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Paper Citation Record · LEDGER

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization

As of 20 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 0 inbound Pith citation observations for arXiv:2608.12043.

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

pith.paper-citation-record.v1
2608.12043 v1

Coverage vector

measured 58 of 58 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:26:56.651885Z

measured 58 of 58 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

58 of 58 outbound references displayed

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External citation measurements

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Outbound references

Observation 9ca48e5d-e259-44c3-9b4f-c9b9d05da8b7 · outbound

This paper cites Moving Anchor Extragradient Methods For Smooth Structured Minimax Problems.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Moving Anchor Extragradient Methods For Smooth Structured Minimax Problems

Reference 1

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Observation 4f2f753d-4999-4148-94e1-8777a8d14147 · outbound

This paper cites Katyusha: The first direct acceleration of stochastic gradient methods.Journal of Machine Learning Research, 18(221):1–51, 2018.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Katyusha: The first direct acceleration of stochastic gradient methods.Journal of Machine Learning Research, 18(221):1–51, 2018

Reference 2

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Observation 19a5d371-edb7-4c91-85f7-21883b0b0b31 · outbound

This paper cites Bauschke and Patrick L.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Bauschke and Patrick L

Reference 3

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Observation 0968775c-7506-4dd4-a8cf-8023e8dc68ca · outbound

This paper cites Stochastic gradient descent-ascent: Unified theory and new efficient methods.International Conference on Artificial Intelligence and Statistics, 2023.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Stochastic gradient descent-ascent: Unified theory and new efficient methods.International Conference on Artificial Intelligence and Statistics, 2023

Reference 4

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Observation c010fa8d-08fc-43ec-879b-0d7f4328aef3 · outbound

This paper cites Bot ¸ and Enis Chenchene.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Bot ¸ and Enis Chenchene

Reference 5

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Observation b9dee892-0d6f-43d2-aafd-af0c48620dcf · outbound

This paper cites Fast Optimistic Gradient De- scent Ascent (OGDA) method in continuous and discrete time.Foundations of Computational Mathematics, 2023.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Fast Optimistic Gradient De- scent Ascent (OGDA) method in continuous and discrete time.Foundations of Computational Mathematics, 2023

Reference 6

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Source-reported events for the cited work

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Observation 1075a698-88e3-4b0c-8264-0428b1809114 · outbound

This paper cites Stochastic fixed-point iterations for nonexpansive maps: Convergence and error bounds.SIAM Journal on Control and Optimization, 62(1):191–219, 2024.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Stochastic fixed-point iterations for nonexpansive maps: Convergence and error bounds.SIAM Journal on Control and Optimization, 62(1):191–219, 2024

Reference 7

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Observation dcdff90e-0306-4569-acd3-20baafe255e7 · outbound

This paper cites Stochastic Halpern iteration in normed spaces and appli- cations to reinforcement learning.Mathematical Programming, 2026.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Stochastic Halpern iteration in normed spaces and appli- cations to reinforcement learning.Mathematical Programming, 2026

Reference 8

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Observation 0dfd8b07-87ce-46a7-8c0a-f5ab11d6fb1f · outbound

This paper cites Stochastic halpern iteration with variance reduction for stochastic monotone inclusions.Neural Information Processing Systems, 2022.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Stochastic halpern iteration with variance reduction for stochastic monotone inclusions.Neural Information Processing Systems, 2022

Reference 9

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Observation 07cd6030-aef7-4138-bfc9-d0ee991d5383 · outbound

This paper cites Variance reduced halpern iteration for finite-sum monotone inclusions.International Conference on Learning Representations, 2024.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Variance reduced halpern iteration for finite-sum monotone inclusions.International Conference on Learning Representations, 2024

Reference 10

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Observation 05ede653-4c07-4d4f-b534-bf5962988ffd · outbound

This paper cites Accelerated single-call methods for constrained min-max optimiza- tion.International Conference on Learning Representations, 2023.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Accelerated single-call methods for constrained min-max optimiza- tion.International Conference on Learning Representations, 2023

Reference 11

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Observation 6657cf94-628b-4a60-afdf-b87bae7509a4 · outbound

This paper cites Near-optimal algorithms for making the gradient small in stochastic minimax optimization.Journal of Machine Learning Research, 25(387):1–44, 2024.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Near-optimal algorithms for making the gradient small in stochastic minimax optimization.Journal of Machine Learning Research, 25(387):1–44, 2024

Reference 12

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Observation a5b13463-1cb6-41b7-b5ad-d95ae82959a0 · outbound

This paper cites Training GANs with optimism.International Conference on Learning Representations, 2018.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Training GANs with optimism.International Conference on Learning Representations, 2018

Reference 13

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Observation bc69296a-bac9-406f-9d90-a8d6ba6df40d · outbound

This paper cites Variance reduction for root-finding problems.Mathematical Programming, 197(1): 375–410, 2023.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Variance reduction for root-finding problems.Mathematical Programming, 197(1): 375–410, 2023

Reference 14

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Observation 9e453e6e-020f-46b2-b71f-9a0644e7ba28 · outbound

This paper cites First-order methods of smooth convex optimization with inexact oracle.Mathematical Programming, 146(1):37–75, 2014.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization First-order methods of smooth convex optimization with inexact oracle.Mathematical Programming, 146(1):37–75, 2014

Reference 15

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 8f35f8b8-8485-45ce-9772-63c452f74990 · outbound

This paper cites Halpern iteration for near-optimal and parameter-free monotone inclusion and strong solutions to variational inequalities.Conference on Learning Theory, 2020.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Halpern iteration for near-optimal and parameter-free monotone inclusion and strong solutions to variational inequalities.Conference on Learning Theory, 2020

Reference 16

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Observation b733cfbb-7c63-4c5a-b088-914c58d2d5da · outbound

This paper cites Springer-Verlag, 2003.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Springer-Verlag, 2003

Reference 17

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Observation 5d1eddbd-f8d9-49f0-b8fd-88e06ccf6a64 · outbound

This paper cites Accelerated gradient methods for nonconvex nonlinear and stochastic programming.Mathematical Programming, 156(1):59–99, 2016.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Accelerated gradient methods for nonconvex nonlinear and stochastic programming.Mathematical Programming, 156(1):59–99, 2016

Reference 18

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Observation c8efa343-914a-4ecb-a17f-fa76b07040bf · outbound

This paper cites Generative adversarial nets.Neural Information Processing Systems, 2014.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Generative adversarial nets.Neural Information Processing Systems, 2014

Reference 19

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Observation 6de432b0-ef02-413c-8c90-750925592e3a · outbound

This paper cites Stochastic extragradient: General analysis and improved rates.International Conference on Artificial Intelligence and Statis- tics, 2022.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Stochastic extragradient: General analysis and improved rates.International Conference on Artificial Intelligence and Statis- tics, 2022

Reference 20

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Observation 1070d2f6-f4a0-483a-b51b-e6130815421d · outbound

This paper cites Fixed points of nonexpanding maps.Bulletin of the American Mathematical Society, 73(6):957–961, 1967.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Fixed points of nonexpanding maps.Bulletin of the American Mathematical Society, 73(6):957–961, 1967

Reference 21

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Observation 4fe22a57-fa47-43da-93aa-8a62c2f50034 · outbound

This paper cites Accelerated proximal point method for maximally monotone operators.Mathe- matical Programming, 190(1–2):57–87, 2021.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Accelerated proximal point method for maximally monotone operators.Mathe- matical Programming, 190(1–2):57–87, 2021

Reference 22

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Observation e906fb94-afb9-44ef-a09a-1c31b4f46ea5 · outbound

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Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Unresolved cited work

Reference 23

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Observation 3d67c067-54d5-43db-b4f2-eb10f26b89f7 · outbound

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Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Unresolved cited work

Reference 24

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Observation 658c1c17-9d54-4389-a09b-c067cb3facc7 · outbound

This paper cites an unresolved cited work.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Unresolved cited work

Reference 25

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Observation 5829444e-f5de-4214-9f77-acc31bc9c702 · outbound

This paper cites Near-optimal sample complexity for MDPs via anchoring.Proceedings of the 42nd international conference on machine learning, 2025.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Near-optimal sample complexity for MDPs via anchoring.Proceedings of the 42nd international conference on machine learning, 2025

Reference 26

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 0d113d78-1513-45a7-9639-0a3ce569dbb6 · outbound

This paper cites Fast extra gradient methods for smooth structured nonconvex– nonconcave minimax problems.Neural Information Processing Systems, 2021.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Fast extra gradient methods for smooth structured nonconvex– nonconcave minimax problems.Neural Information Processing Systems, 2021

Reference 27

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Observation 5122a7b6-5683-42a1-8f75-3374fe71fe08 · outbound

This paper cites PAGE: A simple and optimal probabilistic gradient estimator for nonconvex optimization.International Conference on Machine Learning, 2021.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization PAGE: A simple and optimal probabilistic gradient estimator for nonconvex optimization.International Conference on Machine Learning, 2021

Reference 28

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Observation b6b60c5f-5faa-4aa4-a916-f2bb52e6852e · outbound

This paper cites On the convergence rate of the Halpern-iteration.Optimization Letters, 15(2):405– 418, 2021.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization On the convergence rate of the Halpern-iteration.Optimization Letters, 15(2):405– 418, 2021

Reference 29

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Unavailable: canonical work link unavailable.

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Observation b9786d49-2796-4ff0-8dc1-ab35a39297e3 · outbound

This paper cites A universal catalyst for first-order optimization.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization A universal catalyst for first-order optimization

Reference 30

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 9a515f79-22f0-46ca-a1d2-750d0c127cce · outbound

This paper cites an unresolved cited work.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Unresolved cited work

Reference 31

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-16T00:26:56.525315Z digest=sha256:6d38e3d51208187d938010f87571f887c3a15f1e6fcd2dd1e200f896ca232280

Observation aa174ebd-eca1-42e5-bc18-13ed823eaa34 · outbound

This paper cites Stochastic gradient descent-ascent and consensus optimization for smooth games: Convergence analysis under expected co-coercivity.Neural Information Processing Systems, 2021.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Stochastic gradient descent-ascent and consensus optimization for smooth games: Convergence analysis under expected co-coercivity.Neural Information Processing Systems, 2021

Reference 32

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raw_fallback, observed 2026-08-16T00:26:57.076008Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 21630d7f-f6f8-4f16-b76e-60986c01d4e1 · outbound

This paper cites To- wards deep learning models resistant to adversarial attacks.International Conference on Learning Representations, 2018.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization To- wards deep learning models resistant to adversarial attacks.International Conference on Learning Representations, 2018

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T00:26:57.057820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-16T00:26:56.534787Z digest=sha256:9dfc27a7754be0c128420ff1346213f25a6afa9e0e78e2fc402649940b1e5582

Observation 69ac14be-22d9-4050-9e43-a23073f9bc4d · outbound

This paper cites Mean value methods in iteration.Proceedings of the American Mathematical Society, 4(3):506–510, 1953.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Mean value methods in iteration.Proceedings of the American Mathematical Society, 4(3):506–510, 1953

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T00:26:57.041576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-16T00:26:56.539068Z digest=sha256:770bf4d6930ea4d7f544462961e8854181b7d78659f397b6eb56aee74ec0444d

Observation 5037ba4e-3bdf-40c6-9af0-5b892468da50 · outbound

This paper cites Foo, Vijay Chandrasekhar, and Georgios Piliouras.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Foo, Vijay Chandrasekhar, and Georgios Piliouras

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-16T00:26:56.543541Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:26:56.543541Z digest=sha256:8dcaadac550096dd408c1d1339b682010541ec347b3a312ee05219fd0a4b4c36

Observation 68a09a9e-3ea0-410a-b0a9-43603d56006b · outbound

This paper cites an unresolved cited work.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Unresolved cited work

Reference 36

Resolution
unresolved
raw_fallback, observed 2026-08-16T00:26:57.014715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-16T00:26:56.548196Z digest=sha256:bcc9e68438a21ef160f70b8a6151d3b2e24df139b83819110583b0eda16e43c6

Observation fd586ae2-44e8-4b8e-b384-f54aa3318bcd · outbound

This paper cites Cocoercivity, smoothness and bias in variance-reduced stochastic gradient methods.Numerical Algorithms, 91(2):749–772, 2022.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Cocoercivity, smoothness and bias in variance-reduced stochastic gradient methods.Numerical Algorithms, 91(2):749–772, 2022

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T00:26:56.997984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-16T00:26:56.552500Z digest=sha256:3405435a7b81b1b24648183d3504f3088bbd4ad0f75b8b3a10a80934e6535a39

Observation 813f6fb5-018f-40be-b1e5-5281450bb682 · outbound

This paper cites an unresolved cited work.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Unresolved cited work

Reference 38

Resolution
unresolved
raw_fallback, observed 2026-08-16T00:26:56.981250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-16T00:26:56.557550Z digest=sha256:cd0c3aa601a6fe006c3437ee721ede9eb2dd1d110184acdc1b124e308dc79273

Observation 712a8ae9-39c0-4da8-91f1-09a8265fec21 · outbound

This paper cites Nguyen, Jie Liu, Katya Scheinberg, and Martin Tak´ aˇ c.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Nguyen, Jie Liu, Katya Scheinberg, and Martin Tak´ aˇ c

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T00:26:56.964753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-16T00:26:56.562616Z digest=sha256:a1db33f2270c1f140a86920afda9d5f121766ff4566a560be930523d11f3c2de

Observation 3c1d2767-aa5d-411b-908e-a45f00873e8d · outbound

This paper cites Exact optimal accelerated complexity for fixed-point iterations.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Exact optimal accelerated complexity for fixed-point iterations

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-16T00:26:56.567174Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:26:56.567174Z digest=sha256:6775fd8eb4557b8de624867a17e3fd68fa079688eb343cb2bc5695b21a372f27

Observation 3ebdc605-9956-4ea2-a444-239ce510bd7e · outbound

This paper cites Asymptotic Regularity of a Generalised Stochastic Halpern Scheme.Journal of Optimization Theory and Applications, 210(1):3, 2026.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Asymptotic Regularity of a Generalised Stochastic Halpern Scheme.Journal of Optimization Theory and Applications, 210(1):3, 2026

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T00:26:56.938535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-16T00:26:56.571798Z digest=sha256:676a0db5e19536c0994ed497b8ffe90539908770d43e7f341827992de60f2fc7

Observation e887f767-9197-4054-b36d-90d0bd62bc79 · outbound

This paper cites Tyrrell Rockafellar.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Tyrrell Rockafellar

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-16T00:26:56.576237Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:26:56.576237Z digest=sha256:0fd71849037b7ef8fe8cc56bf4c011e65bf9f0479030df5a1e12b38c677be00e

Observation f12effb2-b58d-4529-9409-b7dae2bff6af · outbound

This paper cites Cam- bridge University Press, 2022.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Cam- bridge University Press, 2022

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T00:26:56.912954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-16T00:26:56.581119Z digest=sha256:f2ad2f64b4ebc9b596d91131ceacc4eec3f6a18fe0a8758ff92971a776560c2d

Observation 537882a6-227c-4629-8378-c86820f1b1d7 · outbound

This paper cites ODE Analysis of Stochastic Gradient Methods with Optimism and Anchoring for Minimax Problems.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization ODE Analysis of Stochastic Gradient Methods with Optimism and Anchoring for Minimax Problems

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-16T00:26:56.586164Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:26:56.586164Z digest=sha256:dd6679b5522eb0a1ef9baf524f39cc41604e67fb4f2a1d274af672e7cfab619a

Observation c1cf30be-8c97-4371-abf5-48020c28c9cb · outbound

This paper cites A first order method for solving convex bilevel optimization problems.SIAM Journal on Optimization, 27(2):640–660, 2017.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization A first order method for solving convex bilevel optimization problems.SIAM Journal on Optimization, 27(2):640–660, 2017

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-16T00:26:56.590801Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:26:56.590801Z digest=sha256:fd8cc4f4f856073f6637cf479887021c997057e1e8c7780f9327f0ee7183eea7

Observation a4acb349-eacd-41d0-8284-79cc34e631a0 · outbound

This paper cites an unresolved cited work.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Unresolved cited work

Reference 46

Resolution
unresolved
raw_fallback, observed 2026-08-16T00:26:56.888058Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-16T00:26:56.595166Z digest=sha256:d62da520a9f3ad3c0bd3e5553f4e862f49c95cd47100704b395b84844df75011

Observation 0ff27ff5-8155-4111-bfd4-20de5915ca36 · outbound

This paper cites Formes bilineaires coercitives sur les ensembles convexes.Comptes Rendus Hebdomadaires Des Seances De L Academie Des Sciences, 258(18):4413, 1964.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Formes bilineaires coercitives sur les ensembles convexes.Comptes Rendus Hebdomadaires Des Seances De L Academie Des Sciences, 258(18):4413, 1964

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T00:26:56.874206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-16T00:26:56.600270Z digest=sha256:ef728f25f3ab7b6e91fbdc25ac8c96b08f0957ec59f51b5359692c09260414ca

Observation 45d43a3d-16a4-40ac-9fbc-3aafa5820003 · outbound

This paper cites Suh, Jisun Park, and Ernest K.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Suh, Jisun Park, and Ernest K

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T00:26:56.859060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-16T00:26:56.604840Z digest=sha256:b16fadb2029b9c3b97863454e601c4097d4cbadb680892af40022692dd5f6e3a

Observation b0ba7c0d-5d15-41b7-ab3f-8f2783d15d5f · outbound

This paper cites From Halpern’s fixed-point iterations to Nesterov’s accelerated interpretations for root-finding problems.Computational Optimization and Applications, 87(1):181–218, 2024.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization From Halpern’s fixed-point iterations to Nesterov’s accelerated interpretations for root-finding problems.Computational Optimization and Applications, 87(1):181–218, 2024

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-16T00:26:56.609479Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:26:56.609479Z digest=sha256:a405f734fe52e0083b127d3420d2bae2a5d41587457b964844b34f32797b2f22

Observation b882eafd-41f2-46cd-bb4d-ad0e13bc0908 · outbound

This paper cites Halpern-Type Accelerated and Splitting Algorithms For Monotone Inclusions.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Halpern-Type Accelerated and Splitting Algorithms For Monotone Inclusions

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-16T00:26:56.614725Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:26:56.614725Z digest=sha256:bc4837b7989f1b198536de29ba6663cf72a7a63f2dbb59d956ee4d0192b4370d

Observation e6cf40e1-0f8a-4504-9a8d-3fcb85c4d45e · outbound

This paper cites Approximation of fixed points of nonexpansive mappings.Archiv der Mathe- matik, 58(5):486–491, 1992.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Approximation of fixed points of nonexpansive mappings.Archiv der Mathe- matik, 58(5):486–491, 1992

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T00:26:56.833375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-16T00:26:56.619760Z digest=sha256:ab6511e75815760ee4a124c4c3e1ef26ff90f9ff2c327a55a015be1cc7dc6b2a

Observation 450a733d-1099-415c-befd-8beda267b948 · outbound

This paper cites A Theory of Composition and Duality of Extremal Optimal Fixed-Point Algorithms.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization A Theory of Composition and Duality of Extremal Optimal Fixed-Point Algorithms

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-16T00:26:56.624497Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:26:56.624497Z digest=sha256:09a6e817fb40338579f84593803743f02a47e08e41b8b36718f1f0243cf1f5f2

Observation dda1ab64-2737-41a9-beaa-af95a431f9e1 · outbound

This paper cites an unresolved cited work.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Unresolved cited work

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-16T00:26:56.629267Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:26:56.629267Z digest=sha256:745dc88d7f3b4d13f1ec364497e8d03a89f76cd3f27cf4dcec600b3644d195c5

Observation 4c5d9ed4-54af-46e9-8a35-8e00f2a88173 · outbound

This paper cites an unresolved cited work.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Unresolved cited work

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-16T00:26:56.633854Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:26:56.633854Z digest=sha256:645012c1721b99ad2de5bf9ec54e004dbfa33a214d9b86d83c3e8473129aba1c

Observation e0c7fc16-1dec-4a02-a35a-e66eccb66e38 · outbound

This paper cites Suh, and Ernest K.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Suh, and Ernest K

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T00:26:56.798096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-16T00:26:56.638224Z digest=sha256:f26f142445ab3df13e28b2f2ac13e45345e0df545379dbbf9b5a162551435379

Observation 3d0f78d9-6e79-4e9b-9904-65cb12ecb9c0 · outbound

This paper cites H-invariance theory: A complete charac- terization of minimax optimal fixed-point algorithms.Accepted for publication in Mathematical Programming, 2025.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization H-invariance theory: A complete charac- terization of minimax optimal fixed-point algorithms.Accepted for publication in Mathematical Programming, 2025

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T00:26:56.783479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-16T00:26:56.642671Z digest=sha256:8820899b2c4ebf82efce42c624f759a03feb18e0b1d0f2222f620174a96cc9cf

Observation 69cd4e41-be33-453f-ad95-62f74678a47a · outbound

This paper cites Multiplayer federated learning: Reaching equilibrium with less communication.Advances in Neural Information Processing Systems, 2026.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Multiplayer federated learning: Reaching equilibrium with less communication.Advances in Neural Information Processing Systems, 2026

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T00:26:56.768884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-16T00:26:56.647223Z digest=sha256:afc2e5738827ccb21bd849a77ab3dcde41af8035fed838a919edd420b0f24507

Observation 804b15de-0304-417e-947a-4e29e94d109c · outbound

This paper cites an unresolved cited work.

Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization Unresolved cited work

Reference 58

Resolution
unresolved
raw_fallback, observed 2026-08-16T00:26:56.751765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-16T00:26:56.651885Z digest=sha256:9d7e46b3e793ba1b0a208b0a49564b5e7f0c6cf1114027c646137ec0c7f4aaeb

Pith citing papers

No inbound Pith citation observations are available.