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Paper Citation Record · LEDGER

Provable Complexity Improvement of AdaGrad over SGD: Upper and Lower Bounds in Stochastic Non-Convex Optimization

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2406.04592.

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

pith.paper-citation-record.v1
2406.04592 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:45:07.761309Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T20:58:15.734279Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation d7452adf-7dba-458a-a72c-1b33a7ef44c3 · inbound

SGD with Adaptive Preconditioning: Unified Analysis and Momentum Acceleration cites this paper.

SGD with Adaptive Preconditioning: Unified Analysis and Momentum Acceleration Provable Complexity Improvement of AdaGrad over SGD: Upper and Lower Bounds in Stochastic Non-Convex Optimization

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-06T21:45:07.761309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T21:45:07.761309Z digest=sha256:99f582a563c2072ef3413d4751e610937a267fa61f1fbfea96526f85ee757aae

Observation cb405e5a-dfea-42ed-b37a-658bd3181d3d · inbound

Non-Euclidean SGD for Structured Optimization: Unified Analysis and Improved Rates cites this paper.

Non-Euclidean SGD for Structured Optimization: Unified Analysis and Improved Rates Provable Complexity Improvement of AdaGrad over SGD: Upper and Lower Bounds in Stochastic Non-Convex Optimization

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-03T22:20:05.547507Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T22:20:05.547507Z digest=sha256:ec7dee3feb3221d0cf2c412037881bd7d0589ada465e3e2c782b7f2f65191e67

Observation 1fbab019-480c-4942-9966-a31379e52df6 · inbound

Optimal Projection-Free Adaptive SGD for Matrix Optimization cites this paper.

Optimal Projection-Free Adaptive SGD for Matrix Optimization Provable Complexity Improvement of AdaGrad over SGD: Upper and Lower Bounds in Stochastic Non-Convex Optimization

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-05-13T20:58:15.735847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-13T20:56:41.466669Z digest=sha256:e0ef1ec3776ee29bc1d7612bc490f36675f32700f23ccd93e32a62de11d3b22e

Observation 143d654f-2f15-4a05-99a1-b8169616974c · inbound

A unified convergence theory for adaptive first-order methods in the nonconvex case, including AdaNorm, full and diagonal AdaGrad, Shampoo and Muo cites this paper.

A unified convergence theory for adaptive first-order methods in the nonconvex case, including AdaNorm, full and diagonal AdaGrad, Shampoo and Muo Provable Complexity Improvement of AdaGrad over SGD: Upper and Lower Bounds in Stochastic Non-Convex Optimization

Reference 27

Resolution
verified exact
arxiv_id, observed 2026-05-10T07:26:59.838893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-10T07:19:59.335184Z digest=sha256:f3004524be0b4f4da34574ad01569587dfc16c07d45343f8f4da54bcd3a488c4

Observation 7ee54a71-d2e5-47f8-929b-96ac55cfdafa · inbound

Optimizer-Model Consistency: Full Finetuning with the Same Optimizer as Pretraining Forgets Less cites this paper.

Optimizer-Model Consistency: Full Finetuning with the Same Optimizer as Pretraining Forgets Less Provable Complexity Improvement of AdaGrad over SGD: Upper and Lower Bounds in Stochastic Non-Convex Optimization

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-05-11T19:26:08.606390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-08T12:00:49.127471Z digest=sha256:05889dcf866b530bd16f92df21b5d90d980e3c1c22332cf4d8a95af16ca764ed