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Paper Citation Record · LEDGER

Derivative-free optimization methods

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1904.11585.

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

pith.paper-citation-record.v1
1904.11585 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T23:34:45.661004Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-24T10:24:20.202217Z

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 14357ce6-a1da-4798-8be3-cea86fbed0a3 · inbound

Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics cites this paper.

Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics Derivative-free optimization methods

Reference 162

Resolution
verified exact
arxiv_id, observed 2026-05-24T10:24:20.205192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-24T10:22:00.419523Z digest=sha256:a53e9efe9b89a7f50d1a91e4b52b0d5ed1eba77dd2b8692c6ff0c6f8d358bfa0

Observation d6709621-4c40-4e8d-84c0-f11b71478454 · inbound

Learning Gradient Flow: Using Equation Discovery to Accelerate Engineering Optimization cites this paper.

Learning Gradient Flow: Using Equation Discovery to Accelerate Engineering Optimization Derivative-free optimization methods

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-02T23:34:45.661004Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T23:34:45.661004Z digest=sha256:33f8745b8d03927abb57f8768de7bcf3a40155d26806738340747c30869a7918