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Paper Citation Record · LEDGER

Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:1603.03236.

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

pith.paper-citation-record.v1
1603.03236 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T11:42:21.367473Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

108
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation b76a961c-8abe-4723-b5e9-a85dc41cb592 · inbound

A Flag Decomposition for Hierarchical Datasets cites this paper.

A Flag Decomposition for Hierarchical Datasets Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-08T11:42:21.367473Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T11:42:21.367473Z digest=sha256:6960559b82a1a5396463736d6fb226b3114643bdf08709dbbbf4c058ed8a00c6

Observation 1b70da86-c0dd-4eb3-aaa6-f5791836ec1c · inbound

Diagonal Isometric Form for Tensor Network States in Two Dimensions cites this paper.

Diagonal Isometric Form for Tensor Network States in Two Dimensions Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation

Reference 53

Resolution
verified exact
arxiv_id, observed 2026-05-19T05:07:04.656692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-19T05:05:25.922170Z digest=sha256:a374875050cfc5e41fa5a4881152ab45a87cb0c3dae323d3f9eb94289092842e

Observation d90670ac-b15b-4fdb-891f-dcb630d11be9 · inbound

Holographic Representation of One-Dimensional Many-Body Quantum States via Isometric Tensor Networks cites this paper.

Holographic Representation of One-Dimensional Many-Body Quantum States via Isometric Tensor Networks Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation

Reference 81

Resolution
verified exact
arxiv_id, observed 2026-05-16T22:48:38.251645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-16T22:44:27.492539Z digest=sha256:bbb56b4ed22d1e2cae846a84ee8458a2978f31a51e5befcbb1228612753af200

Observation 409bd12c-856c-4ac5-8043-67333fdcb830 · inbound

Foundations of Riemannian Geometry for Riemannian Optimization: A Monograph with Detailed Derivations cites this paper.

Foundations of Riemannian Geometry for Riemannian Optimization: A Monograph with Detailed Derivations Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation

Reference 15

Resolution
metadata mismatch
arxiv_id, observed 2026-05-09T06:30:44.519947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-08T18:23:05.938250Z digest=sha256:f2e910b768237ee396d191cfe833c03ab5935047161ebe1198a3d1f2856b115b

Observation 5cd60168-ad04-474e-a0b3-b1533d9c215a · inbound

Nonconvex optimization methods for ground states in disordered continuous-spin models cites this paper.

Nonconvex optimization methods for ground states in disordered continuous-spin models Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation

Reference 14

Resolution
metadata mismatch
arxiv_id, observed 2026-05-08T17:18:40.764014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-08T17:16:13.206936Z digest=sha256:3c9b11463ec349ccaf2e8b21d0f1a24f5ab99a402a702e1557483e50925e93b5

Observation d90ac435-cb49-4150-be1e-484e46755abc · inbound

Nonconvex optimization methods for ground states in disordered continuous-spin models cites this paper.

Nonconvex optimization methods for ground states in disordered continuous-spin models Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-02T14:49:17.777270Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T14:49:17.777270Z digest=sha256:ce6ddd7ff111e94cde6049a98145a1f7333dfdcbb2167d01a1d0f4c09ec31836

Observation 0b7bb690-9ac9-438b-9163-2dc2e3425891 · inbound

A modified Riemannian Levenberg-Marquardt Algorithm for robust or constraint optimization on manifolds cites this paper.

A modified Riemannian Levenberg-Marquardt Algorithm for robust or constraint optimization on manifolds Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation

Reference 48

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T12:09:49.401814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-06-26T07:12:14.485377Z digest=sha256:c4a0ac731b0ab434546804f11c033bb6821a80139e4b3635e9d3f94bbb7862c0