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Paper Citation Record · LEDGER

Global Convergence of a Grassmannian Gradient Descent Algorithm for Subspace Estimation

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1506.07405.

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

pith.paper-citation-record.v1
1506.07405 v3

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 2026-08-23T06:30:58.430688+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:14:14.096918Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-15T21:14:14.407796Z

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 4cb84261-0f54-4ebb-8fdc-8b5d59e8d6df · inbound

Global Convergence of Adaptive Sensing for Principal Eigenvector Estimation cites this paper.

Global Convergence of Adaptive Sensing for Principal Eigenvector Estimation Global Convergence of a Grassmannian Gradient Descent Algorithm for Subspace Estimation

Reference 5

Resolution
verified exact
local_arxiv, observed 2026-08-15T21:14:14.411250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T21:14:14.096918Z digest=sha256:ca2a3bd66bb19c01447dff3f75a0bee44f12b9fc8cbe0a718ef5e7eb83d6c204

Observation 9915ddb9-ec98-43f5-852f-aaac70eeae3f · inbound

Geometrically Principled Randomized Optimization for Efficient LLM Training cites this paper.

Geometrically Principled Randomized Optimization for Efficient LLM Training Global Convergence of a Grassmannian Gradient Descent Algorithm for Subspace Estimation

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-04T12:51:26.788589Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T12:51:26.788589Z digest=sha256:8cbda67f153ff1d0c5132764c15235298a146d1602a4726839ca1544dbf100b3