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A Fast Algorithm for Separated Sparsity via Perturbed Lagrangians
[article]
2017
arXiv
pre-print
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While prior algorithms for computing a projection onto this constraint set required quadratic time, we provide a perturbed Lagrangian relaxation approach that computes provably exact projection in only ...
Although the sparsity constraint is non-convex, our perturbed Lagrangian approach is still guaranteed to find a globally optimal solution. ...
We thank Arturs Backurs for insightful discussions. L. Schmidt thanks Chinmay Hegde for providing a dataset for some of our experiments. A. Mądry was supported in part by an Alfred. P. ...
arXiv:1712.08130v1
fatcat:gq2klxmtujd5dpq35j4uz7i3ie
Sparsity-Cognizant Total Least-Squares for Perturbed Compressive Sampling
2011
IEEE Transactions on Signal Processing
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Near-optimum and reduced-complexity suboptimum sparse (S-) TLS algorithms are developed to address the perturbed compressive sampling (and the related dictionary learning) challenge, when there is a mismatch ...
However, existing TLS approaches do not account for sparsity possibly present in the unknown vector of regression coefficients. ...
The -norm based formulation accounting for the said perturbations is known as basis pursuit (BP) [11] , and the corresponding convex problem written in its Lagrangian form is: , where is a sparsity-tuning ...
doi:10.1109/tsp.2011.2109956
fatcat:4uwyrlh26bhqlduppj2roibaqi
A Complete Analysis of the l_1,p Group-Lasso
[article]
2012
arXiv
pre-print
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For all p-norms, a highly efficient projected gradient algorithm is presented. ...
We characterize conditions for solutions of the l_1,p Group-Lasso for all p-norms with 1 <= p <= ∞, and we present a unified active set algorithm. ...
Adapt Lagrangian multiplier µ via interval bisection. ...
arXiv:1206.4632v1
fatcat:hd7ftrc7fvezxhg6g75adrvpq4
Fast and Stable Signal Deconvolution via Compressible State-Space Models
2018
IEEE Transactions on Biomedical Engineering
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Under suitable sparsity assumptions on the dynamics, we prove optimal stability guarantees for the recovery of the states and present a method for the identification of the underlying discrete events with ...
We consider a dynamic compressive sensing optimization problem and develop a fast solution, using two nested Expectation Maximization algorithms, to jointly estimate the states as well as their transition ...
The Lagrangian formulation allows us to adaptively select the sparsity level in a data-driven fashion: by choosing the trade-off parameter λ via cross-validation, the sparsity level of the reconstructed ...
doi:10.1109/tbme.2017.2694339
pmid:28422648
pmcid:PMC5683949
fatcat:jltmxhudyncfnfuzcelvqyj3pe
Sparsity Meets Robustness: Channel Pruning for the Feynman-Kac Formalism Principled Robust Deep Neural Nets
[article]
2020
arXiv
pre-print
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In this paper, we focus on a co-design of efficient DNN compression algorithms and sparse neural architectures for robust and accurate deep learning. ...
Such a co-design enables us to advance the goal of accommodating both sparsity and robustness. ...
For all the experiments below, we run 200 epochs of the PGD (10 iterations of the iterative fast gradient sign method (IFGSM 10 ) with α = 2/255 and = 8/255, and an initial random perturbation of magnitude ...
arXiv:2003.00631v1
fatcat:fxqyasu6fbf6haseckqbdbgidm
FeaFiner
2013
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '13
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We apply a recently developed augmented Lagrangian method to solve this formulation in which each subproblem is solved by a non-monotone spectral projected gradient method. ...
Specifically, we formulate a double sparsity optimization problem that identifies groups in the low-level features, generalizes higher level features using the groups and performs feature selection. ...
Specifically, we propose a formulation for learning a sparse group structure matrix and a sparse prediction model via ℓ1-regularization, and adopt an efficient block coordinate descent algorithm for solving ...
doi:10.1145/2487575.2487671
dblp:conf/kdd/ZhouLSYWY13
fatcat:hjg2toiyqnhmfldusltwqnwwuu
Background Subtraction via Fast Robust Matrix Completion
2017
2017 IEEE International Conference on Computer Vision Workshops (ICCVW)
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In modeling the background, we benefited from the in-face extended Frank-Wolfe algorithm for solving a defined convex optimization problem. ...
In this regard, our paper addresses the problem of background modeling in a computationally efficient way, which is important for current eruption of "big data" processing coming from high resolution multi-channel ...
Inexact augmented Lagrangian multiplier (IALM) is a successful effort in this regard for solving the problem of RPCA based on the augmented Lagrangian multiplier (ALM) algorithm without taking the unnecessary ...
doi:10.1109/iccvw.2017.221
dblp:conf/iccvw/RezaeiO17
fatcat:srgo64qnurdwta7hvm6topemvu
Background Subtraction via Fast Robust Matrix Completion
[article]
2017
arXiv
pre-print
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In modeling the background, we benefited from the in-face extended Frank-Wolfe algorithm for solving a defined convex optimization problem. ...
In this regard, our paper addresses the problem of background modeling in a computationally efficient way, which is important for current eruption of "big data" processing coming from high resolution multi-channel ...
Inexact augmented Lagrangian multiplier (IALM) is a successful effort in this regard for solving the problem of RPCA based on the augmented Lagrangian multiplier (ALM) algorithm without taking the unnecessary ...
arXiv:1711.01218v1
fatcat:fh4rdps2yze67frmzphdzs6xnu
Group sparse Lasso for cognitive network sensing robust to model uncertainties and outliers
2012
Physical Communication
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The sensing scheme is based on a parsimonious model that accounts for two forms of sparsity: one due to the narrowband nature of transmit-PSDs compared to the large portion of spectrum that a CR can sense ...
To account for variations in the frequency, time, and space dimensions, dynamic re-use of licensed bands under the cognitive radio (CR) paradigm calls for innovative network-level sensing algorithms for ...
A centralized algorithm for solving GS-Lasso problems is developed in Section 3, whereas perturbations in the channel (regression) matrices are considered in Section 4. ...
doi:10.1016/j.phycom.2011.07.005
fatcat:kjrgye7eirfobajjnt3bfj7bfa
Fast L1-Minimization Algorithms For Robust Face Recognition
[article]
2012
arXiv
pre-print
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Although the underlying numerical problem is a linear program, traditional algorithms are known to suffer poor scalability for large-scale applications. ...
We investigate a new solution based on a classical convex optimization framework, known as Augmented Lagrangian Methods (ALM). ...
The accuracy for the remaining five algorithm is separated in three groups. ...
arXiv:1007.3753v4
fatcat:wawm3rqhdjfgndgm7uuvqhk6le
Decomposition into low-rank plus additive matrices for background/foreground separation: A review for a comparative evaluation with a large-scale dataset
2017
Computer Science Review
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2015 pre-print arXiv:1511.01245v2 fatcat:ce57uuiurnbipdimhi4ofsaereOther Versions
matrices for testing and ranking existing algorithms for background/foreground separation. ...
Furthermore, we investigate if incremental algorithms and real-time implementations can be achieved for background/foreground separation. ...
thank the following researchers: Zhouchen Lin (Visual Computing Group, Microsoft Research Asia) who has kindly provided the solver LADMAP [192] and the l 1 -filtering [196] , Shiqian Ma (Institute for ...
doi:10.1016/j.cosrev.2016.11.001
fatcat:vdh7ic4n6zfkjlccnyiq74z5wu
A Highly Efficient Semismooth Newton Augmented Lagrangian Method for Solving Lasso Problems
2018
SIAM Journal on Optimization
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We develop a fast and robust algorithm for solving large scale convex composite optimization models with an emphasis on the 1 -regularized least squares regression (Lasso) problems. ...
By leveraging on available error bound results to realize the asymptotic superlinear convergence property of the augmented Lagrangian algorithm, and by exploiting the second order sparsity of the problem ...
Chao Ding at Chinese Academy of Sciences for numerous discussions on the error bound conditions and the metric subregularity. ...
doi:10.1137/16m1097572
fatcat:is4qdjpvpngh3mebh3oq5cm424
A highly efficient semismooth Newton augmented Lagrangian method for solving Lasso problems
[article]
2017
arXiv
pre-print
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We develop a fast and robust algorithm for solving large scale convex composite optimization models with an emphasis on the ℓ_1-regularized least squares regression (Lasso) problems. ...
By leveraging on available error bound results to realize the asymptotic superlinear convergence property of the augmented Lagrangian algorithm, and by exploiting the second order sparsity of the problem ...
Acknowledgments The authors would like to thank the anonymous referees for carefully reading our work and for their helpful suggestions. The authors would also like to thank Dr. ...
arXiv:1607.05428v3
fatcat:5qiersdtarcjpg5jznjse6piau
Masked-RPCA: Sparse and Low-rank Decomposition Under Overlaying Model and Application to Moving Object Detection
[article]
2019
arXiv
pre-print
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We propose the representation via masked decomposition (i.e. an overlaying model) where each element either belongs to the low-rank or the sparse component, decided by a mask. ...
We propose the Masked-RPCA algorithm to recover the mask and the low-rank components simultaneously, utilizing linearizing and alternating direction techniques. ...
Imposing the sparsity of desired W via 1 -norm we can formulate the problem as in (3) . minimize L,W L * +λ w W 1 subject to: (1 − W ) • (X − L) = 0 W ∈ [0, 1] mn×k (3) The algorithm for solving the problem ...
arXiv:1909.08049v1
fatcat:r4ctf4dpvzgihmqzkdj7ut36xu
Fused analytical and iterative reconstruction (AIR) via modified proximal forward–backward splitting: a FDK-based iterative image reconstruction example for CBCT
2016
Physics in Medicine and Biology
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2015 pre-print arXiv:1508.04687v1 fatcat:2fkeokvtonazddao4ltpbklgreOther Versions
Intuitively since the eigenvalues of AR-projection operator are close to the unity, PFBS based AIR has a fast convergence. ...
Specifically, AIR is established based on the modified proximal forward-backward splitting (PFBS) algorithm, and its connection to the filtered data fidelity with sparsity regularization is discussed. ...
Yang-Kyun Park from Massachusetts General Hospital for projection data and inspiring discussions. ...
doi:10.1088/0031-9155/61/19/7187
pmid:27649259
fatcat:rg7bd4t46zeqdgi7mk4rukxbli
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