Paper Citation Record · LEDGER

Exact convergence rate of the last iterate in subgradient methods

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2307.11134.

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

pith.paper-citation-record.v1
2307.11134 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 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 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T20:47:10.686709Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T04:37:36.542213Z

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 1bc4c72b-9dd5-4f14-89f4-401b28292ebd · inbound

Accelerating Proximal Gradient Descent via Silver Stepsizes cites this paper.

Accelerating Proximal Gradient Descent via Silver Stepsizes Exact convergence rate of the last iterate in subgradient methods

Reference 1953

Resolution: unresolved; no resolver link, observed 2026-08-11T20:47:10.686709Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e8e6c92-2518-48cc-b0f9-d75f5f17155d · inbound

The Surprising Agreement Between Convex Optimization Theory and Learning-Rate Scheduling for Large Model Training cites this paper.

The Surprising Agreement Between Convex Optimization Theory and Learning-Rate Scheduling for Large Model Training Exact convergence rate of the last iterate in subgradient methods

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-09T21:56:50.806915Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f978e0e-a9be-465e-912e-cdc667bda462 · inbound

Training Deep Learning Models with Norm-Constrained LMOs cites this paper.

Training Deep Learning Models with Norm-Constrained LMOs Exact convergence rate of the last iterate in subgradient methods

Reference 219

Resolution: verified exact; arxiv_id, observed 2026-05-21T21:22:37.168853Z

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.

Observation 24a51a21-9221-4c02-addb-0ecc48c4b659 · inbound

Optimized methods for composite optimization: a reduction perspective cites this paper.

Optimized methods for composite optimization: a reduction perspective Exact convergence rate of the last iterate in subgradient methods

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T21:42:48.196514Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ece026e4-ea3e-4ee6-a703-85f01bb98c32 · inbound

Fast Last-Iterate Convergence of SGD in the Smooth Interpolation Regime cites this paper.

Fast Last-Iterate Convergence of SGD in the Smooth Interpolation Regime Exact convergence rate of the last iterate in subgradient methods

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-06T17:26:08.763414Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21fb4db9-beaf-4fe6-be07-a0986dc2ac29 · inbound

Last-Iterate Complexity of SGD for Convex and Smooth Stochastic Problems cites this paper.

Last-Iterate Complexity of SGD for Convex and Smooth Stochastic Problems Exact convergence rate of the last iterate in subgradient methods

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T16:19:03.602887Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 90df7d6c-f675-451f-a912-b49bbdbd2f20 · inbound

Gradient Descent's Last Iterate is Often (slightly) Suboptimal cites this paper.

Gradient Descent's Last Iterate is Often (slightly) Suboptimal Exact convergence rate of the last iterate in subgradient methods

Reference 29

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T12:30:23.747778Z

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.

Observation cc2c8ec3-bb3b-4c01-9169-2fb7b11d270a · inbound

Mirror Descent Beyond Euclidean Stability: An Exponential Separation in Initialization Sensitivity cites this paper.

Mirror Descent Beyond Euclidean Stability: An Exponential Separation in Initialization Sensitivity Exact convergence rate of the last iterate in subgradient methods

Reference 16

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T04:37:36.543966Z

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.

Observation c7b26d10-213c-471b-86c3-2813946e0fa6 · inbound

Convergence of Continual Learning in Homogeneous Deep Networks cites this paper.

Convergence of Continual Learning in Homogeneous Deep Networks Exact convergence rate of the last iterate in subgradient methods

Reference 27

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T07:14:21.731466Z

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.