An accelerated semi-proximal ADMM with applications to multi-block sparse optimization problems | Paper Citation Record

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This paper cites and Marroco, A.: Sur l’approximation, par ´ el´ ements ﬁnis d’ordre un, et la r´ esolution, par p´ enalisation-dualit´ e d’une classe de probl` emes de Dirichlet non lin´ eaires.

An accelerated semi-proximal ADMM with applications to multi-block sparse optimization problems and Marroco, A.: Sur l’approximation, par ´ el´ ements ﬁnis d’ordre un, et la r´ esolution, par p´ enalisation-dualit´ e d’une classe de probl` emes de Dirichlet non lin´ eaires

Reference 1

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Reference 2

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Reference 3

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Reference 4

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Reference 5

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Reference 6

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Reference 7

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An accelerated semi-proximal ADMM with applications to multi-block sparse optimization problems Unresolved cited work

Reference 8

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Reference 9

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This paper cites Advances in neural in formation processing systems.

An accelerated semi-proximal ADMM with applications to multi-block sparse optimization problems Advances in neural in formation processing systems

Reference 10

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Reference 11

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Reference 12

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Reference 13

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Reference 14

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Reference 15

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An accelerated semi-proximal ADMM with applications to multi-block sparse optimization problems Unresolved cited work

Reference 16

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An accelerated semi-proximal ADMM with applications to multi-block sparse optimization problems Unresolved cited work

Reference 17

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This paper cites Splitting methods in communication, imaging, science, and engineeri ng.

An accelerated semi-proximal ADMM with applications to multi-block sparse optimization problems Splitting methods in communication, imaging, science, and engineeri ng

Reference 18

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An accelerated semi-proximal ADMM with applications to multi-block sparse optimization problems Unresolved cited work

Reference 19

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An accelerated semi-proximal ADMM with applications to multi-block sparse optimization problems Unresolved cited work

Reference 20

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This paper cites : Fast alternating direction op- timization methods.

An accelerated semi-proximal ADMM with applications to multi-block sparse optimization problems : Fast alternating direction op- timization methods

Reference 21

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An accelerated semi-proximal ADMM with applications to multi-block sparse optimization problems Unresolved cited work

Reference 22

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Reference 23

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This paper cites Lagrangian-based methods in convex optimization: prediction-correction frameworks with non-ergodic convergence rates.

An accelerated semi-proximal ADMM with applications to multi-block sparse optimization problems Lagrangian-based methods in convex optimization: prediction-correction frameworks with non-ergodic convergence rates

Reference 24

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An accelerated semi-proximal ADMM with applications to multi-block sparse optimization problems Unresolved cited work

Reference 25

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An accelerated semi-proximal ADMM with applications to multi-block sparse optimization problems Unresolved cited work

Reference 26

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An accelerated semi-proximal ADMM with applications to multi-block sparse optimization problems Unresolved cited work

Reference 27

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An accelerated semi-proximal ADMM with applications to multi-block sparse optimization problems Unresolved cited work

Reference 28

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Observation f5efd4b8-568c-44db-9349-7cecb8acab98 · outbound

This paper cites An Efficient HPR Algorithm for the Wasserstein Barycenter Problem with $O({Dim(P)}/\varepsilon)$ Computational Complexity.

An accelerated semi-proximal ADMM with applications to multi-block sparse optimization problems An Efficient HPR Algorithm for the Wasserstein Barycenter Problem with $O({Dim(P)}/\varepsilon)$ Computational Complexity

Reference 29

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An accelerated semi-proximal ADMM with applications to multi-block sparse optimization problems Unresolved cited work

Reference 30

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An accelerated semi-proximal ADMM with applications to multi-block sparse optimization problems Unresolved cited work

Reference 31

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This paper cites A.: A family of variable metric proximal methods.

An accelerated semi-proximal ADMM with applications to multi-block sparse optimization problems A.: A family of variable metric proximal methods

Reference 32

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An accelerated semi-proximal ADMM with applications to multi-block sparse optimization problems Unresolved cited work

Reference 33

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This paper cites arXiv preprint arXiv:2408 .12179.

An accelerated semi-proximal ADMM with applications to multi-block sparse optimization problems arXiv preprint arXiv:2408 .12179

Reference 34

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This paper cites Princeton Universit y Press Princeton, 1970.

An accelerated semi-proximal ADMM with applications to multi-block sparse optimization problems Princeton Universit y Press Princeton, 1970

Reference 35

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An accelerated semi-proximal ADMM with applications to multi-block sparse optimization problems Unresolved cited work

Reference 36

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This paper cites IEEE Access.

An accelerated semi-proximal ADMM with applications to multi-block sparse optimization problems IEEE Access

Reference 37

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An accelerated semi-proximal ADMM with applications to multi-block sparse optimization problems Unresolved cited work

Reference 38

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This paper cites W avelets and Sparsity XV.

An accelerated semi-proximal ADMM with applications to multi-block sparse optimization problems W avelets and Sparsity XV

Reference 39

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An accelerated semi-proximal ADMM with applications to multi-block sparse optimization problems Unresolved cited work

Reference 40

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An accelerated semi-proximal ADMM with applications to multi-block sparse optimization problems Unresolved cited work

Reference 41

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An accelerated semi-proximal ADMM with applications to multi-block sparse optimization problems Unresolved cited work

Reference 42

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An accelerated semi-proximal ADMM with applications to multi-block sparse optimization problems Unresolved cited work

Reference 43

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Observation 8b51fcb6-3f6c-4652-be07-e6316e426bd9 · outbound

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An accelerated semi-proximal ADMM with applications to multi-block sparse optimization problems Unresolved cited work

Reference 44

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:53:26.653807Z

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Observation 46a2e73d-78fc-48cd-8a44-c7a42d2da6c0 · outbound

This paper cites an unresolved cited work.

An accelerated semi-proximal ADMM with applications to multi-block sparse optimization problems Unresolved cited work

Reference 45

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Observation dbf4c593-021e-492c-b79f-f949578085da · outbound

This paper cites IEEE Trans.

An accelerated semi-proximal ADMM with applications to multi-block sparse optimization problems IEEE Trans

Reference 46

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This paper cites IE EE Transactions on image.

An accelerated semi-proximal ADMM with applications to multi-block sparse optimization problems IE EE Transactions on image

Reference 47

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Observation efd0dfe1-be6a-4665-a26a-4eb42384f172 · outbound

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An accelerated semi-proximal ADMM with applications to multi-block sparse optimization problems Unresolved cited work

Reference 48

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:53:25.585197Z

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Observation de5c3100-5c75-437b-a504-c64b2f0195d5 · outbound

This paper cites : Distributed optimization and statistical learning via the alternating direction method of multipliers.

An accelerated semi-proximal ADMM with applications to multi-block sparse optimization problems : Distributed optimization and statistical learning via the alternating direction method of multipliers

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:25.435069Z

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Observation e3b43c14-e18f-4980-a1c4-ba8d65ec15f4 · outbound

This paper cites (41) From the iterative format of sPADMM, the following equation holds, Axk+1 + Byk+1 − Axk − Byk = 1 τ λ(2zk − zk+1 − zk−1).

An accelerated semi-proximal ADMM with applications to multi-block sparse optimization problems (41) From the iterative format of sPADMM, the following equation holds, Axk+1 + Byk+1 − Axk − Byk = 1 τ λ(2zk − zk+1 − zk−1)

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:25.191522Z

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Observation ad10aa2b-2f09-4be2-8c82-bb854d72f509 · outbound

This paper cites For a detailed description of the last inequality above, we let ak := zk+1 − 2zk + zk−1 and bk := B(yk+1 − 2yk + yk−1).

An accelerated semi-proximal ADMM with applications to multi-block sparse optimization problems For a detailed description of the last inequality above, we let ak := zk+1 − 2zk + zk−1 and bk := B(yk+1 − 2yk + yk−1)

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:24.993970Z

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Observation 3b638979-9eb4-4a22-902c-9d30dc9b580f · outbound

This paper cites where the third inequality uses ( 41).

An accelerated semi-proximal ADMM with applications to multi-block sparse optimization problems where the third inequality uses ( 41)

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:24.826901Z

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Observation 2a6f8feb-c7d4-4e49-969f-a41c3b60cc02 · outbound

This paper cites an unresolved cited work.

An accelerated semi-proximal ADMM with applications to multi-block sparse optimization problems Unresolved cited work

Reference 53

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:53:24.519528Z

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Observation 8dd5e974-a92f-4b5d-8df8-e0e692c32856 · outbound

This paper cites Then, one can get 0 < m < 1, τ −2 2τ 2λ < t < 0, 0 < 1 4tτ 2 + λ 2 < λ 2 , t − τ −2 2τ 2λ < 1 2τ λ and ( t − τ −2 2τ λ )/( 1 2τ λ) = ( 1 4tτ 2 + λ 2 )/( λ 2 ) = m.

An accelerated semi-proximal ADMM with applications to multi-block sparse optimization problems Then, one can get 0 < m < 1, τ −2 2τ 2λ < t < 0, 0 < 1 4tτ 2 + λ 2 < λ 2 , t − τ −2 2τ 2λ < 1 2τ λ and ( t − τ −2 2τ λ )/( 1 2τ λ) = ( 1 4tτ 2 + λ 2 )/( λ 2 ) = m

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:53:24.215173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4267957b-40fd-483d-adae-6abb20b96e45 · outbound

This paper cites an unresolved cited work.

An accelerated semi-proximal ADMM with applications to multi-block sparse optimization problems Unresolved cited work

Reference 55

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:53:23.946501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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