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Paper Citation Record · LEDGER

A simple uniformly optimal method without line search for convex optimization

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2310.10082.

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

pith.paper-citation-record.v1
2310.10082 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 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 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T18:49:52.499732Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T07:42:42.957198Z

Reference resolution

0 of 0 outbound references displayed

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  • verified fuzzy0
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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 a7f3055d-392f-440a-91b0-0721d190d985 · inbound

Uniformly Optimal and Parameter-free First-order Methods for Convex and Function-constrained Optimization cites this paper.

Uniformly Optimal and Parameter-free First-order Methods for Convex and Function-constrained Optimization A simple uniformly optimal method without line search for convex optimization

Reference 25

Resolution
verified exact
arxiv_id, observed 2026-05-23T07:42:42.961285Z

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=2026-05-23T07:42:25.733968Z digest=sha256:1e5048d245b01dbbea1558cffc8dfe25365e538ed7b53b2d4b347a7c0ba4858c

Observation 17b0771c-7440-4330-8c40-5bce85efbd3c · inbound

Projected gradient methods for nonconvex and stochastic smooth optimization: new complexities and auto-conditioned stepsizes cites this paper.

Projected gradient methods for nonconvex and stochastic smooth optimization: new complexities and auto-conditioned stepsizes A simple uniformly optimal method without line search for convex optimization

Reference 26

Resolution
verified exact
arxiv_id, observed 2026-05-23T06:25:28.073687Z

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=2026-05-23T06:23:51.617737Z digest=sha256:4c3043370b5fc0052fcce45cee718cea1627ab61f74c253d0c76a79cdca76279

Observation dc27d49f-6d70-4de7-8556-123ac7ef1ae7 · inbound

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization cites this paper.

A Parameter-Free and Near-Optimal Zeroth-Order Algorithm for Stochastic Convex Optimization A simple uniformly optimal method without line search for convex optimization

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-08T18:49:52.499732Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T18:49:52.499732Z digest=sha256:413407e458b911738d4d5a1e3859503a512878072b6cf5f30192ddb21f8f0c08

Observation 5f05fe23-b761-4118-9756-402baffd1343 · inbound

Gradient Methods with Online Scaling Part I. Theoretical Foundations cites this paper.

Gradient Methods with Online Scaling Part I. Theoretical Foundations A simple uniformly optimal method without line search for convex optimization

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-07T13:10:12.329475Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:10:12.329475Z digest=sha256:3e1753fc700f4a7a778027dbd6e77a6f591f9c4c52dd5516ba5dab6e0bedda88

Observation b5c63562-f318-4e1e-afc2-981cd77cfa44 · inbound

Doubly Smoothed Optimistic Gradients: A Universal Approach for Smooth Minimax Problems cites this paper.

Doubly Smoothed Optimistic Gradients: A Universal Approach for Smooth Minimax Problems A simple uniformly optimal method without line search for convex optimization

Reference 2025

Resolution
unresolved
no resolver link, observed 2026-08-07T05:47:48.443882Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:47:48.443882Z digest=sha256:c03026eb9347bb162bf7a062dd18801a7a6d1b9a5a16b779e7c39151ae7897bc

Observation 1a7f0fb7-aca2-4c02-842a-88d3e1c121da · inbound

Online Learning-guided Learning Rate Adaptation via Gradient Alignment cites this paper.

Online Learning-guided Learning Rate Adaptation via Gradient Alignment A simple uniformly optimal method without line search for convex optimization

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-07T05:24:28.611408Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:24:28.611408Z digest=sha256:20298dbf1a4ccd29c84915d3ecdbe76620cdaaca209392f2290500ad643b9965

Observation 6bfe14ab-f60b-4826-bfc9-c353829fd351 · inbound

Nesterov Finds GRAAL: Optimal and Adaptive Gradient Method for Convex Optimization cites this paper.

Nesterov Finds GRAAL: Optimal and Adaptive Gradient Method for Convex Optimization A simple uniformly optimal method without line search for convex optimization

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-06T17:59:07.249015Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:59:07.249015Z digest=sha256:dcac6a97f2a8f100ba1f0454c0e80a1b108f4dc669755adce4a46569012b5195

Observation 97fff874-5793-4c70-bd6c-5441de448984 · inbound

A Proximal Stochastic Gradient Method with Adaptive Step Size and Variance Reduction for Convex Composite Optimization cites this paper.

A Proximal Stochastic Gradient Method with Adaptive Step Size and Variance Reduction for Convex Composite Optimization A simple uniformly optimal method without line search for convex optimization

Reference 25

Resolution
verified exact
arxiv_id, observed 2026-05-18T17:21:40.018420Z

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=2026-05-18T17:20:09.257391Z digest=sha256:6b87d39524ecf9bfd49d61e113bb6e5e573b78894f41d9312615e9d7814a2e45