Paper Citation Record · LEDGER

A Newton-MR algorithm with complexity guarantees for nonconvex smooth unconstrained optimization

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2208.07095.

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

pith.paper-citation-record.v1
2208.07095 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T00:04:19.620060Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T03:19:29.055060Z

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 20cc988b-fc03-46fa-be98-80c9f03c0064 · inbound

A simple and practical adaptive trust-region method cites this paper.

A simple and practical adaptive trust-region method A Newton-MR algorithm with complexity guarantees for nonconvex smooth unconstrained optimization

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T00:04:19.620060Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a249761-49e7-4163-9b03-5fe38e8a97fb · inbound

First-ish Order Methods: Hessian-aware Scalings of Gradient Descent cites this paper.

First-ish Order Methods: Hessian-aware Scalings of Gradient Descent A Newton-MR algorithm with complexity guarantees for nonconvex smooth unconstrained optimization

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-09T04:11:15.390246Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4dce74f-96a0-4799-aba9-420207725749 · inbound

Active-set Newton-MR methods for nonconvex optimization problems with bound constraints cites this paper.

Active-set Newton-MR methods for nonconvex optimization problems with bound constraints A Newton-MR algorithm with complexity guarantees for nonconvex smooth unconstrained optimization

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-05T14:51:14.749690Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 85aeebc0-7018-4902-97f7-7cbefc6493f7 · inbound

Achieving Material Robustness via Symmetric Stress Finite Element Discretizations cites this paper.

Achieving Material Robustness via Symmetric Stress Finite Element Discretizations A Newton-MR algorithm with complexity guarantees for nonconvex smooth unconstrained optimization

Reference 69

Resolution: verified exact; arxiv_id, observed 2026-05-21T03:19:29.057386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.