Paper Citation Record · LEDGER

On the Iterate Convergence of Bregman Projected Gradient Method

As of 10 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 2 inbound Pith citation observations for arXiv:2608.05035.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2608.05035 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T11:09:52.237733Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T12:00:38.160589Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-08T11:35:12.874589Z

Reference resolution

43 of 43 outbound references displayed

verified exact1
verified fuzzy38
unresolved4
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fdf86273-6ad0-4aa8-a9eb-268e9403b9de · outbound

This paper cites Hessian Riemannian gradient flows in convex programming.SIAM journal on control and optimization, 43(2):477–501, 2004.

On the Iterate Convergence of Bregman Projected Gradient Method Hessian Riemannian gradient flows in convex programming.SIAM journal on control and optimization, 43(2):477–501, 2004

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:54.327055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation def7beaa-93b8-4324-b873-506b771c0903 · outbound

This paper cites On the convergence of the proximal algorithm for nonsmooth functions involving analytic features.Mathematical Programming, Series B, 116:5–16, 2009.

On the Iterate Convergence of Bregman Projected Gradient Method On the convergence of the proximal algorithm for nonsmooth functions involving analytic features.Mathematical Programming, Series B, 116:5–16, 2009

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:54.091963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 98ccd995-9bab-42bb-91e9-aec246cb51e1 · outbound

This paper cites an unresolved cited work.

On the Iterate Convergence of Bregman Projected Gradient Method Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-06T11:09:53.851066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b945dc4c-a59b-4923-8fd9-9c2485171427 · outbound

This paper cites The rate of convergence of Bregman proximal methods: Local geometry versus regularity versus sharpness.SIAM Journal on Optimization, 34(3):2440–2471, 2024.

On the Iterate Convergence of Bregman Projected Gradient Method The rate of convergence of Bregman proximal methods: Local geometry versus regularity versus sharpness.SIAM Journal on Optimization, 34(3):2440–2471, 2024

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:53.538306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c3f2657b-ff45-462a-bb8f-83007fed11dc · outbound

This paper cites A descent lemma beyond Lipschitz gradient continuity: First-order methods revisited and applications.Mathematics of Operations Research, 42(2):330–348, 2017.

On the Iterate Convergence of Bregman Projected Gradient Method A descent lemma beyond Lipschitz gradient continuity: First-order methods revisited and applications.Mathematics of Operations Research, 42(2):330–348, 2017

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:53.140953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1c77e344-e9d3-43f2-8d65-8c2602b58b7f · outbound

This paper cites an unresolved cited work.

On the Iterate Convergence of Bregman Projected Gradient Method Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-06T11:09:52.817425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation aaf4e895-d063-415d-aead-4aef92698c36 · outbound

This paper cites MOS-SIAM Series on Optimization.

On the Iterate Convergence of Bregman Projected Gradient Method MOS-SIAM Series on Optimization

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:52.782769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 94c3974f-1447-4cc9-931b-42c7986b064e · outbound

This paper cites Image deblurring with Poisson data: From cells to galaxies.Inverse Problems, 25(12):123006, 2009.

On the Iterate Convergence of Bregman Projected Gradient Method Image deblurring with Poisson data: From cells to galaxies.Inverse Problems, 25(12):123006, 2009

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:52.758762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e5014b09-1ad9-48a2-b26f-90d5e337a599 · outbound

This paper cites Barrier operators and associated gradient-like dy- namical systems for constrained minimization problems.SIAM journal on control and optimization, 42(4):1266–1292, 2003.

On the Iterate Convergence of Bregman Projected Gradient Method Barrier operators and associated gradient-like dy- namical systems for constrained minimization problems.SIAM journal on control and optimization, 42(4):1266–1292, 2003

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:52.736446Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b70cf760-7bd1-44ca-a42b-96be268c476d · outbound

This paper cites The Łojasiewicz inequality for nons- mooth subanalytic functions with applications to subgradient dynamical systems.SIAM Journal on Optimization, 17(4):1205–1223, 2007.

On the Iterate Convergence of Bregman Projected Gradient Method The Łojasiewicz inequality for nons- mooth subanalytic functions with applications to subgradient dynamical systems.SIAM Journal on Optimization, 17(4):1205–1223, 2007

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:52.712252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3f47ff8a-e774-4a9e-a1c6-593b46209977 · outbound

This paper cites Proximal alternating linearized minimization for nonconvex and nonsmooth problems.Mathematical Programming, Series A, 146(1):459–494, 2014.

On the Iterate Convergence of Bregman Projected Gradient Method Proximal alternating linearized minimization for nonconvex and nonsmooth problems.Mathematical Programming, Series A, 146(1):459–494, 2014

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:52.684438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2d57df21-53cc-493e-9e61-f663e83b0343 · outbound

This paper cites First order methods beyond convexity and Lipschitz gradient continuity with applications to quadratic inverse problems.SIAM Journal on Optimization, 28(3):2131–2151, 2018.

On the Iterate Convergence of Bregman Projected Gradient Method First order methods beyond convexity and Lipschitz gradient continuity with applications to quadratic inverse problems.SIAM Journal on Optimization, 28(3):2131–2151, 2018

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:52.660148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 93662a4d-e565-48d8-9048-500ead2dc3c4 · outbound

This paper cites Spurious Stationarity and Hardness Results for Bregman Proximal-Type Algorithms.

On the Iterate Convergence of Bregman Projected Gradient Method Spurious Stationarity and Hardness Results for Bregman Proximal-Type Algorithms

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-06T11:09:52.275456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ab357237-805b-4804-90d5-aa154a78e1eb · outbound

This paper cites Springer, New York, 2003.

On the Iterate Convergence of Bregman Projected Gradient Method Springer, New York, 2003

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:52.646141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f17aba71-64cd-422f-915f-9b1a3897374a · outbound

This paper cites On approximate solutions of systems of linear inequalities.

On the Iterate Convergence of Bregman Projected Gradient Method On approximate solutions of systems of linear inequalities

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:52.633939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 70cf0422-915a-413d-add8-7e3b9b7e55a0 · outbound

This paper cites Riemannian proximal gradient methods.Mathematical Programming, 194(1):371–413, 2022.

On the Iterate Convergence of Bregman Projected Gradient Method Riemannian proximal gradient methods.Mathematical Programming, 194(1):371–413, 2022

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:52.621194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation cf9ef305-c212-43a6-9ac8-008934a46120 · outbound

This paper cites Central paths, generalized proximal point methods, and Cauchy trajectories in Riemannian manifolds.SIAM Journal on Control and Optimization, 37(2):566–588, 1999.

On the Iterate Convergence of Bregman Projected Gradient Method Central paths, generalized proximal point methods, and Cauchy trajectories in Riemannian manifolds.SIAM Journal on Control and Optimization, 37(2):566–588, 1999

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:52.606904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 26e57d11-b31a-48f7-9334-901790388770 · outbound

This paper cites Non-Convex Optimization for Machine Learning.

On the Iterate Convergence of Bregman Projected Gradient Method Non-Convex Optimization for Machine Learning

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:52.592457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 40ef6477-58da-4a64-b51b-185659252b05 · outbound

This paper cites Unifying mirror descent and dual averaging.Mathematical Programming, Series A, 199(1-2):793–830, 2023.

On the Iterate Convergence of Bregman Projected Gradient Method Unifying mirror descent and dual averaging.Mathematical Programming, Series A, 199(1-2):793–830, 2023

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:52.579611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 95d223f3-4f29-4329-941d-fe487f9b9982 · outbound

This paper cites Bregman Finito/MISO for nonconvex regularized finite sum minimization without Lipschitz gradient continuity.SIAM Journal on Optimization, 32(3):2230–2262, 2022.

On the Iterate Convergence of Bregman Projected Gradient Method Bregman Finito/MISO for nonconvex regularized finite sum minimization without Lipschitz gradient continuity.SIAM Journal on Optimization, 32(3):2230–2262, 2022

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:52.564954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f536cf5b-b192-4cfc-8c0e-24fb9dbabcb8 · outbound

This paper cites an unresolved cited work.

On the Iterate Convergence of Bregman Projected Gradient Method Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-08-06T11:09:52.551550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 73f086e6-5108-4893-8c73-4f65b46c7e63 · outbound

This paper cites A convergent single-loop algorithm for relaxation of Gromov-Wasserstein in graph data.

On the Iterate Convergence of Bregman Projected Gradient Method A convergent single-loop algorithm for relaxation of Gromov-Wasserstein in graph data

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:52.538432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1a34e581-1864-4aa2-ba89-b74a7405e536 · outbound

This paper cites Relatively smooth convex optimization by first-order methods, and applications.SIAM Journal on Optimization, 28(1):333–354, 2018.

On the Iterate Convergence of Bregman Projected Gradient Method Relatively smooth convex optimization by first-order methods, and applications.SIAM Journal on Optimization, 28(1):333–354, 2018

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T11:09:52.144761Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a528a1c6-62b2-47e1-8481-8ab97a06c150 · outbound

This paper cites Errorbounds forquadraticsystems.

On the Iterate Convergence of Bregman Projected Gradient Method Errorbounds forquadraticsystems

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:52.518814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6eade96c-c0d0-4d0e-a573-14bfd7840960 · outbound

This paper cites Error bounds and convergence analysis of feasible descent methods: A general approach.Annals of Operations Research, 46(1):157–178, 1993.

On the Iterate Convergence of Bregman Projected Gradient Method Error bounds and convergence analysis of feasible descent methods: A general approach.Annals of Operations Research, 46(1):157–178, 1993

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:52.507487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 535cf092-f762-4fc8-8710-409cc52a456c · outbound

This paper cites Pearson, South Carolina, 1999.

On the Iterate Convergence of Bregman Projected Gradient Method Pearson, South Carolina, 1999

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:52.495155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1c7c36e6-051f-4698-a783-ab8d4147fdaf · outbound

This paper cites Self-scaled barriers and interior-point methods for convex programming.Mathematics of Operations research, 22(1):1–42, 1997.

On the Iterate Convergence of Bregman Projected Gradient Method Self-scaled barriers and interior-point methods for convex programming.Mathematics of Operations research, 22(1):1–42, 1997

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:52.482752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9512fcf6-8244-4537-a140-c36e4e8aca5b · outbound

This paper cites Springer, Berlin, Heidelberg, 2018.

On the Iterate Convergence of Bregman Projected Gradient Method Springer, Berlin, Heidelberg, 2018

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:52.471938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5e9ca172-d839-4b80-a75d-ea351a93d14c · outbound

This paper cites Springer Series in Operations Re- search and Financial Engineering, 2nd edn.

On the Iterate Convergence of Bregman Projected Gradient Method Springer Series in Operations Re- search and Financial Engineering, 2nd edn

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:52.459020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b5df7a29-8704-4696-a49a-2d51d77d0bcd · outbound

This paper cites Computational Optimal Transport: With Applica- tions to Data Science.Foundations and Trends®in Machine Learning, 11(5-6):355–607, 2019.

On the Iterate Convergence of Bregman Projected Gradient Method Computational Optimal Transport: With Applica- tions to Data Science.Foundations and Trends®in Machine Learning, 11(5-6):355–607, 2019

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:52.446057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ded61a90-9a0f-4433-aa0a-8f6268709b79 · outbound

This paper cites Springer Science & Business Media, Berlin, Heidelberg, 2009.

On the Iterate Convergence of Bregman Projected Gradient Method Springer Science & Business Media, Berlin, Heidelberg, 2009

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:52.436025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation de38fb08-e681-400c-8999-995d6dcc547c · outbound

This paper cites Nonconvex optimization for signal processing and machine learning [from the guest editors].IEEE Signal Processing Magazine, 37(5):15–17, 2020.

On the Iterate Convergence of Bregman Projected Gradient Method Nonconvex optimization for signal processing and machine learning [from the guest editors].IEEE Signal Processing Magazine, 37(5):15–17, 2020

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:52.425591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5e9e98d3-5725-49ba-8c7e-c77c799b7eae · outbound

This paper cites A new non- monotonic algorithm for pet image reconstruction.

On the Iterate Convergence of Bregman Projected Gradient Method A new non- monotonic algorithm for pet image reconstruction

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:52.415369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4e8a6d3c-dfcf-4c93-b191-35a176da2772 · outbound

This paper cites Neural Information Processing Series.

On the Iterate Convergence of Bregman Projected Gradient Method Neural Information Processing Series

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:52.400871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c3682586-d7b5-46d9-8b63-5edf837e0c9c · outbound

This paper cites Approximate Bregman proximal gradient algo- rithm for relatively smooth nonconvex optimization.Computational Optimization and Applications, 90(1):227–256, 2025.

On the Iterate Convergence of Bregman Projected Gradient Method Approximate Bregman proximal gradient algo- rithm for relatively smooth nonconvex optimization.Computational Optimization and Applications, 90(1):227–256, 2025

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:52.387702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f448c369-99a8-4763-b78c-a48b728b190d · outbound

This paper cites New Bregman proximal type algorithms for solving DC optimization problems.Computational Optimization and Applications, 83(3):893–931, 2022.

On the Iterate Convergence of Bregman Projected Gradient Method New Bregman proximal type algorithms for solving DC optimization problems.Computational Optimization and Applications, 83(3):893–931, 2022

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:52.374287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0cdd2092-f5c0-4b54-9721-6a87c12594db · outbound

This paper cites A simplified view of first order methods for optimization.Mathematical Programming, Series B, 170(1):67–96, 2018.

On the Iterate Convergence of Bregman Projected Gradient Method A simplified view of first order methods for optimization.Mathematical Programming, Series B, 170(1):67–96, 2018

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:52.363465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 75b27dee-6168-4ca0-89e4-20baa1f1782e · outbound

This paper cites Approximation accuracy, gradient methods, and error bound for structured convex optimization.Mathematical Programming, Series B, 125(2):263–295, 2010.

On the Iterate Convergence of Bregman Projected Gradient Method Approximation accuracy, gradient methods, and error bound for structured convex optimization.Mathematical Programming, Series B, 125(2):263–295, 2010

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:52.350839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d0005dc7-3f1e-4055-bf75-a5cdb091b13f · outbound

This paper cites Inertial proximal gradient methods with Bregman regularization for a class of nonconvex optimization problems.

On the Iterate Convergence of Bregman Projected Gradient Method Inertial proximal gradient methods with Bregman regularization for a class of nonconvex optimization problems

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:52.339236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation db1cf73d-5489-40f4-a6d0-9ed34ed82e03 · outbound

This paper cites Gromov-Wasserstein learning for graph matching and node embedding.

On the Iterate Convergence of Bregman Projected Gradient Method Gromov-Wasserstein learning for graph matching and node embedding

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:52.325825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e639f5d7-24d1-477a-ab34-d1922cf74e6d · outbound

This paper cites Proximal-like incremental aggregated gradient method with linear convergence under Bregman distance growth conditions.

On the Iterate Convergence of Bregman Projected Gradient Method Proximal-like incremental aggregated gradient method with linear convergence under Bregman distance growth conditions

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:52.312275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f410231d-972b-4780-b0ae-bb78e158d912 · outbound

This paper cites A unified approach to error bounds for structured convex optimization problems.Mathematical Programming, Series A, 165(2):689–728, 2017.

On the Iterate Convergence of Bregman Projected Gradient Method A unified approach to error bounds for structured convex optimization problems.Mathematical Programming, Series A, 165(2):689–728, 2017

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:52.300702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 328d08b7-afac-416f-992b-21523d5c8fee · outbound

This paper cites Level-set subdifferential error bounds and linear convergence of Bregman proximal gradient method.Journal of Optimization Theory and Applications, 189(3):889–918, 2021.

On the Iterate Convergence of Bregman Projected Gradient Method Level-set subdifferential error bounds and linear convergence of Bregman proximal gradient method.Journal of Optimization Theory and Applications, 189(3):889–918, 2021

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:09:52.287955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

Observation bd213bdc-957a-4571-9cfe-78f672721347 · inbound

A Unified Framework for Iterate Convergence of Bregman Proximal Methods cites this paper.

A Unified Framework for Iterate Convergence of Bregman Proximal Methods On the Iterate Convergence of Bregman Projected Gradient Method

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-08T11:35:12.878626Z

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Establishing Boundary KKT Convergence of Mirror Descent through Reparameterization cites this paper.

Establishing Boundary KKT Convergence of Mirror Descent through Reparameterization On the Iterate Convergence of Bregman Projected Gradient Method

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