Relax and Follow: L0-Path Computation with L0-Bregman Relaxations | Paper Citation Record

Observation bdf44a4a-7245-4a33-9b45-5e2bac69190e · outbound

This paper cites Notterman, Kenneth W.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations Notterman, Kenneth W

Reference 1

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Observation 93dc04ee-d87c-4cd0-ada5-ac41bc0ff728 · outbound

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Relax and Follow: L0-Path Computation with L0-Bregman Relaxations Unresolved cited work

Reference 2

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Observation f9f5c7f9-449b-48f2-9df4-171aaf56f54f · outbound

This paper cites A fast iterative shrinkage-thresholding algorithm for linear inverse problems.SIAM journal on imaging sciences, 2(1):183–202, 2009.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations A fast iterative shrinkage-thresholding algorithm for linear inverse problems.SIAM journal on imaging sciences, 2(1):183–202, 2009

Reference 3

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Observation 1f167024-7b0e-421e-9938-abd85ea708d7 · outbound

This paper cites Beyond l1: Faster and better sparse models with skglm.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations Beyond l1: Faster and better sparse models with skglm

Reference 4

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Observation ce2e8a57-8c13-472d-8e39-6a575d73569a · outbound

This paper cites Best subset selection via a modern optimization lens.The Annals of Statistics, 44(2):813, 2016.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations Best subset selection via a modern optimization lens.The Annals of Statistics, 44(2):813, 2016

Reference 5

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Observation 41e55186-7301-458f-bbc8-2acaa74fe033 · outbound

This paper cites A smoothing proximal gradient algorithm for nonsmooth convex regression with cardinality penalty.SIAM Journal on Numerical Analysis, 58(1): 858–883, 2020.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations A smoothing proximal gradient algorithm for nonsmooth convex regression with cardinality penalty.SIAM Journal on Numerical Analysis, 58(1): 858–883, 2020

Reference 6

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Observation e85bc094-51f2-4d4e-836d-3458a8615c34 · outbound

This paper cites Exact sparse approximation problems via mixed-integer programming: Formulations and computational performance.IEEE Transactions on Signal Processing, 64(6):1405–1419, 2016.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations Exact sparse approximation problems via mixed-integer programming: Formulations and computational performance.IEEE Transactions on Signal Processing, 64(6):1405–1419, 2016

Reference 7

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Observation 74f448a0-38a3-45b7-a554-5c63938f25c9 · outbound

This paper cites an unresolved cited work.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations Unresolved cited work

Reference 8

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Observation f04dd795-d310-4072-b473-25c63dbd6015 · outbound

This paper cites Coordinate descent algorithms for nonconvex penalized regression, with applications to biological feature selection.Annals of Applied Statistics, 5 (1):232–253, 2011.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations Coordinate descent algorithms for nonconvex penalized regression, with applications to biological feature selection.Annals of Applied Statistics, 5 (1):232–253, 2011

Reference 9

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Observation f387fbf9-8361-4933-a163-825ca24181c9 · outbound

This paper cites High-dimensional statistics with a view toward applications in biology.Annual Review of Statistics and Its Application, 1:255–278, 01 2014.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations High-dimensional statistics with a view toward applications in biology.Annual Review of Statistics and Its Application, 1:255–278, 01 2014

Reference 10

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Observation e49eb8f2-2ba2-4559-ae96-1e89b16bce2a · outbound

This paper cites Candes, J.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations Candes, J

Reference 11

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Observation d55c009e-9872-4dfb-9ad1-6a0b53fdb234 · outbound

This paper cites Candès, Michael B.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations Candès, Michael B

Reference 12

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Observation b9e8946e-7461-4214-8c5e-7937c58b1085 · outbound

This paper cites On convex envelopes and regularization of non-convex functionals without moving global minima.Journal of Optimization Theory and Applications, 183(1):66–84, 2019.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations On convex envelopes and regularization of non-convex functionals without moving global minima.Journal of Optimization Theory and Applications, 183(1):66–84, 2019

Reference 13

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Observation 8c35c776-e2c4-4e19-8c61-c75c75eb2347 · outbound

This paper cites An unbiased approach to compressed sensing.Inverse Problems, 36(11):115014, 2020.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations An unbiased approach to compressed sensing.Inverse Problems, 36(11):115014, 2020

Reference 14

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Observation 0a92556d-b6d8-4dbf-8aca-982659ceab42 · outbound

This paper cites Spoqℓp- over-ℓq regularization for sparse signal recovery applied to mass spectrometry.IEEE Trans- actions on Signal Processing, 68:6070–6084, 2020.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations Spoqℓp- over-ℓq regularization for sparse signal recovery applied to mass spectrometry.IEEE Trans- actions on Signal Processing, 68:6070–6084, 2020

Reference 15

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Observation abfa8c93-ca9e-4930-9ae2-3195e059aab9 · outbound

This paper cites Optimization land- scape of l0-bregman relaxations.arXiv preprint arXiv:2511.12157, 2025.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations Optimization land- scape of l0-bregman relaxations.arXiv preprint arXiv:2511.12157, 2025

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-07-02T00:06:24.105082Z

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Observation c8cef650-aaa9-4d3a-8926-2e148e7fc13d · outbound

This paper cites A block coordinate variable metric forward–backward algorithm.Journal of Global Optimization, 66(3):457– 485, 2016.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations A block coordinate variable metric forward–backward algorithm.Journal of Global Optimization, 66(3):457– 485, 2016

Reference 17

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Observation 521e5031-00f0-4b62-9a11-c96b263c431d · outbound

This paper cites Proximal splitting methods in signal processing.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations Proximal splitting methods in signal processing

Reference 18

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Observation aa327972-6524-4e12-84d7-2cdf9382aa15 · outbound

This paper cites Learning sparse classifiers: Con- tinuous and mixed integer optimization perspectives.Journal of Machine Learning Re- search, 2021.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations Learning sparse classifiers: Con- tinuous and mixed integer optimization perspectives.Journal of Machine Learning Re- search, 2021

Reference 19

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Observation 1a2b6538-9661-4802-a548-86a59b2d5328 · outbound

This paper cites Anovelintegerlinearprogramming approach for globalℓ0 minimization.Journal of Machine Learning Research, 24(382):1–28, 2023.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations Anovelintegerlinearprogramming approach for globalℓ0 minimization.Journal of Machine Learning Research, 24(382):1–28, 2023

Reference 20

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Observation a57fe47c-544d-4624-b66b-a8a7b732567a · outbound

This paper cites Least angle regres- sion.The Annals of statistics, 32(2):407–451, 2004.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations Least angle regres- sion.The Annals of statistics, 32(2):407–451, 2004

Reference 21

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Observation ba6c87ce-4cb4-4943-87c1-3c856d42f6f7 · outbound

This paper cites Essafri, L.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations Essafri, L

Reference 22

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Observation dc7cf454-1155-4ecc-b5ee-c0dcf877ddbf · outbound

This paper cites Onℓ0-bregman-relaxations for kullback-leibler sparse regression.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations Onℓ0-bregman-relaxations for kullback-leibler sparse regression

Reference 23

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Observation d3d6e7a8-df6c-4ed1-83b0-94e43e01c771 · outbound

This paper cites Variable selection via nonconcave penalized likelihood and its oracle properties.Journal of the American Statistical Association, 96:1348 – 1360, 2001.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations Variable selection via nonconcave penalized likelihood and its oracle properties.Journal of the American Statistical Association, 96:1348 – 1360, 2001

Reference 24

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Observation 025a06e6-58a9-4865-8c45-00bf00b306ca · outbound

This paper cites A paraboloidal surrogates algorithm for convergent penalized-likelihood emission image reconstruction.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations A paraboloidal surrogates algorithm for convergent penalized-likelihood emission image reconstruction

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Observation 82c91973-7259-4780-b75a-1f1fb329d1d1 · outbound

This paper cites Sparsest solutions of underdetermined linear systems via ℓq-minimization for0< q≤1.Applied and Computational Harmonic Analysis, 26:395–407, 2009.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations Sparsest solutions of underdetermined linear systems via ℓq-minimization for0< q≤1.Applied and Computational Harmonic Analysis, 26:395–407, 2009

Reference 26

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Observation 3369f0c0-f853-44c3-bfbe-9ea00529d382 · outbound

This paper cites Perspective cuts for a class of convex 0–1 mixed integer programs.Mathematical Programming, 106:225–236, 2006.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations Perspective cuts for a class of convex 0–1 mixed integer programs.Mathematical Programming, 106:225–236, 2006

Reference 27

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Observation 3b9661fc-c0ed-4a62-ac43-db0f52d79d01 · outbound

This paper cites Pathwise coordi- nate optimization.The Annals of Applied Statistics, 1(2):302–332, 2007.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations Pathwise coordi- nate optimization.The Annals of Applied Statistics, 1(2):302–332, 2007

Reference 28

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Observation 1b1ca9bb-1896-474b-a80b-f129be9efc55 · outbound

This paper cites Regularization paths for gen- eralized linear models via coordinate descent.Journal of Statistical Software, 33(1):1–22, 2010.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations Regularization paths for gen- eralized linear models via coordinate descent.Journal of Statistical Software, 33(1):1–22, 2010

Reference 29

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Observation 968f9e9a-df84-4c7d-acd5-5471a008efa6 · outbound

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Relax and Follow: L0-Path Computation with L0-Bregman Relaxations Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.science, 286(5439):531–537, 1999

Reference 30

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Observation ed3f185f-d4b0-41f9-bbce-3d579ed0dc47 · outbound

This paper cites Perspective reformulations of mixed integer nonlinear programs with indicator variables.Mathematical programming, 124:183–205, 2010.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations Perspective reformulations of mixed integer nonlinear programs with indicator variables.Mathematical programming, 124:183–205, 2010

Reference 31

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Observation 8a9e4ca7-05a2-49ee-8a1a-cc53be292040 · outbound

This paper cites A new branch-and- bound pruning framework for l0-regularized problems.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations A new branch-and- bound pruning framework for l0-regularized problems

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Observation 6bf87033-cecd-45c2-8b8a-931a99f2180b · outbound

This paper cites El0ps: An Exact L0-regularized Problems Solver.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations El0ps: An Exact L0-regularized Problems Solver

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Resolution: verified exact; arxiv_id, observed 2026-07-02T00:06:24.101765Z

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Observation 06f9f9c6-f8c2-4ccf-8f8e-23c69ae8414b · outbound

This paper cites Fast best subset selection: Coordinate descent and local combinatorial optimization algorithms.Operations Research, 68(5):1517–1537, 2020.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations Fast best subset selection: Coordinate descent and local combinatorial optimization algorithms.Operations Research, 68(5):1517–1537, 2020

Reference 34

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Observation 2b0ce797-d009-42db-9912-a50cfe09b9f6 · outbound

This paper cites L0learn: A scalable package for sparse learning using l0 regularization.Journal of Machine Learning Research, 24(205):1–8, 2023.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations L0learn: A scalable package for sparse learning using l0 regularization.Journal of Machine Learning Research, 24(205):1–8, 2023

Reference 35

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Observation cd5e11f8-6f96-4e2e-96e5-378a76eb00ea · outbound

This paper cites Dc approximation approaches for sparse optimization.European Journal of Operational Research, 244(1): 26–46, 2015.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations Dc approximation approaches for sparse optimization.European Journal of Operational Research, 244(1): 26–46, 2015

Reference 36

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Observation ca4000f7-0bf0-40a2-8eeb-76bb91c190bf · outbound

This paper cites Mallat and Zhifeng Zhang.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations Mallat and Zhifeng Zhang

Reference 37

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Observation 897a1e13-8125-48fc-8a44-f9fc39c2d934 · outbound

This paper cites L Mangasarian.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations L Mangasarian

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Observation 5c034c04-e197-45c4-a838-a971ff4de161 · outbound

This paper cites Sparsenet: Coordinate descent with nonconvex penalties.Journal of the American Statistical Association, 106:1125–1138, 01 2010.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations Sparsenet: Coordinate descent with nonconvex penalties.Journal of the American Statistical Association, 106:1125–1138, 01 2010

Reference 39

Resolution: verified exact; doi, observed 2026-06-28T15:12:18.800367Z

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Observation 4f3afa26-f60e-4c65-90e7-fa127958a0d5 · outbound

This paper cites A fast approach for overcomplete sparse decomposition based on smoothedℓ 0 norm.IEEE Transactions on Signal Processing, 57:289–301, 2008.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations A fast approach for overcomplete sparse decomposition based on smoothedℓ 0 norm.IEEE Transactions on Signal Processing, 57:289–301, 2008

Reference 40

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Observation 65fbc51c-bd73-4422-b19b-1055583bea19 · outbound

This paper cites Natarajan.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations Natarajan

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Observation 413f1900-c9ef-411c-afae-62052e3573e9 · outbound

This paper cites Gradient methods for minimizing composite functions.Mathematical pro- gramming, 140(1):125–161, 2013.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations Gradient methods for minimizing composite functions.Mathematical pro- gramming, 140(1):125–161, 2013

Reference 42

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Observation 0592c879-4565-40d0-b5e5-8122b2a728f3 · outbound

This paper cites Nguyen, Charles Soussen, Jérôme Idier, and El-Hadi Djermoune.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations Nguyen, Charles Soussen, Jérôme Idier, and El-Hadi Djermoune

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Observation 59d68779-9363-4b4c-aee9-b1c0a9d7150c · outbound

This paper cites Description of the minimizers of least squares regularized withℓ0-norm.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations Description of the minimizers of least squares regularized withℓ0-norm

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Observation a7f745ad-d6fe-43e4-8d2d-991f4180b831 · outbound

This paper cites On iteratively reweighted algorithms for nonsmooth nonconvex optimization in computer vision.SIAM Journal on Imaging Sciences, 8(3):331–372, 2015.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations On iteratively reweighted algorithms for nonsmooth nonconvex optimization in computer vision.SIAM Journal on Imaging Sciences, 8(3):331–372, 2015

Reference 45

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Observation 76349fea-2473-4093-8b50-4b11bfc3c8a6 · outbound

This paper cites Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition

Reference 46

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Observation 38fdece3-5883-4d87-8ecd-b4496c4b26c1 · outbound

This paper cites Pilanci, M.J.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations Pilanci, M.J

Reference 47

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Observation b0ee8fca-de3b-47a4-95ac-adf72d928c51 · outbound

This paper cites Euclid in a taxicab: Sparse blind deconvolution with smoothedℓ1/ℓ2 regularization.IEEE Signal Processing Letters, 22(5):539–543, 2015.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations Euclid in a taxicab: Sparse blind deconvolution with smoothedℓ1/ℓ2 regularization.IEEE Signal Processing Letters, 22(5):539–543, 2015

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Observation 4223279a-0af3-497c-8cc9-292985c5cf33 · outbound

This paper cites Systematic variation in gene expression patterns in human cancer cell lines.Nature genetics, 24(3):227–235, 2000.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations Systematic variation in gene expression patterns in human cancer cell lines.Nature genetics, 24(3):227–235, 2000

Reference 49

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Observation 1f841b9b-b600-41b2-a50d-912c3b49ed56 · outbound

This paper cites Piecewise linear regularized solution paths.The Annals of Statistics, 35(3):1012–1030, 2007.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations Piecewise linear regularized solution paths.The Annals of Statistics, 35(3):1012–1030, 2007

Reference 50

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Observation 6a016c7e-41c7-4c1f-9ff1-f8d1ca46cb3e · outbound

This paper cites A continuous exactℓ0 penalty (CEL0) for least squares regularized problem.SIAM Journal on Imaging Sciences, 8(3): 1607–1639, 2015.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations A continuous exactℓ0 penalty (CEL0) for least squares regularized problem.SIAM Journal on Imaging Sciences, 8(3): 1607–1639, 2015

Reference 51

Resolution: unresolved; no resolver link, observed 2026-06-28T13:36:45.131766Z

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Observation ca2ca20a-758f-44f3-becd-69191d62354c · outbound

This paper cites A unified view of exact con- tinuous penalties forℓ2-ℓ0 minimization.SIAM Journal on Optimization, 27(3):2034–2060, 2017.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations A unified view of exact con- tinuous penalties forℓ2-ℓ0 minimization.SIAM Journal on Optimization, 27(3):2034–2060, 2017

Reference 52

Resolution: unresolved; no resolver link, observed 2026-06-28T13:36:45.131766Z

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Observation 8b378755-d4dc-457c-b6e0-398ef153e549 · outbound

This paper cites From bernoulli–gaussian deconvolution to sparse signal restoration.IEEE Transactions on Signal Processing, 59 (10):4572–4584, 2011.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations From bernoulli–gaussian deconvolution to sparse signal restoration.IEEE Transactions on Signal Processing, 59 (10):4572–4584, 2011

Reference 53

Resolution: unresolved; no resolver link, observed 2026-06-28T13:36:45.131766Z

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Observation 83f322a6-208a-4718-8de5-cce972f067ee · outbound

This paper cites an unresolved cited work.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations Unresolved cited work

Reference 54

Resolution: unresolved; no resolver link, observed 2026-06-28T13:36:45.131766Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c1a57735-4c78-4307-a35d-dd7d4adaa420 · outbound

This paper cites Regression shrinkage and selection via the LASSO.Journal of the Royal Statistical Society, 58(1):267–288, 1996.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations Regression shrinkage and selection via the LASSO.Journal of the Royal Statistical Society, 58(1):267–288, 1996

Reference 55

Resolution: unresolved; no resolver link, observed 2026-06-28T13:36:45.131766Z

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Observation 3c50515e-3900-4433-bf7e-2c2a47291338 · outbound

This paper cites Cardinality minimization, constraints, and regularization: a survey.SIAM Review, 66(3):403–477, 2024.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations Cardinality minimization, constraints, and regularization: a survey.SIAM Review, 66(3):403–477, 2024

Reference 56

Resolution: unresolved; no resolver link, observed 2026-06-28T13:36:45.131766Z

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Observation 99ee88cf-af4d-4e8c-b078-640abc0c0c98 · outbound

This paper cites an unresolved cited work.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations Unresolved cited work

Reference 57

Resolution: unresolved; no resolver link, observed 2026-06-28T13:36:45.131766Z

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Observation 39397c6e-4581-4c57-997c-3cf0621c982f · outbound

This paper cites Nearly unbiased variable selection under minimax concave penalty.The Annals of Statistics, 38(2):894 – 942, 2010.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations Nearly unbiased variable selection under minimax concave penalty.The Annals of Statistics, 38(2):894 – 942, 2010

Reference 58

Resolution: unresolved; no resolver link, observed 2026-06-28T13:36:45.131766Z

Source-reported events for the cited work

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Observation 69c55b56-d387-46fa-a850-d05524f4901e · outbound

This paper cites Multi-stage convex relaxation for learning with sparse regularization.

Relax and Follow: L0-Path Computation with L0-Bregman Relaxations Multi-stage convex relaxation for learning with sparse regularization

Reference 59

Resolution: unresolved; no resolver link, observed 2026-06-28T13:36:45.131766Z

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