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and Other Inverse Problems, IEEE JSTSP [[Paper](https://ieeexplore.ieee.org/abstract/document/4407762)] 54 | - [2007] Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit, IEEE TIT [[Paper](https://ieeexplore.ieee.org/abstract/document/4385788)] 55 | - [1993] Orthogonal Matching Pursuit: Recursive Function Approximation with Applications to Wavelet Decomposition, IEEE ACSSC [[Paper](https://ieeexplore.ieee.org/abstract/document/342465)] 56 | 57 | 58 | ### Second-Order Algorithms 59 | - [2022] Sparse SVM for Sufficient Data Reduction, IEEE TPAMI [[Paper](https://ieeexplore.ieee.org/document/9415153)] [[Matlab](https://github.com/ShenglongZhou/NSSVM)] 60 | - [2022] Gradient Projection Newton Pursuit for Sparsity Constrained Optimization, Applied and Computational Harmonic Analysis [[Paper](https://www.sciencedirect.com/science/article/pii/S1063520322000458)] [[Matlab](https://github.com/ShenglongZhou/GPNP)] 61 | - [2022] A Lagrange-Newton Algorithm for Sparse Nonlinear Programming, Mathematical Programming [[Paper](https://link.springer.com/article/10.1007/s10107-021-01719-x)] 62 | - [2021] Newton Hard-Thresholding Pursuit for Sparse Linear Complementarity Problem via A New Merit Function, SIAM Journal on Scientific Computing [[Paper](https://epubs.siam.org/doi/10.1137/19M1301539)] [[Matlab](https://github.com/ShenglongZhou/NHTP)] 63 | - [2021] Global and Quadratic Convergence of Newton Hard-Thresholding Pursuit, Journal of Machine Learning Research [[Paper](https://jmlr.org/papers/volume22/19-026/19-026.pdf)] [[Matlab](https://github.com/ShenglongZhou/NHTP)] 64 | - [2020] Greedy Projected Gradient-Newton Method for Sparse Logistic Regression, IEEE TNNLS [[Paper](https://ieeexplore.ieee.org/abstract/document/8688642)] 65 | - [2017] Newton-Type Greedy Selection Methods for L0-Constrained Minimization, IEEE TPAMI [[Paper](https://ieeexplore.ieee.org/abstract/document/7814339)] 66 | - [2017] Fast Newton Hard Thresholding Pursuit for Sparsity Constrained Non-Convex Optimization, KDD [[Paper](https://dl.acm.org/doi/abs/10.1145/3097983.3098165)] 67 | - [2013] Greedy Sparsity-Constrained Optimization, Journal of Machine Learning Research [[Paper](https://www.jmlr.org/papers/volume14/bahmani13a/bahmani13a.pdf)] 68 | 69 | 70 | 71 | 72 | ## Regularized Methods 73 | 74 | ### First-Order Algorithms 75 | - [2020] An Active Set Barzilar-Borwein Algorithm for L0 Regularized Optimization, Journal of Global Optimization [[Paper](https://link.springer.com/article/10.1007/s10898-019-00830-w)] 76 | - [2020] A Smoothing Proximal Gradient Algorithm for Nonsmooth Convex Regression with Cardinality Penalty, SIAM Journal on Numerical Analysis [[paper](https://epubs.siam.org/doi/abs/10.1137/18M1186009)] 77 | - [2018] Proximal Mapping for Symmetric Penalty and Sparsity, SIAM Journal on Optimization [[Paper](https://epubs.siam.org/doi/abs/10.1137/17M1116544)] 78 | - [2016] Image Restoration by Minimizing Zero Norm of Wavelet Frame Coefficients, Inverse Problems [[Paper](https://iopscience.iop.org/article/10.1088/0266-5611/32/11/115004/meta)] 79 | - [2015] Homotopy Based Algorithms for L0-Regularized Least-Squares, IEEE TSP [[Paper](https://ieeexplore.ieee.org/abstract/document/7084156)] 80 | - [2015] CGIHT: Conjugate Gradient Iterative Hard Thresholding for Compressed Sensing and Matrix Completion, Information and Inference [[Paper](https://ieeexplore.ieee.org/abstract/document/8189185)] 81 | - [2014] Iterative Hard Thresholding Methods for L0 Regularized Convex Cone Programming, Mathematical Programming [[Paper](https://link.springer.com/article/10.1007/s10107-013-0714-4)] 82 | - [2013] Sparse Approximation via Penalty Decomposition Methods, SIAM Journal on Optimization [[Paper](https://epubs.siam.org/doi/abs/10.1137/100808071)] 83 | - [2009] Iterative Hard Thresholding for Compressed Sensing, Applied and Computational Harmonic Analysis [[Paper](https://www.sciencedirect.com/science/article/pii/S1063520309000384)] 84 | 85 | 86 | 87 | ### Second-Order Algorithms 88 | - [2023] Revisiting Lq (0<=q<1) Norm Regularized Optimization, ArXiv [[Paper](https://arxiv.org/abs/2306.14394)] [[Matlab](https://github.com/ShenglongZhou/PSNP)] 89 | - [2022] Newton Method for L0-Regularized Optimization, Numerical Algorithms [[Paper](https://link.springer.com/article/10.1007/s11075-021-01085-x)] [[Matlab](https://github.com/ShenglongZhou/NL0R)] 90 | - [2018] A Constructive Approach to l0 Penalized Regression, Journal of Machine Learning Research [[Paper](https://www.jmlr.org/papers/volume19/17-194/17-194.pdf)] 91 | - [2015] A Primal Dual Active Set with Continuation Algorithm for the l0-Regularized Optimization Problem, Applied and Computational Harmonic Analysis [[Paper](https://www.sciencedirect.com/science/article/pii/S1063520314001250)] 92 | - [2013] A Variational Approach to Sparsity Optimization Based on Lagrange Multiplier Theory, Inverse Problems [[Paper](https://iopscience.iop.org/article/10.1088/0266-5611/30/1/015001/meta)] 93 | 94 | 95 | 96 | 97 | 98 | ## Learning Methods 99 | - [2023] Sparse Plus Low Rank Matrix Decomposition: A Discrete Optimization Approach, Journal of Machine Learning Research [[Paper](https://www.jmlr.org/papers/volume24/21-1130/21-1130.pdf)] [[Julia](https://github.com/NicholasJohnson2020/SparseLowRankSoftware)] 100 | - [2022] A Comparative Study of Multi-Objective Optimization Algorithms for Sparse Signal Reconstruction, Artificial Intelligence Review [[Paper](https://link.springer.com/article/10.1007/s10462-021-10073-5)] 101 | - [2022] Learning to Optimize: A Primer and A Benchmark, Journal of Machine Learning Research [[Paper](https://dl.acm.org/doi/abs/10.5555/3586589.3586778)] [[Python](https://github.com/VITA-Group/Open-L2O)] 102 | - [2020] ADMM-CSNet: A Deep Learning Approach for Image Compressive Sensing, IEEE TPAMI [[Paper](https://ieeexplore.ieee.org/abstract/document/8550778)] [[Python](https://github.com/yangyan92/Pytorch_ADMM-CSNet)] 103 | - [2019] Evolutionary Multitasking Sparse Reconstruction: Framework and Case Study, IEEE TEVC [[Paper](https://ieeexplore.ieee.org/abstract/document/8540026)] 104 | - [2018] FFDNet: Toward a Fast and Flexible Solution for CNN-Based Image Denoising, IEEE TIP [[Paper](https://ieeexplore.ieee.org/abstract/document/8365806)] [[Matlab](https://github.com/cszn/FFDNet)] 105 | - [2017] Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems, ICCV [[Paper](https://openaccess.thecvf.com/content_iccv_2017/html/Meinhardt_Learning_Proximal_Operators_ICCV_2017_paper.html)] [[Python](https://github.com/tum-vision/learn_prox_ops)] 106 | - [2017] Compressed Sensing using Generative Models, ICML [[Paper](http://proceedings.mlr.press/v70/bora17a.html)] 107 | - [2016] Maximal Sparsity with Deep Networks? NIPS [[Paper](https://proceedings.neurips.cc/paper_files/paper/2016/hash/0d73a25092e5c1c9769a9f3255caa65a-Abstract.html)] 108 | - [2014] An Evolutionary Multiobjective Approach to Sparse Reconstruction, IEEE TEVC [[Paper](https://ieeexplore.ieee.org/abstract/document/6646243)] 109 | - [2010] Learning Fast Approximations of Sparse Coding, ICML [[Paper](https://dl.acm.org/doi/abs/10.5555/3104322.3104374)] [[Matlab](https://github.com/minhnhat93/lfa_sc)] 110 | 111 | 112 | 113 | 114 | 115 | ## Applications 116 | 117 | ### Unsupervised Feature Selection 118 | - [2023] Effcient Top-K Feature Selection Using Coordinate Descent Method, AAAI [[Paper](https://ojs.aaai.org/index.php/AAAI/article/view/26258)] [[Matlab](https://github.com/solerxl/Code_For_AAAI_2023)] 119 | - [2023] Structured Sparsity Optimization With Non-Convex Surrogates of L2,0-Norm: A Unified Algorithmic Framework, IEEE TPAMI [[Paper](https://ieeexplore.ieee.org/abstract/document/9916142)] 120 | - [2023] Learning Feature-Sparse Principal Subspace, IEEE TPAMI [[Paper](https://ieeexplore.ieee.org/abstract/document/9941008)] [[Matlab](https://github.com/icety3/FSPCA)] 121 | - [2023] Fast Unsupervised Feature Selection With Bipartite Graph and L2,0-Norm Constraint, IEEE TKDE [[Paper](https://ieeexplore.ieee.org/abstract/document/9695194)] 122 | - [2022] Learning Deep Sparse Regularizers With Applications to Multi-View Clustering and Semi-Supervised Classification, IEEE TPAMI [[Paper](https://ieeexplore.ieee.org/document/9439159)] [[Python](https://github.com/chenzl23/DSRL)] 123 | - [2022] Column L2,0-norm Regularized Factorization Model of Low-Rank Matrix Recovery and Its Computation, SIAM Journal on Optimization [[Paper](https://epubs.siam.org/doi/abs/10.1137/20M136205X)] 124 | - [2022] Unsupervised Feature Selection With Constrained l2,0-Norm and Optimized Graph, IEEE TNNLS [[Paper](https://ieeexplore.ieee.org/abstract/document/9309097)] 125 | - [2022] Low-Rank Tensor Learning with Nonconvex Overlapped Nuclear Norm Regularization, Journal of Machine Learning Research [[Paper](https://dl.acm.org/doi/abs/10.5555/3586589.3586725)] [[Matlab](https://github.com/quanmingyao/FasTer)] 126 | - [2019] Large-Scale Low-Rank Matrix Learning with Nonconvex Regularizers, IEEE TPAMI [[Paper](https://ieeexplore.ieee.org/abstract/document/8416722)] [[Matlab](https://github.com/quanmingyao/FaNCL)] 127 | - [2013] Exact Top-k Feature Selection via L2,0-norm Constraint, IJCAI [[Paper](https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=246737333a8e2e42b92be7a79f9508700b64c290)] [[Matlab](https://github.com/guijiejie/Feature-selection/tree/master/Feature%20selection%20based%20on%20structured%20sparsity/Exact%20top-k%20feature%20selection%20via%20l2%2C0-norm%20constraint)] 128 | 129 | 130 | ### One-Bit Compressive Sensing 131 | - [2024] OneBit: Towards Extremely Low-bit Large Language Models, arXiv [[Paper](https://arxiv.org/pdf/2402.11295.pdf)] 132 | - [2023] Sparse Logistic Regression-Based One-Bit SAR Imaging, IEEE TGRS [[Paper](https://ieeexplore.ieee.org/abstract/document/10273421)] 133 | - [2023] One-Bit Compressed Sensing via Total Variation Minimization Method, Signal Processing [[Paper](https://www.sciencedirect.com/science/article/abs/pii/S0165168423000130)] 134 | - [2023] 1-Bit Compressive Sensing for Efficient Federated Learning Over the Air, IEEE TWC [[Paper](https://ieeexplore.ieee.org/abstract/document/9912341)] 135 | - [2022] One-Bit Phase Retrieval: More Samples Means Less Complexity?, IEEE TSP [[Paper](https://ieeexplore.ieee.org/abstract/document/9896984)] 136 | - [2022] What Does a One-Bit Quanta Image Sensor Offer?, IEEE TCI [[Paper](https://ieeexplore.ieee.org/abstract/document/9868147)] 137 | - [2022] Computing One-Bit Compressive Sensing via Double-Sparsity Constrained Optimization, IEEE TSP [[Paper](https://ieeexplore.ieee.org/abstract/document/9729395)] [[Matlab](https://github.com/ShenglongZhou/GPSP)] 138 | - [2022] One-Bit Compressive Sensing: Can We Go Deep and Blind?, IEEE SPL [[Paper](https://ieeexplore.ieee.org/abstract/document/9812512)] 139 | - [2022] NBIHT: An Efficient Algorithm for 1-Bit Compressed Sensing With Optimal Error Decay Rate, IEEE ITI [[Paper](https://ieeexplore.ieee.org/abstract/document/9597562)] 140 | - [2021] One-Bit Tensor Completion via Transformed Tensor Singular Value Decomposition, Applied Mathematical Modelling [[Paper](https://www.sciencedirect.com/science/article/abs/pii/S0307904X21001165)] 141 | - [2021] Robust Recovery in 1-Bit Compressive Sensing via Lq-Constrained Least Squares, Signal Processing [[Paper](https://www.sciencedirect.com/science/article/abs/pii/S0165168420303662)] 142 | - [2020] Model-Based Deep Learning for One-Bit Compressive Sensing, IEEE TSP [[Paper](https://ieeexplore.ieee.org/abstract/document/9187438)] 143 | - [2020] One-Bit Supervision for Image Classification, NIPS [[Paper](https://proceedings.neurips.cc/paper/2020/hash/05f971b5ec196b8c65b75d2ef8267331-Abstract.html)] 144 | - [2020] Feature Selection and Classification of Noisy Proteomics Mass Spectrometry Data Based on One-Bit Perturbed 145 | Compressed Sensing, Bioinformatics [[Paper](https://academic.oup.com/bioinformatics/article/36/16/4423/5838182)] 146 | - [2019] One-Bit Compressive Sensing via Schur-Concave Function Minimization, IEEE TSP [[Paper](https://ieeexplore.ieee.org/abstract/document/8747470)] 147 | - [2019] Sparse Recovery and Dictionary Learning From Nonlinear Compressive Measurements, IEEE TSP [[Paper](https://ieeexplore.ieee.org/abstract/document/8834789)] 148 | - [2019] Computationally Efficient Sinusoidal Parameter Estimation From Signed Measurements: ADMM Approaches, IEEE SPL [[Paper](https://ieeexplore.ieee.org/abstract/document/8882283)] 149 | - [2019] Superset Technique for Approximate Recovery in One-Bit Compressed Sensing, NIPS [[Paper](https://proceedings.neurips.cc/paper_files/paper/2019/hash/c900ced7451da79502d29aa37ebb7b60-Abstract.html)] 150 | - [2018] Nonconvex Penalties with Analytical Solutions for One-Bit Compressive Sensing, Signal Processing [[Paper](https://www.sciencedirect.com/science/article/abs/pii/S0165168417303821)] 151 | - [2018] Robust Decoding from 1-Bit Compressive Sampling with Ordinary and Regularized Least Squares, SIAM Journal on Scientific Computing [[Paper](https://epubs.siam.org/doi/abs/10.1137/17M1154102)] 152 | - [2018] Pinball Loss Minimization for One-Bit Compressive Sensing: Convex Models and Algorithms, Neurocomputing [[Paper](https://www.sciencedirect.com/science/article/abs/pii/S0925231218308087)] 153 | - [2018] A Survey on One-Bit Compressed Sensing: Theory and Applications, Frontiers of Computer Science [[Paper](https://link.springer.com/article/10.1007/s11704-017-6132-7)] 154 | - [2016] One-Bit Compressive Sensing With Norm Estimation, IEEE TIT [[Paper](https://ieeexplore.ieee.org/abstract/document/7434599)] 155 | - [2016] Noisy 1-Bit Compressive Sensing: Models and Algorithms, Applied and Computational Harmonic Analysis [[Paper](https://www.sciencedirect.com/science/article/pii/S1063520314001419)] 156 | - [2015] Robust One-Bit Bayesian Compressed Sensing with Sign-Flip Errors, IEEE SPL [[Paper](https://ieeexplore.ieee.org/abstract/document/6963346)] 157 | - [2014] Efficient Algorithms for Robust One-Bit Compressive Sensing, ICML [[Paper](http://proceedings.mlr.press/v32/zhangc14.html)] 158 | - [2013] One-Bit Compressed Sensing by Linear Programming, Communications on Pure and Applied Mathematics [[Paper](https://onlinelibrary.wiley.com/doi/abs/10.1002/cpa.21442)] 159 | - [2013] One-Bit Compressed Sensing: Provable Support and Vector Recovery, ICML [[Paper](http://proceedings.mlr.press/v28/gopi13.html)] 160 | - [2013] Robust 1-Bit Compressed Sensing and Sparse Logistic Regression: A Convex Programming Approach, IEEE ITI [[Paper](https://ieeexplore.ieee.org/abstract/document/6294516)] 161 | - [2013] Robust 1-Bit Compressive Sensing via Binary Stable Embeddings of Sparse Vectors, IEEE TIT [[Paper](https://ieeexplore.ieee.org/abstract/document/6418031)] 162 | - [2012] Robust 1-bit Compressive Sensing Using Adaptive Outlier Pursuit, IEEE TSP [[Paper](https://ieeexplore.ieee.org/abstract/document/6178284)] 163 | - [2011] Trust, But Verify: Fast and Accurate Signal Recovery From 1-Bit Compressive Measurements, IEEE TSP [[Paper](https://ieeexplore.ieee.org/abstract/document/5955138)] 164 | - [2009] Greedy Sparse Signal Reconstruction from Sign Measurements, IEEE ACSSC [[Paper](https://ieeexplore.ieee.org/abstract/document/5469926)] 165 | - [2008] 1-Bit Compressive Sensing, IEEE CISS [[Paper](https://ieeexplore.ieee.org/abstract/document/4558487)] 166 | 167 | 168 | ### Object Tracking 169 | - [2023] Enhanced Robust Spatial Feature Selection and Correlation Filter Learning for UAV Tracking, Neural Networks [[Paper](https://www.sciencedirect.com/science/article/abs/pii/S0893608023000035)] [[Matlab](https://github.com/HonglinChu/EFSCF)] 170 | - [2022] Graph Moving Object Segmentation, IEEE TPAMI [[paper](https://ieeexplore.ieee.org/abstract/document/9288631)] [[Matlab](https://github.com/jhonygiraldo/GraphMOS)] 171 | - [2022] Federated Over-Air Subspace Tracking From Incomplete and Corrupted Data, IEEE TSP [[Paper](https://ieeexplore.ieee.org/abstract/document/9808342)] [[Matlab](https://github.com/andrewssobral/distributed-pca)] 172 | - [2020] Robust Structural Low-Rank Tracking, IEEE TIP [[paper](https://ieeexplore.ieee.org/abstract/document/8995776)] 173 | - [2020] Plug-and-Play Algorithms for Large-Scale Snapshot Compressive Imaging, CVPR [[Paper](https://ieeexplore.ieee.org/document/9156491)] [[Matlab](https://github.com/liuyang12/PnP-SCI)] 174 | - [2019] Low-rank Tensor Tracking, ICCV [[Paper](https://openaccess.thecvf.com/content_ICCVW_2019/papers/RSL-CV/javed_Low-Rank_Tensor_Tracking_ICCVW_2019_paper.pdf)] 175 | - [2015] Robust Visual Tracking Via Consistent Low-Rank Sparse Learning, International Journal of Computer Vision [[Paper](https://nlpr.ia.ac.cn/mmc/homepage/tzzhang/tianzhu%20zhang_files/Journal%20Articles/IJCV15_zhang_Low-Rank%20Sparse%20Learning.pdf)] [[Matlab](https://nlpr.ia.ac.cn/mmc/homepage/tzzhang/Project_Tianzhu/zhang_IJCV14/Robust%20Visual%20Tracking%20Via%20Consistent%20Low-Rank%20Sparse.html)] 176 | 177 | 178 | 179 | 180 | ## Links 181 | 182 | ### Journals 183 | - Mathematical Programming [[Link](https://www.springer.com/journal/10107/)] 184 | - SIAM Journal on Optimization [[Link](https://www.siam.org/publications/journals/siam-journal-on-optimization-siopt)] 185 | - Mathematics of Operations Research [[Link](http://mor.journal.informs.org/)] 186 | 187 | ### Tools 188 | - TFOCS: Templates for First-Order Conic Solvers [[Link](http://cvxr.com/tfocs/)] 189 | - Scikit-feature: 40 Popular Feature Selection Algorithms With 25+ Datasets [[Link](https://github.com/jundongl/scikit-feature)] 190 | 191 | --------------------------------------------------------------------------------