Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography | Paper Citation Record

Observation 6769fac1-0473-4078-a7c5-8574aee7b276 · outbound

This paper cites Acar and C.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Acar and C

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.188629Z

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This paper cites Alter, V.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Alter, V

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.263157Z

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This paper cites Oxford Mathematical Monographs.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Oxford Mathematical Monographs

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.190180Z

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This paper cites Andreu, C.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Andreu, C

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.162675Z

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Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Unresolved cited work

Reference 5

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This paper cites Barzilai and J.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Barzilai and J

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.240386Z

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Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Unresolved cited work

Reference 7

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Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Unresolved cited work

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.198439Z

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This paper cites Beck.First-Order Methods in Optimization.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Beck.First-Order Methods in Optimization

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.178719Z

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This paper cites Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems.IEEE Trans.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems.IEEE Trans

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.203941Z

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Observation 1ca313c7-5e5f-419a-bbb7-3a98a0431916 · outbound

This paper cites A fast iterative shrinkage-thresholding algorithm for linear inverse problems.SIAM J.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography A fast iterative shrinkage-thresholding algorithm for linear inverse problems.SIAM J

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.211691Z

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Observation 8fa0c05e-0f92-4797-acbc-06ba2ab87604 · outbound

This paper cites Distributed op- timization and statistical learning via the alternating direction method of multipliers.Found.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Distributed op- timization and statistical learning via the alternating direction method of multipliers.Found

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.286099Z

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This paper cites Bredies and M.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Bredies and M

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.238038Z

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Observation 137c90fe-d8c4-41e9-b4d1-42529faecd81 · outbound

This paper cites Sparsity of solutions for variational inverse problems with finite-dimensional data.Calc.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Sparsity of solutions for variational inverse problems with finite-dimensional data.Calc

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.182976Z

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Observation 9f231b84-a300-4155-8a3f-7f231c57904b · outbound

This paper cites Regularization of linear inverse problems with total general- ized variation.J.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Regularization of linear inverse problems with total general- ized variation.J

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.191908Z

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Observation 4c353682-1cd4-4c59-8483-c2b6469e65f0 · outbound

This paper cites Convergence rates of convex variational regularization.Inverse Problems, 20(5):1411–1421.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Convergence rates of convex variational regularization.Inverse Problems, 20(5):1411–1421

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.260777Z

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Observation 3b77a941-ebff-4191-941c-04c6ecb1d0d1 · outbound

This paper cites An algorithm for total variation minimization and applications.J.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography An algorithm for total variation minimization and applications.J

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.276814Z

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Observation dbf2cc49-8383-4fcd-9546-7f5079c23ce7 · outbound

This paper cites An introduction to total variation for image analysis.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography An introduction to total variation for image analysis

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.248951Z

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Observation bc5c6074-6f27-45b1-8f2b-482a3f9b47d2 · outbound

This paper cites Image recovery via total variation minimization and related problems.Numer.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Image recovery via total variation minimization and related problems.Numer

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.184863Z

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Observation 6f7e8baa-2797-4769-aada-70d41f11e37a · outbound

This paper cites A first-order primal-dual algorithm for convex problems with applications to imaging.J.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography A first-order primal-dual algorithm for convex problems with applications to imaging.J

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.148223Z

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Observation 8d92649f-dbb3-47f2-aefc-3b05a368d2cb · outbound

This paper cites Chan and Selim Esedo¯ glu.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Chan and Selim Esedo¯ glu

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.153952Z

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Observation b5df2d65-4e4d-4264-8104-a775868b74c2 · outbound

This paper cites Chan, Gene H.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Chan, Gene H

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.176695Z

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Observation a845b25d-4cfb-4cb2-9d39-d4a96c93796f · outbound

This paper cites Iglesias, and Daniel Walter.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Iglesias, and Daniel Walter

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.150290Z

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Observation 12a2ca64-535d-4982-ba7c-66bb5524d060 · outbound

This paper cites Ellwood, O.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Ellwood, O

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.152167Z

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Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Unresolved cited work

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.146280Z

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Observation 60d35946-551c-4ace-ad1c-57b3850a89b1 · outbound

This paper cites Berichte aus der Mathematik.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Berichte aus der Mathematik

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.159494Z

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Observation 402e18a1-d254-46dd-afe7-003b3890db0d · outbound

This paper cites A new approach to source conditions in regularization with general residual term.Numer.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography A new approach to source conditions in regularization with general residual term.Numer

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.155902Z

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Observation 7a03d8b2-63a9-4dbb-8338-85e912a0f06f · outbound

This paper cites Convergence rates in constrained Tikhonov regulariza- tion: equivalence of projected source conditions and variational inequalities.Inverse Problems, 27(8):085001, 11.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Convergence rates in constrained Tikhonov regulariza- tion: equivalence of projected source conditions and variational inequalities.Inverse Problems, 27(8):085001, 11

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.166202Z

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Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Unresolved cited work

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.233082Z

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Observation 1e10b5f7-2371-4719-bb7d-8b69327f2ec0 · outbound

This paper cites Gfrerer, S.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Gfrerer, S

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.265225Z

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Observation bab25551-d9ec-4eaf-9ecf-6f5c1640b1ca · outbound

This paper cites Gfrerer and J.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Gfrerer and J

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.267365Z

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Observation 79a00f27-f7ec-4795-a593-e8ecfc1d8e99 · outbound

This paper cites Gfrerer and J.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Gfrerer and J

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.274100Z

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Observation 5fb81e2f-c859-42f4-8e9b-35f3f7d9611b · outbound

This paper cites Nonlocal operators with applications to image processing.Mul- tiscale Model.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Nonlocal operators with applications to image processing.Mul- tiscale Model

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.284052Z

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Observation 2da61828-78a8-4b6a-8d86-6334f4aef21c · outbound

This paper cites The split Bregman method forL1-regularized problems.SIAM J.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography The split Bregman method forL1-regularized problems.SIAM J

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.253792Z

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Observation e72cd880-bd92-41a5-b70d-c4a67a86ce2a · outbound

This paper cites Generalized Bregman distances and convergence rates for non-convex regular- ization methods.Inverse Problems, 26(11):115014, 16.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Generalized Bregman distances and convergence rates for non-convex regular- ization methods.Inverse Problems, 26(11):115014, 16

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.258637Z

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Observation f80ab2f5-f01e-45fe-b7c4-07b4d3a574da · outbound

This paper cites Variational inequalities and higher order convergence rates for Tikhonov reg- ularisation on Banach spaces.J.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Variational inequalities and higher order convergence rates for Tikhonov reg- ularisation on Banach spaces.J

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.256508Z

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Observation 5d548ce2-e5af-4728-b588-98ca5ed0437c · outbound

This paper cites Tomographic X-ray data of a walnut.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Tomographic X-ray data of a walnut

Reference 37

Resolution: verified exact; local_arxiv, observed 2026-07-01T14:05:46.962804Z

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Observation ff20b5ce-2427-4e44-bd9e-216e3fc16a9f · outbound

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Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Unresolved cited work

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.280223Z

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Observation 5fd6d1c3-775b-49ad-9bbf-a5265dd3e421 · outbound

This paper cites Convergence rates for regularization of ill-posed problems in Banach spaces by approximate source conditions.Inverse Problems, 24(4):045007, 10.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Convergence rates for regularization of ill-posed problems in Banach spaces by approximate source conditions.Inverse Problems, 24(4):045007, 10

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.225994Z

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Observation bea36377-844a-4bb0-90cb-0e687ad0415f · outbound

This paper cites Hinterm¨ uller and K.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Hinterm¨ uller and K

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.235043Z

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Observation 818b2fa9-5b2a-468e-ae81-7b9aeb6d9d80 · outbound

This paper cites Hofmann, B.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Hofmann, B

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.231040Z

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Observation 66ff8083-aace-4575-930a-039f448e086c · outbound

This paper cites Iglesias, Gwenael Mercier, and Otmar Scherzer.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Iglesias, Gwenael Mercier, and Otmar Scherzer

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.288092Z

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Observation 55d910e8-6788-4a1b-8d7c-dd4e0bcf2a7c · outbound

This paper cites Kindermann and S.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Kindermann and S

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.213456Z

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Observation 5e07a1a4-05af-401a-9342-a442a8fef62e · outbound

This paper cites Convex Tikhonov regularization in Banach spaces: new results on conver- gence rates.J.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Convex Tikhonov regularization in Banach spaces: new results on conver- gence rates.J

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Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.209758Z

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Observation d6960533-496b-4ef9-bd61-dc9d1fa9153a · outbound

This paper cites an unresolved cited work.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Unresolved cited work

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.208016Z

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Observation 296280d2-a09a-41d8-b6ab-6c1780f7762c · outbound

This paper cites Kuchment and L.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Kuchment and L

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.250908Z

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Observation 29e06872-2e46-4ab7-bf23-1592e400adc1 · outbound

This paper cites Li and L.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Li and L

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.215145Z

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Observation ee4955f3-ee6c-495f-a4b4-a381a863c527 · outbound

This paper cites an unresolved cited work.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Unresolved cited work

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.206359Z

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Observation 87d9e909-5914-4e14-8b23-086ca22fd31b · outbound

This paper cites American Mathematical Society, Providence, RI.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography American Mathematical Society, Providence, RI

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.200266Z

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Observation 5910eb69-e8f7-4000-8cc7-dcd5ba151d42 · outbound

This paper cites Mueller and S.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Mueller and S

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.164289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 7089b783-cdca-427d-9f36-40d6e43bee34 · outbound

This paper cites Natterer.The Mathematics of Computerized Tomography.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Natterer.The Mathematics of Computerized Tomography

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.245224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation a2ae17a7-25cf-4359-96d3-d35ac72d9541 · outbound

This paper cites Nesterov.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Nesterov

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.195080Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation e2e03fcd-75a1-4989-ac9b-cb72b1dd50ff · outbound

This paper cites On enhanced convergence rates for Tikhonov regularization of nonlinear ill-posed problems in Banach spaces.Inverse Problems, 25(6):065009, 10.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography On enhanced convergence rates for Tikhonov regularization of nonlinear ill-posed problems in Banach spaces.Inverse Problems, 25(6):065009, 10

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.186880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 21cade1a-030b-49b3-9735-c0c2a8c68393 · outbound

This paper cites Modified Tikhonov regularization for nonlinear ill-posed problems in Banach spaces.J.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Modified Tikhonov regularization for nonlinear ill-posed problems in Banach spaces.J

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.196922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 3f382704-ba82-4902-a6d4-1a9d7a89379a · outbound

This paper cites Improved and extended results for enhanced convergence rates of Tikhonov regularization in Banach spaces.Appl.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Improved and extended results for enhanced convergence rates of Tikhonov regularization in Banach spaces.Appl

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.180700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 1b4753d3-f0a2-473a-86db-a7eb61e94799 · outbound

This paper cites Qi and J.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Qi and J

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.228858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation a6d0a0c6-7606-48e3-8bca-8acb2605e7d3 · outbound

This paper cites Regularization of ill-posed problems in Banach spaces: convergence rates.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Regularization of ill-posed problems in Banach spaces: convergence rates

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.202083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 3b4f7e7e-cee7-4c93-a085-72918edb4df2 · outbound

This paper cites Error estimates for non-quadratic regularization and the relation to enhancement.Inverse Problems, 22(3):801–814.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Error estimates for non-quadratic regularization and the relation to enhancement.Inverse Problems, 22(3):801–814

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.271394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation ae7300d2-bdb5-435c-b0ab-005f8b385f41 · outbound

This paper cites an unresolved cited work.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Unresolved cited work

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.221553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 0418c1b3-5689-48e1-83ca-654821474a8e · outbound

This paper cites an unresolved cited work.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Unresolved cited work

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.172774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation c4235979-e930-4ebc-9ac8-fe46583af6d3 · outbound

This paper cites an unresolved cited work.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Unresolved cited work

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.193467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 9de05bb6-8e9b-42a9-b22f-bb42c43e2263 · outbound

This paper cites Rudin, Stanley Osher, and Emad Fatemi.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Rudin, Stanley Osher, and Emad Fatemi

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.167979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 63a764a0-a2ed-4dd1-a066-1291345ed98b · outbound

This paper cites Scherzer, M.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Scherzer, M

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.169542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 513c5147-d780-480a-8e94-76dab647e39e · outbound

This paper cites an unresolved cited work.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Unresolved cited work

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.269157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 6369afd4-4c5c-43a7-b730-679f75408ff2 · outbound

This paper cites van Aarle, W.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography van Aarle, W

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.174479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 727cbf38-ddd3-4e69-8b32-927c960a0a61 · outbound

This paper cites Wang and M.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography Wang and M

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.161130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

Observation 6560e004-cb78-4761-8200-6bb1d7d68396 · outbound

This paper cites SSSN” stands for our semismooth ∗ Newton approach, i.e., Algorithm 3.3, and “CP.

Efficient TV regularization of large-scale linear inverse problems via the SCD semismooth* Newton method with applications in tomography SSSN” stands for our semismooth ∗ Newton approach, i.e., Algorithm 3.3, and “CP

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T14:23:52.282090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.

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Outbound references

Pith citing papers