Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Nonconvex Matrix Factorization is Geodesically Convex: Global Landscape Analysis for Fixed-rank Matrix Optimization From a Riemannian Perspective
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2209.15130.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T22:55:23.937293Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-23T04:47:33.504933Z
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 4287f720-b81a-40cb-86fe-8e2c46342194 · inbound
A primal-dual interior point trust region method for second-order stationary points of Riemannian inequality-constrained optimization problems cites this paper.
A primal-dual interior point trust region method for second-order stationary points of Riemannian inequality-constrained optimization problems Nonconvex Matrix Factorization is Geodesically Convex: Global Landscape Analysis for Fixed-rank Matrix Optimization From a Riemannian Perspective
Reference 39
- Resolution
- verified exact
- arxiv_id, observed 2026-07-21T00:19:48.031947Z
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.
Observation df1f0fbf-0a08-4d04-a2af-0cf45036ca60 · inbound
A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing cites this paper.
A High-Dimensional Statistical Theory for Convex and Nonconvex Matrix Sensing Nonconvex Matrix Factorization is Geodesically Convex: Global Landscape Analysis for Fixed-rank Matrix Optimization From a Riemannian Perspective
Reference 48
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T22:55:23.937293Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.