Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
On the Differentiability of the Solution to Convex Optimization Problems
As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1804.05098.
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 2026-08-14T06:32:32.682623+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-10T23:21:41.446558Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-18T05:50:57.120256Z
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 5396aa6b-78c5-4639-b1fd-8f7eba9980b0 · inbound
Differentiable Convex Optimization Layers in Neural Architectures: Foundations and Perspectives cites this paper.
Differentiable Convex Optimization Layers in Neural Architectures: Foundations and Perspectives On the Differentiability of the Solution to Convex Optimization Problems
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T23:21:41.446558Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1f7e4448-5f04-4e4b-8848-01a3a76e8268 · inbound
A flexible and differentiable coil proxy for stellarator equilibrium optimization cites this paper.
A flexible and differentiable coil proxy for stellarator equilibrium optimization On the Differentiability of the Solution to Convex Optimization Problems
Reference 41
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T05:50:57.122575Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation b2008216-1bc0-4ac9-bdc0-e485d118ad71 · inbound
Efficient and provably convergent end-to-end training of deep neural networks with linear constraints cites this paper.
Efficient and provably convergent end-to-end training of deep neural networks with linear constraints On the Differentiability of the Solution to Convex Optimization Problems
Reference 6
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T01:57:05.986651Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.