Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
A decentralized proximal-gradient method with network independent step-sizes and separated convergence rates
As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1704.07807.
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 2026-08-20T06:33:59.587034+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T15:38:59.057934Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T16:57:09.569378Z
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 3b9e30e5-36d3-4fd3-a569-8c5d1e76e5f4 · inbound
SPARKLE: A Unified Single-Loop Primal-Dual Framework for Decentralized Bilevel Optimization cites this paper.
SPARKLE: A Unified Single-Loop Primal-Dual Framework for Decentralized Bilevel Optimization A decentralized proximal-gradient method with network independent step-sizes and separated convergence rates
Reference 30
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T15:38:59.057934Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5e822fdf-29fb-4be5-9998-3ccbe758ff9d · inbound
Accelerated Decentralized Stochastic Gradient Descent for Strongly Convex Optimization cites this paper.
Accelerated Decentralized Stochastic Gradient Descent for Strongly Convex Optimization A decentralized proximal-gradient method with network independent step-sizes and separated convergence rates
Reference 14
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T16:57:09.570666Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.