Paper Citation Record · LEDGER

An Optimistic Gradient Tracking Method for Distributed Minimax Optimization

As of 21 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2508.21431.

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

pith.paper-citation-record.v1
2508.21431 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T14:28:38.401661Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

32 of 32 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ecb99654-8c76-405a-b01b-9e4f87cad347 · outbound

This paper cites Agnostic federated learning,.

An Optimistic Gradient Tracking Method for Distributed Minimax Optimization Agnostic federated learning,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:28:44.380302Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation dfdb64a4-401b-45da-b8db-89bf88fe795b · outbound

This paper cites Certifying Some Distributional Robustness with Principled Adversarial Training.

An Optimistic Gradient Tracking Method for Distributed Minimax Optimization Certifying Some Distributional Robustness with Principled Adversarial Training

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-05T14:28:34.612349Z

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Observation b297cd9e-418e-487f-b6b0-a699a7fa9ec4 · outbound

This paper cites Game theory for autonomy: From min-max optimization to equilibrium and bounded ratio nality learning,.

An Optimistic Gradient Tracking Method for Distributed Minimax Optimization Game theory for autonomy: From min-max optimization to equilibrium and bounded ratio nality learning,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:28:44.195301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c465dbf3-c95a-4df8-b979-ef0a3e32909e · outbound

This paper cites Generative adversar ial nets,.

An Optimistic Gradient Tracking Method for Distributed Minimax Optimization Generative adversar ial nets,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:28:43.946096Z

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Observation b6b08e21-41b2-4665-aece-98388a1b4c8f · outbound

This paper cites Improved training of wasserstein gans,.

An Optimistic Gradient Tracking Method for Distributed Minimax Optimization Improved training of wasserstein gans,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:28:43.775581Z

Source-reported events for the cited work

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Observation 80545b20-4383-4c86-b66a-50b3bc77e575 · outbound

This paper cites Federated Optimization: Distributed Machine Learning for On-Device Intelligence.

An Optimistic Gradient Tracking Method for Distributed Minimax Optimization Federated Optimization: Distributed Machine Learning for On-Device Intelligence

Reference 6

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Source-reported events for the cited work

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Observation b8870ab7-dbe4-4b0b-9e39-d52bca447eb1 · outbound

This paper cites Distributed optimization for control,.

An Optimistic Gradient Tracking Method for Distributed Minimax Optimization Distributed optimization for control,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:28:43.545629Z

Source-reported events for the cited work

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Observation d452d4a2-a156-4292-a8ad-8f412277cac3 · outbound

This paper cites Can decentralized algorithms outperform centralized algorit hms? a case study for decentralized parallel stochastic gradient desc ent,.

An Optimistic Gradient Tracking Method for Distributed Minimax Optimization Can decentralized algorithms outperform centralized algorit hms? a case study for decentralized parallel stochastic gradient desc ent,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:28:43.390350Z

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Observation f08cdb00-0bff-4e55-868b-7aa94295b276 · outbound

This paper cites A survey of distributed optimiza tion and control algorithms for electric power systems,.

An Optimistic Gradient Tracking Method for Distributed Minimax Optimization A survey of distributed optimiza tion and control algorithms for electric power systems,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:28:43.216003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation beeba10e-f808-4250-95ac-21d4cb64fbcc · outbound

This paper cites A decentralized parallel algorithm for training generati ve adversarial nets,.

An Optimistic Gradient Tracking Method for Distributed Minimax Optimization A decentralized parallel algorithm for training generati ve adversarial nets,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:28:43.020744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2837bf9f-bfbd-4281-8345-f019801595eb · outbound

This paper cites Achieving near -optimal convergence for distributed minimax optimization with ada ptive step- sizes,.

An Optimistic Gradient Tracking Method for Distributed Minimax Optimization Achieving near -optimal convergence for distributed minimax optimization with ada ptive step- sizes,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:28:42.864799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 64aefe79-d2b4-4db6-a6df-ad941bb1ede0 · outbound

This paper cites Numerical methods for ﬁnding saddle points,.

An Optimistic Gradient Tracking Method for Distributed Minimax Optimization Numerical methods for ﬁnding saddle points,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:28:42.644910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 537fa163-ccf0-4467-beb9-55987997a384 · outbound

This paper cites Training GANs with Optimism.

An Optimistic Gradient Tracking Method for Distributed Minimax Optimization Training GANs with Optimism

Reference 13

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Source-reported events for the cited work

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Observation c86437d8-20bb-427d-8164-ff91be62600b · outbound

This paper cites The extragradient method for ﬁndin g saddle points and other problems,.

An Optimistic Gradient Tracking Method for Distributed Minimax Optimization The extragradient method for ﬁndin g saddle points and other problems,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:28:42.484106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ebdb543c-6fc0-4170-ab46-0ae088659bb5 · outbound

This paper cites Distributed average cons ensus with time-varying metropolis weights,.

An Optimistic Gradient Tracking Method for Distributed Minimax Optimization Distributed average cons ensus with time-varying metropolis weights,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:28:42.260455Z

Source-reported events for the cited work

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Observation 27b6edbb-6ec3-4793-a51b-5b5d26d300af · outbound

This paper cites Local stochastic gradient desc ent ascent: Convergence analysis and communication efﬁciency,.

An Optimistic Gradient Tracking Method for Distributed Minimax Optimization Local stochastic gradient desc ent ascent: Convergence analysis and communication efﬁciency,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:28:42.033162Z

Source-reported events for the cited work

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Observation 61c04127-a1a1-4815-863c-a709a11d81b9 · outbound

This paper cites Communication-efﬁcient learning of deep networks from de central- ized data,.

An Optimistic Gradient Tracking Method for Distributed Minimax Optimization Communication-efﬁcient learning of deep networks from de central- ized data,

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 874cd4e1-f44d-4e6a-9efd-665d812c3e88 · outbound

This paper cites An efﬁcient stochastic algor ithm for decentralized nonconvex-strongly-concave minimax op timization,.

An Optimistic Gradient Tracking Method for Distributed Minimax Optimization An efﬁcient stochastic algor ithm for decentralized nonconvex-strongly-concave minimax op timization,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:28:41.503671Z

Source-reported events for the cited work

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Observation 4c82735e-a44b-4446-a98e-afea59f798dc · outbound

This paper cites A uniﬁed an alysis of extra- gradient and optimistic gradient methods for saddle point p roblems: Proximal point approach,.

An Optimistic Gradient Tracking Method for Distributed Minimax Optimization A uniﬁed an alysis of extra- gradient and optimistic gradient methods for saddle point p roblems: Proximal point approach,

Reference 19

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Source-reported events for the cited work

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Observation 5380faea-a4db-489d-8d6e-e35d22ca9bb2 · outbound

This paper cites A tight and uniﬁed analysis of gradient-based methods for a whole sp ectrum of differentiable games,.

An Optimistic Gradient Tracking Method for Distributed Minimax Optimization A tight and uniﬁed analysis of gradient-based methods for a whole sp ectrum of differentiable games,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:28:41.020459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7e48777a-0a44-4d61-89eb-d8ae48cf58fd · outbound

This paper cites Interaction matters: A note on n on-asymptotic local convergence of generative adversarial networks,.

An Optimistic Gradient Tracking Method for Distributed Minimax Optimization Interaction matters: A note on n on-asymptotic local convergence of generative adversarial networks,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:28:40.784807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4beb1588-ab4c-48f2-9d82-d7fb2013c468 · outbound

This paper cites Distributed Saddle-Point Problems: Lower Bounds, Near-Optimal and Robust Algorithms.

An Optimistic Gradient Tracking Method for Distributed Minimax Optimization Distributed Saddle-Point Problems: Lower Bounds, Near-Optimal and Robust Algorithms

Reference 22

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Observation 27442e3e-5045-4855-9a16-4012cfab0f40 · outbound

This paper cites A Decentralized Proximal Point-type Method for Saddle Point Problems.

An Optimistic Gradient Tracking Method for Distributed Minimax Optimization A Decentralized Proximal Point-type Method for Saddle Point Problems

Reference 23

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Source-reported events for the cited work

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Observation 9a9c623a-8b9d-4ba5-bbdf-7639413b5020 · outbound

This paper cites Diffusion stoc hastic optimization for min-max problems,.

An Optimistic Gradient Tracking Method for Distributed Minimax Optimization Diffusion stoc hastic optimization for min-max problems,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:28:40.542236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 71dbe344-f9d7-4ac3-9bee-01d147dde152 · outbound

This paper cites Augmented distribut ed gradient methods for multi-agent optimization under uncoordinated constant stepsizes,.

An Optimistic Gradient Tracking Method for Distributed Minimax Optimization Augmented distribut ed gradient methods for multi-agent optimization under uncoordinated constant stepsizes,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:28:40.320933Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation eb9d75ee-b752-451c-aa96-e6496adc3b5c · outbound

This paper cites Distributed stochastic gradient tracking methods,.

An Optimistic Gradient Tracking Method for Distributed Minimax Optimization Distributed stochastic gradient tracking methods,

Reference 26

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Source-reported events for the cited work

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Observation e9a60b0c-a495-4f4c-9de4-5236c2ea5895 · outbound

This paper cites Multi-agent re in- forcement learning via double averaging primal-dual optim ization,.

An Optimistic Gradient Tracking Method for Distributed Minimax Optimization Multi-agent re in- forcement learning via double averaging primal-dual optim ization,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:28:40.110064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0122bfd8-1e24-4ce8-9c03-237504e99b12 · outbound

This paper cites A decentralized algo rithm for large scale min-max problems,.

An Optimistic Gradient Tracking Method for Distributed Minimax Optimization A decentralized algo rithm for large scale min-max problems,

Reference 28

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 070993b7-678f-4360-8567-d1e5c70138e9 · outbound

This paper cites Distributed opt imization based on gradient tracking revisited: Enhancing convergen ce rate via surrogation,.

An Optimistic Gradient Tracking Method for Distributed Minimax Optimization Distributed opt imization based on gradient tracking revisited: Enhancing convergen ce rate via surrogation,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:28:39.723651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 53b716f3-deff-42a2-bb7b-f3d0ffb7c8f2 · outbound

This paper cites Distributed saddle-point problems under data similarity,.

An Optimistic Gradient Tracking Method for Distributed Minimax Optimization Distributed saddle-point problems under data similarity,

Reference 30

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5a6ad848-a12c-4682-ab23-41dcbc64cce8 · outbound

This paper cites Near-optimal distributed mi nimax optimization under the second-order similarity,.

An Optimistic Gradient Tracking Method for Distributed Minimax Optimization Near-optimal distributed mi nimax optimization under the second-order similarity,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:28:39.262884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7a01fa43-8072-4d50-8769-7fee9b9e5fbd · outbound

This paper cites Accelerated linear iterations f or distributed averaging,.

An Optimistic Gradient Tracking Method for Distributed Minimax Optimization Accelerated linear iterations f or distributed averaging,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T14:28:38.967573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

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