Paper Citation Record · LEDGER

Stochastic Subgradient Methods with Guaranteed Global Stability in Nonsmooth Nonconvex Optimization

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2307.10053.

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

pith.paper-citation-record.v1
2307.10053 v5

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:10:55.390854Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T08:57:47.420793Z

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 0f862a80-8a16-4d01-82d7-a0d843490679 · inbound

Optimization Hyper-parameter Laws for Large Language Models cites this paper.

Optimization Hyper-parameter Laws for Large Language Models Stochastic Subgradient Methods with Guaranteed Global Stability in Nonsmooth Nonconvex Optimization

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-08-11T01:19:49.772953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 409d5b73-5735-41b6-b555-24d8cf6ce3d2 · inbound

Mathematical analysis of the gradients in deep learning cites this paper.

Mathematical analysis of the gradients in deep learning Stochastic Subgradient Methods with Guaranteed Global Stability in Nonsmooth Nonconvex Optimization

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-10T14:10:55.390854Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f173c0e-a527-4a2c-a930-0e641ca2462a · inbound

On exploration of an interior mirror descent flow for stochastic nonconvex constrained problem cites this paper.

On exploration of an interior mirror descent flow for stochastic nonconvex constrained problem Stochastic Subgradient Methods with Guaranteed Global Stability in Nonsmooth Nonconvex Optimization

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-06T15:48:30.584749Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06bfafd7-6683-4f07-9154-ecd0ee717e5a · inbound

StoSignSGD: Unbiased Structural Stochasticity Fixes SignSGD for Training Large Language Models cites this paper.

StoSignSGD: Unbiased Structural Stochasticity Fixes SignSGD for Training Large Language Models Stochastic Subgradient Methods with Guaranteed Global Stability in Nonsmooth Nonconvex Optimization

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-08-11T01:19:49.772953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 28ad14f0-78e7-4d52-b9a2-7830e8e69ee3 · inbound

When and Why SignSGD Outperforms SGD: A Theoretical Study Based on $\ell_1$-norm Lower Bounds cites this paper.

When and Why SignSGD Outperforms SGD: A Theoretical Study Based on $\ell_1$-norm Lower Bounds Stochastic Subgradient Methods with Guaranteed Global Stability in Nonsmooth Nonconvex Optimization

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-08-11T01:19:49.772953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3b0b4661-68a5-4e6d-894d-77d19a46a43d · inbound

Convergence of difference inclusions via a diameter criterion cites this paper.

Convergence of difference inclusions via a diameter criterion Stochastic Subgradient Methods with Guaranteed Global Stability in Nonsmooth Nonconvex Optimization

Reference 114

Resolution: verified exact; arxiv_id, observed 2026-08-11T01:19:49.772953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f98de7f7-612f-4a99-b0c1-f9a145a192af · inbound

Decentralized Stochastic Subgradient-type Methods with Communication Compression for Nonsmooth Nonconvex Optimization cites this paper.

Decentralized Stochastic Subgradient-type Methods with Communication Compression for Nonsmooth Nonconvex Optimization Stochastic Subgradient Methods with Guaranteed Global Stability in Nonsmooth Nonconvex Optimization

Reference 67

Resolution: verified exact; arxiv_id, observed 2026-08-11T01:19:49.772953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.