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Paper Citation Record · LEDGER

When Are Nonconvex Problems Not Scary?

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:1510.06096.

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

pith.paper-citation-record.v1
1510.06096 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T04:30:37.176306Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-02T11:46:55.219319Z

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 0de661c6-0579-48f0-8cd9-7551f59e1222 · inbound

Second-order methods for provably escaping strict saddle points in composite nonconvex and nonsmooth optimization cites this paper.

Second-order methods for provably escaping strict saddle points in composite nonconvex and nonsmooth optimization When Are Nonconvex Problems Not Scary?

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-06T22:17:58.531907Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:17:58.531907Z digest=sha256:dad12335b337174a2645d2b015a226ca0fcd4c488acb58a7d9c2503a0ec01427

Observation 2eabfebd-5973-445d-86ee-edd73777cffb · inbound

The Optimization Landscape of Carath\'eodory Decomposition of Toeplitz Covariances cites this paper.

The Optimization Landscape of Carath\'eodory Decomposition of Toeplitz Covariances When Are Nonconvex Problems Not Scary?

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-04T00:25:16.469676Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T00:25:16.469676Z digest=sha256:31d153f58c1725678716341c55036a4a7e7b03115d3dfd4820f4d1dd24a13d42

Observation 7a259426-77ac-4960-88f5-fe224b2ecc7c · inbound

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

Convergence of difference inclusions via a diameter criterion When Are Nonconvex Problems Not Scary?

Reference 245

Resolution
metadata mismatch
local_arxiv, observed 2026-05-15T02:23:31.795336Z

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.

source=arxiv_source observed=2026-05-15T02:21:30.228735Z digest=sha256:1f1b99d6b9d5d59dc0530c324bd77e4a357525bbcdaba3645f603e29241653c4

Observation 62a2fc1b-a708-4ff5-9826-387102365028 · inbound

When Both Layers Learn: Training Dynamics of Representing Linear Models via ReLU Networks cites this paper.

When Both Layers Learn: Training Dynamics of Representing Linear Models via ReLU Networks When Are Nonconvex Problems Not Scary?

Reference 44

Resolution
verified exact
local_arxiv, observed 2026-07-02T07:16:44.871423Z

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.

source=pdf_text observed=2026-06-28T07:02:26.496063Z digest=sha256:29c0ded581fdbb5d6c498206892503533773b261627622f675c97f8e1e77fc68

Observation 08051c89-d778-4193-8524-4a9ea061987b · inbound

Principles and Practice of Deep Representation Learning: or a Mathematical Theory of Memory cites this paper.

Principles and Practice of Deep Representation Learning: or a Mathematical Theory of Memory When Are Nonconvex Problems Not Scary?

Reference 98

Resolution
metadata mismatch
local_arxiv, observed 2026-07-02T11:46:55.220778Z

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.

source=pdf_text observed=2026-06-28T03:07:52.730713Z digest=sha256:4457f68f905193fa4aadd8f6d959d6d9c7ba4c1bf3a5acf23f31b796b89c229d

Observation 4bd345a8-b0dd-4ec8-a96f-eeca0633f5a5 · inbound

Stochastic Saddle Avoidance Beyond Unit Excitation and Smoothness: A Pathwise Lyapunov-Perron Framework cites this paper.

Stochastic Saddle Avoidance Beyond Unit Excitation and Smoothness: A Pathwise Lyapunov-Perron Framework When Are Nonconvex Problems Not Scary?

Reference 71

Resolution
unresolved
no resolver link, observed 2026-08-08T04:30:37.176306Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T04:30:37.176306Z digest=sha256:87dd34f51420a61ae62a87a520cab040fefb99e6c570ba004b8ae3daf1a52dfd